ISAST Transactions on

ISAST Transactions on

No. 1, Vol. 2, 2008 (ISSN 1797-0989)

Communications and Networking Regular Papers Julian Meng, and Xin Ding: Pulse-Tone Jamming Suppression for a CDMA Rake Receiver………………………………………………………….....1 Yuhua Chen and Wenjing Tang: Critical Region Analysis of Optical Burst Switching Routers with Input Fiber Delay Lines………..…………………….11 Ildoko Moussa, Jean C. Chedjou, Kyandoghere Kyamakya, and Van Doc Nguyen: Dynamics of a Secure Communication Module based on a Chaotic Synchronization……………………………………..14 Danyan Luo, Decheng Zuo, and Xiaozong Yang: IPv6 Address Assignment in Wireless Sensor Networks…………………………………………...………………….......24 H. Ramazanali, J. Olsson, J. Lönn, A. Huynh, Q. Je, and S. Gong: Evaluation of ZigBee Networking in a Campus Environment……………………………………………………..………30 Abhijeer Shirgurkar and M. I. Hayee: Transoceanic Networks for 44Gb/s WDM Transmission using π/2 Alternate-Phase RZ Modulation Format……………………………………………………………………………………………………………………....36 Shamila Makki and Subbarao Wunnava: Next Generation Networks and Code Mobility…………………………………………………………………...………..42 P. Priakanth and Dr. P. Thangaraj: A Traffic Aware Adaptive Scheduling MAC Protocol for Energy Efficiency and Fairness in Mobile Ad hoc Networks……………………………………………………………………………………………………………………48 Venkatesh Ramaswamy, Leticia Cuéllar , Stephan Eidenbenz, and Nicolas Hengartner: Analysis of Packet Dropping Behavior of Sending Rate Estimate Based Queue Management Schemes with Markovian Sources………………………………………………………………………………………........................................…..56 Marium Jalal Chaudhry, Mariam Nosheen, Farhaat Saleemi, Sadaf Tariq, and Fatima Jalal Chaudhry: Secure Wireless Communication Using Advance Encryption Standard.………………………...…..…………………….59 Danpu Liu, Jianjun Hao, and Guangxin Yue: A Best Relay Selection Scheme for Opportunistic Relaying…………………………………………………..……….......66 Peter Orosz, Janos Sztrik, Chesoong Kim: Dynamics and Congestion Control of Alternative TCP Variants on Asymmetric Lines……………………….………….71 P. Harini: An Efficient DAD Scheme for Hierarchical Mobile IPv6 Handoff……………………………………………...………...75 Caroline Fontaine, Claude Delpha, Pierre Duhamel, Abdellatif Benjelloun Touimi, Michel Milhau, Alain Le Guyader, Christophe Giraud, Patrice Martin, David Azemard, and Jean-Bernard Fischer: An end-to-end security architecture for multimedia content distribution on mobile phones………………………………81 Tadamichi Suzuki, Jungo Ito, Kazushi Nakano, Kohji Higuchi, and Tetsuya Miki: New Insight for Robust IP Traffic Control Based on Network Behavior Knowledge…………………………………….92 Yanyan Song, Decheng Zuo, Xiaozong Yang, Gang Cui: ERAD: an Energy-based Radius self-Adaptive Distributed Topology Control Algorithm in Wireless Sensor Networks……………………………………………………………………………….....102 Hong Wu, Yingxin Zhao, Lijun Ge, Zhijiang Zhou, and Weiyi Tan: An Efficient Joint Algorithm of Synchronization, Channel Estimation and the Low Peak-to-Average Power ratio of OFDM System………………………………………………………………………………….…………………………107

Greeting from ISAST Dear Reader, You have the second ISAST transactions on Communications and Networking in your hands. It consists of seventeen original contributed scientific articles on various fields of advanced communications and networking technology. Every original article has gone through peer-review process. ISAST – International Society for Advanced Science and Technology – was founded in 2006 for the purpose of promoting science and technology, mainly electronics, signal processing, communications, networking, intelligent systems, computer science, scientific computing, and software engineering, as well as the areas near to those, not forgetting emerging technologies and applications. To show, how large the diversity of communications and networking is today, we shortly summarize the contents of this Transactions Journal: In their paper Julian Meng and Xin Ding study the effects of pulse-tone jamming suppression for a CDMA rake receiver. Yuhua Chen and Wenjin Tang present theoretical analysis of an OBS router architecture that supports optimal scheduling, deflection routing as well as interoperability of heterogeneous OBS networks. Ildoko Moussa, Jean C. Chedjou, Kyandoghere Kyamakya, and Van Doc Nguyen investigate the dynamics of a secure communication module made-up of three coupled non-identical oscillators in a Master-Slave-Auxiliary configuration and show the possibility of exploiting some characteristic features of such a module in secure communication. Their study proposes a chaotic secure communication scheme. Danyan Luo, Decheng Zuo, and Xiaozong Yang have written a paper on IPv6 address assignment in wireless sensor networks. H. Ramazanali, J. Olsson, J. Lönn, A. Huynh, Q. Je, and S. Gong evaluate ZigBee networking in a campus environment. Using their own-developed FigBee modules they perform evaluations in both outdoor and indoor campus environments. In their paper Abhijeer Shirgurkar, and M. I. Hayee explore the feasibility of 44 Gb/s WDM transmission over a 6000 km transoceanic link using π/2-Alternate-Phase RZ modulation format. Shamila Makki and Subbarao Wunnava describe the integration of the next generation networks with mobile codes. In their paper P. Priakanth and Dr. P. Thangaraj propose a Traffic Aware Scheduling MAC (TA-MAC) protocol for mobile adhoc networks. Venkatesh Ramaswamy, Leticia Cuéllar, Stephan Eidenbenz, and Nicolas Hengartner have written an analysis of packet dropping behavior of sending rate estimate based queue management schemes with Markovian sources. Marium Jalal Chaudhry, Mariam Nosheen, Farhaat Saleemi, Sadaf Tariq, and Fatima Jalal Chaudhry present an architecture to implement the advance encryption standard (AES) for wireless data transmission to enable a proper security mechanism for preventing wireless data from intruders and security attacks by defining a simple infrastructure and processing steps that can be shared between many applications using common technology tools Danpu Liu, Jianjun Hao, and Guangxin Yue study a best relay selection scheme for opportunistic relaying. Peter Orosz, Janos Sztrik, Chesoong Kim present a letter that shows the effect of asymmetric physical lines on the dynamics of novel TCP variants based on their empirical measurement results. P. Harini introduces an efficient DAD scheme for hierarchical mobile IPv6 handoff. Caroline Fontaine, Claude Delpha, Pierre Duhamel, Abdellatif Benjelloun Touimi, Michel Milhau, Alain Le Guyader, Christophe Giraud, Patrice Martin, David Azemard, and Jean-Bernard Fischer present the results of the French collaborative SDMO (Secured Diffusion of music on mObiles) project. The project aims at combining traditional security tools with digital rights management and watermarking, to reinforce the overall security of an audio content distribution service for mobile phones. Tadamichi Suzuki, Jungo Ito, Kazushi Nakano, Kohji Higuchi, and Tetsuya Miki got a new insight for robust IP traffic control. It is based on knowledge mainly focusing on network behavior from two aspects: "backward" and "forward" in a time transition, which is applicable to network operation as well as design and verification.Yanyan Song, Decheng Zuo, Xiaozong Yang, Gang Cui have written a paper about ERAD: an Energy-based Radius self-Adaptive Distributed Topology Control Algorithm in Wireless Sensor Networks. Hong Wu, Yingxin Zhao, Lijun Ge, Zhijiang Zhou, and Weiyi Tan propose an efficient joint algorithm for OFDM system. We are happy to see how many manuscripts we have obtained with ambitious and impressive ideas. We hope that you will inform of the existence of our Society to your colleagues to all over the academic, engineering and industrial world. Best Regards, Professor Timo Hämäläinen, University of Jyväskylä, FINLAND, Editor-in-Chief Professor Jyrki Joutsensalo, University of Jyväskylä, FINLAND, Vice Editor-in-Chief Tuomas Elijärvi, Assistant Editor

ISAST Transactions on Communications and Networking, No.1 Vol. 2, 2008 J. Meng et al.: Pulse-Tone Jamming Suppression for a CDMA Rake Receiver

1

Pulse-Tone Jamming Suppression for a CDMA Rake Receiver J. Meng, Member, IEEE, and X. Ding

Abstract--Narrowband jamming systems, such as those used in military applications and inadvertent situations with non-licensed civilian RF deployments, can present a unique challenge for direct sequence spread-spectrum (DSSS) systems. Previous studies have proposed various adaptive filtering, spectral estimation, blind source separation (BSS) and other techniques to reduce the effects of narrowband jamming on the DSSS system. An attractive feature of adaptive filtering based on linear prediction is its implementation simplicity and has been proposed in numerous variants to limit the effects of narrowband interference in DSSS systems. However, we extend this work to assess the effects of a pulse-tone jammer (PTJ) on a more complex code division multiple access (CDMA) multi-user system utilizing spatial diversity, i.e. the Rake receiver and the use of more realistic fading channel model. PTJ is a difficult jamming scenario given its on/off time-varying nature. An extensive SNR analysis is provided for a CDMA receiver in the presence of PTJ signals and a solution based on a fast adapting filtering technique is suggested. The resulting theoretical bit error rate (BER) performance is confirmed using a simulated CDMA system in the presence of a fading channel and PTJ signals. A discussion of previously published results utilizing blind source separation (BSS) via Independent Component Analysis (ICA) under similar jamming conditions is presented. Index Terms--Wireless Communications, Code Division Multiple Access, Interference Suppression, Rake Receiver.

I. INTRODUCTION

L

arge narrowband jamming signals can present difficulties with direct sequence spread spectrum (DSSS) systems given their (by design) low power output. Although the decorrelation process of the DSSS system does provide some natural jamming rejection capability, this can be augmented by anti-jamming techniques prior to detection. Narrowband jamming can present itself unintentionally in civilian systems due inter-operator usage and is becoming more prominent given today’s limited radio spectrum availability. Intentional jamming will most likely arise in military situations and antijamming techniques are imperative to maintain the availability of communication links vital for strategic and logistical planning. Numerous narrowband jamming rejection techniques for DSSS systems have been previously proposed and can be grouped into popular categories such as: adaptive filtering This study was supported by research grants from the Natural Sciences and Engineering Research Council of Canada (NSERC). The authors are with the University of New Brunswick, Fredericton, N.B., Canada, E3B 5A3. Email: [email protected] and [email protected].

(linear and non-linear), time-frequency analysis, subspace processing, transform-domain excision and antenna beamforming1. The selection of an anti-jamming technique depends on the jamming characteristics and the operational scenario of the wireless system. For example, antenna beam-forming is a good solution given available resources and that antenna directivity can be increased for particular users. This solution, however, cannot be applied to situations where the communication system utilizes quasi omni-directional antennas such as those used in mobile cellular base stations. Blind techniques attempt to limit the directional requirements of the array by assuming the jammer and desired signals are statistically independent [8][9]. Other block-processing methods, such as those based on time-frequency analysis, transform-domain and subspace processing, can also perform satisfactorily but with the added complexity in estimating key parameters of a time-varying jammer [10][11]. With a pulsetone jammer (PTJ), a sinusoidal signal is modulated using a unipolar square-wave making it difficult to estimate and excise with precision. While the PTJ is not necessarily catergorized as a smart jammer per se, the transient nature of this type of jammer presents a difficult situation to block interference processors given its “off/on” character. More recently, a promising blind source separation (BSS) based technique [6][7] using independent component analysis (ICA) for pulsejammer suppression has been proposed [3]-[5]. Although their results are promising, the increased Rake receiver complexity is an issue given that the application of ICA jammer suppression in conditions of low jammer power can introduce errors in detection process. Alternatively, the sample-by-sample processing of the adaptive filter is desirable due to its ability to quickly adapt to significant changes in the predicted signal. Linear prediction filters can be formulated in an adaptive configuration to operate in a non-stationary environment and the narrowband signal can be modeled as a stochastic autoregressive (AR) process. If this assumption holds, the linear prediction filter can be used to predict and remove future values of the narrowband signal, thereby leaving the desired DSSS signal [1][12][13]. Linear prediction has a major advantage in terms of implementation simplicity and adaptability to changing jamming characteristics such as frequency and duration. One of the more popular variants of the linear prediction filter is 1 Excellent summary papers on narrowband interference rejection techniques for spread spectrum systems are given in [1][2].


2

based on recursive-least-squares (RLS) which is similar in execution to the popular Kalman filter often used in state estimation and tracking [12]. RLS techniques have been found to have better convergence properties and improved steady state tracking over the popular least-mean squares (LMS) methods but at a cost of higher computational complexity [12], [13]. In this research, we will present results utilizing linear prediction filters applied to the PTJ problem in combination with the Rake receiver. These results can be directly compared with those recently published in the ICA suppression work. The extensive body of work on narrowband jamming suppression has two drawbacks when considering modern communication systems operating in difficult environments. First, it has mainly focused on jamming models formulated using a constant carrier frequency. In the case of the PTJ, previously proposed linear prediction filtering techniques will have difficulties managing residual errors from this type of pulsed type jammer and the degradation will depend on the convergence time of the adaptive process and the magnitude of the PTJ. Thus, the focus of this study is to investigate PTJ degradation on a code division multiple access (CDMA) basestation receiver operating in a fading channel and assesses any performance gains with the utilization of pre-processing jamming suppression filters. This study presents a detector signal-to-noise ratio (SNR) performance of a CDMA system using the ubiquitous Rake receiver with and without jamming suppression filtering. The overall communication system model is presented in Section 2. Section 3 provides the SNR and theoretical bit error rate (BER) analysis of the PTJ degradation. Finally, Section 4 provides the BER results determined using a simulation model of a base station IS-95 CDMA system operating in a multipath environment with a PTJ present. Given the unconventional aspect of the filtering residuals in the noise term, these results are used to confirm the anticipated analytical results. II. CDMA AND PULSE-TONE JAMMER MODEL For DSSS modulation, a PN sequence is used to spread the user’s signal spectrum over a wider bandwidth to allow for the advantages described in the previous section to be realized. For CDMA, the DSSS system is augmented for several users communicating over the same channel using orthogonal codes such as the Walsh code. The PN/Walsh sequence for a given user is N c −1

m i (t ) =

∑c

(t − lTc )

il w

s (t ) =

∑ b m (t − iT ) i

i

s

(2)

i =0

where bi is the information symbol to be transmitted by a particular user and T s ( = N c Tc ) is the symbol duration. In practice, s(t) is pulse shaped and processed by a M-ary modulator prior to RF transmission. Assuming the modulation/de-modulation process to be transparent, the equivalent baseband output signal through a fading channel can be represented as +∞

y(t ) =

∫ h(t,τ )s(t − τ )dτ

(3)

−∞

where h(t,τ ) is the channel impulse response with t and τ representing time and delay variables of the channel. The characteristics of h(t,τ ) for various fading channels have been extensively studied and the details of which will not be covered here. The output from the channel is corrupted by additive white Gaussian noise (AWGN), n(t ), and by PTJ, given as j (t ). Correspondingly, the equivalent low pass receive signal at the CDMA base station is simply r (t ) = y (t ) + n(t ) + j (t ), (4) and the undesired PTJ can be modeled as j (t ) = Ap(t ) cos(2π ( f m )t ), (5) where f m and p(t ) are the pulse frequency and unipolar binary square-wave modulation function, respectively. The amplitude A is varied to alter the jammer-to-signal ratio (JSR). Also, the pulse rate of p(t ), ρ PJT , will be varied in order to investigate CDMA SNR and BER performance under various jammer scenarios. Two considerations for the PTJ signal model in (5) should be noted: firstly, when the modulating function and frequency are held constant, this will be denoted as the single tone case and, secondly, the frequency of the jammer will change for each “on” cycle of the jammer. The latter makes for a more difficult tracking case. Figure 1(a) illustrates a typical sequence for a PTJ (real part only).

(1)

l =0

where N c is the number of chips per message bit, Tc is the chip duration, cil ( ∈ {1,−1}) is the lth chip of the Walsh/PN sequence, and w(t) is a unity rectangular pulse with a duration equal to the input symbol duration. N c is also referred to as the processing gain of the spread-spectrum. The CDMA signal is given by

(a) Pulse-Tone Jammer


3

+∞

Y (t ) =

∫ y(t − α) f

M

(α )dα

(9)

0 +∞+∞

=

∫ ∫ h(t − α ,τ )s(t − α − τ )f

M

(α )dτdα

0 −∞

and the unwanted correlation output for the AWGN and jamming components are given as +∞ (10) N (t ) = n(t − α )f M (α )dα

∫ 0

and +∞

J (t ) =

∫ j (t − α )f

M

(α )dα ,

(11)

0

respectively. Assuming Y (t) , N (t) and J (t) are zero-mean and independent complex sample processes, the output instantaneous SNR can be stated as 2 (12) E Y t

(b) RLSL Suppression Filter Output

{( ) } . E{J (t ) }+ E{N (t ) } i

SNR =

2

i

2

i

In terms of correlation functions, (12) can be written as2 RY (t1 , t1) . SNR = RN (t1, t1) + RJ (t1, t1)

(13)

In the following sections, the result in (13) will be used to provide insight to the anti-jamming capabilities of the adaptive linear prediction filter for narrowband jamming suppression. III. ANTI-JAMMING SIGNAL-TO-NOISE RATIO ANALYSIS

A. Anti-Jamming Performance for a Pulsed Tone Jammer Using the results given in the Appendix A and substituting (32), (33) and (34) into (13) yields the no filter detector SNR, −1 (14)  4 N 0 + 0.5A 2 N user − 1  + SNRnofilter =   ,  8K Ph βLr Es N c  where K ph , β , E s , N 0 and Lr are the normalized values

(c) MRLSL Suppression Filter Output Figure 1 Example of Pulse-Tone Jammer and Linear Prediction Filter Results, SNR = 30 dB, Forgetting Factor λ = 0.95

At the CDMA base station, the correlation operation of each branch in the Rake receiver is equivalent to a matched filter convolution operation [14], that is, +∞

v(t ) =

∫ r(t − α) f

M

(α )dα

0 +∞

=

∫ (y(t − α ) + n(t − α) + q(t − α ))f

M

(α )dα

(6)

0

where the matched filter impulse response, f M (t ) , is f M (t ) = m * (T s − t ), 0 < t < Ts . Substituting (4) into (6) yields, v(t ) = Y (t ) + N (t ) + J (t ) where the desired signal term is

(7) (8)

for the integrated power delay profile (PDP) of the fading channel, spreading waveform coefficient, signal energy, power spectral density of the AWGN, and the number of Rake correlators, respectively. In theory, when orthogonal codes are used for the CDMA system, other users will not affect individual detector performance. However, multipath fading can distort the received signal in a manner such that the orthogonality assumption is no longer valid and, thus, the second term in (14) is required to limit the CDMA performance when affected by multi-user interference [15]. The limitation is based on the number of concurrent channel users, N user , and the spreading gain of the CDMA system. This result also assumes equal power of reception from all users such that the system does not suffer from a near-far power control problem. Although (14) indicates proportional detector improvement with an increasing number of Rake 2

For this study, we are mainly interested in the case where t2 − t1 = 0 .


4

correlators, previous work [16] has shown there are diminishing returns with a continual addition of correlators. For this study, the JSR, defined as the ratio of jammer power ( ∝ A 2 ) to the signal power ( ∝ E s ), is varied to assess the Rake detector performance. In this respect, it is obvious from (14) that detector performance improves with decreasing JSR. For linear prediction, the estimation of the required filter taps is rooted in previous work by Burg, Levinson-Durbin, [12], [13] and further extended to a method of least-squares with a lattice filter configuration, the recursive least-squares lattice (RLSL) filter3. Due to the pseudo-random characteristic of the PN/Walsh sequence, the desired CDMA signal can be considered noise-like with respect to the narrowband jammer. Thus, the output prediction error represents any uncorrelated noise-like signal present in the receiver signal, including the desired CDMA signal. More importantly, the narrowband jammer is seen as a correlated signal to be removed by the linear prediction filter. As stated previously, an elegant form of the linear prediction filter utilizes a lattice structure for its implementation4. The number of lattice stages equals the prediction order and the reflection coefficients can be equivalently translated to the tap values of a classic transversal linear prediction filter in finite-impulse-response (FIR) form [12], [13]. If a narrowband jamming suppression filter is first applied to the received signal, the input to the PN correlator of the Rake receiver can be given as +∞ (15) f (t ) = hn (α )r (t − α )dα ,

∫

−∞

where hn (α ) represents the impulse response of the RLSL suppression filter. The function, f (t ) , is the error term of the linear prediction process and should, in theory, contain the desired CDMA and AWGN signals less the narrowband jammer. The matched front-end output of the Rake receiver is now +∞ (16) v n (t ) = f (t − α ) f M (α )dα

∫

+∞+∞

N n (t ) =

∫∫

hn (α 1 )n(t − α 2 − α 1 ) f M (α 2 )dα 1 dα 2

0 −∞

and +∞ +∞

J n (t ) =

∫ ∫ h (α )j(t − α n

1

and (22) u(t 0 ) = 0 respectively, where θ is a random phase offset. Using this assumption and given the definitions in (21) and (22), the output of the linear prediction filter at time t 0 can be approximated by examining the response of the three components given in (4) individually. That is,

+∞

1

n

+ 2

− α1) f M (α 2 )dα1dα 2

(17)

0 −∞ +∞+∞+∞

=

∫ ∫ ∫h

n

(α 1 )h(t − α 2 − α 1 , τ )

0 − ∞− ∞

∫

(23)

+∞

h n (α )y (t 0 − α )dα +

0

∫h

n

(α )n(t 0

where the forward error residual at time t 0 is given by e(t 0 ) = d (t 0 ) − u(t 0 ) . Generalizing the output over time yields

For the noise components given in (4), the correlation output for the AWGN and jamming components are given as

f (t ) ≈ d (t0 ) cos(2πf newt )e +∞

+ The RLSL filter, and a variant, was solely used as the pre-processing jamming suppression filter. 4 For the sake of completeness, the RLSL filter algorithm is given in Appendix B.

− α )dα

0

x s(t − τ − α 2 − α 1 ) f M (α 2 )dτdα 1 dα 2 .

3

(19)

B. Anti-Jamming Performance for a Pulse-Tone Jammer Excessive residual energy in the prediction filter output is due to the starting and stopping of the jammer pulse and the inability of the adaptive process to converge instantaneously to these types of events. Previous work has shown that the residual error for a frequency shift-keyed interferer is a function of input JSR, frequency separation of the new and old frequency and the filter convergence time [17]. Extending this analysis, the PN correlator output SNR can be derived for the PTJ case. Denoting t0 as the sample time where the pulse starts, the desired and predicted jammer signal can be stated as follows (21) d (t 0 ) = A cos(2πf new t 0 + θ )

f (t0 ) ≈ e(t0 )

+∞ +∞

∫ ∫ h (α )y(t − α

− α1)f M (α 2 )dα1dα 2 ,

0 −∞

0

Yn (t ) =

2

respectively. Using the results (37), (38) and (41) in (13) gives the detector SNR for the case of a single tone jammer with RLSL suppression as −1 (20)  2 N 0 K hn + E s + N 0 N user − 1  + SNR RLSL =   . N c   4 K Ph K hn β L R E s Thus, the resulting detector SNR for this case is independent of the jammer power and indicates that a single tone jammer will have limited degradation on CDMA systems utilizing linear prediction filtering.

= Yn (t ) + N n (t ) + J n (t ) , where

(18)

∫ 0

−

t Tλ

(24)

+∞

h n (α )y (t − α )dα +

∫h

n

(α )n(t − α )dα

0

where T λ is the convergence speed of the adaptive filter. Eq.


5

(24) shows a prediction error that contains an additional −

residual term, d (t 0 ) cos(2πf new t )e

t Tλ

, that is oscillatory in nature and whose duration is dependent on the convergence speed of the adaptive prediction process. Figure 1 (b) shows the magnitude of the complex output from a standard RLSL filter using the signal from Figure 1 (a) as the input. Isolating the residual term in (24), the respective correlation of the Rake receiver output for this particular term is (25) R residual (t1 , t 2 )

A well-known trade-off in adaptive systems is the relationship between convergence speed and tracking performance. Clearly from (27), the SNR performance of the suppression filtering approach can be improved by reducing the convergence time (i.e. reducing the forgetting factor) of the adaptive filter but this occurs at the expense of the filter’s tracking performance.

 0.333 A 2 Ω residual E s + 2 N 0 K hn + E s + N 0   Γ = 4 K Ph K hn βL R E s  

(t −α )  − 1 1  E  cos(2πf new (t1 − α 1 ))e Tλ  0 

+ ∞+ ∞

= d (t 0 )

2

∫∫ 0

x cos(2πf new (t 2 − α 2 ))e

−

(t 2 −α 2 )  Tλ

   

+

x f M* (α 1 ) f M (α 2 )dα 1 dα 2 +∞+∞

= d (t 0 )

2

∫ ∫Ω

residual δ

(t 2 − t1 + α 1 − α 2 ) ,

0 0

x f M* (α 1 ) f M (α 2 )dα 1 dα 2 where δ is a Dirac delta function. Eq. (25) can then be reduced to (26) R residual (t1 , t 2 ) = d (t 0 )

2

Ω residual R s (t 2 − t1 )

2

= d (t 0 ) Ω residual Es , t 2 − t1 = 0, where Ω residual is the normalized integrated energy of the residual error due to frequency changes in the PTJ and is significantly less than unity due to the decaying exponential envelope of the residual term and the pulsing nature of the PTJ. Assuming the cosine term in (22) is a random variable varying from 0 to +/-1, then

d (t o )

2

= 0.333A 2 . Eq. (26)

indicates that the magnitude and duration of the residual energy term depends on two factors: the jammer power and the convergence speed of the adaptive suppression filter. The R residual (t1 , t 2 ) term represents an additional noise term in the SNR and has been found to degrade Rake receiver delay estimates [18]. Thus, using (26) to modify (20) leads to (27), the estimation of the SNR with prediction filtering in the PTJ case5. The result in (27) indicates the degradation effect of the PTJ when compared in a single tone case given in (20). The pulse-rate ratio Γ =

(27)

SNR RLSL

ρ PTJ is added as a scaling factor to the ρCDMA

residual term in order to compensate for various cases of PTJ pulse rate.

N user − 1   Nc 

−1

Although, previous work has derived the optimal forgetting factor that balances convergence time and steady state frequency tracking of a signal with discrete frequency changes [19], it is of interest to investigate the properties of the previously proposed Memoryless RLSL (MRLSL) filter [17]6. This filter limits the duration of residual error by resetting certain filter memory parameters after detecting changes in the jammer frequency. This variation of the RLSL filter converges nearly instantaneously (i.e. T λ ≈ 0) to a new frequency while maintaining a high forgetting factor for the purposes of good tracking performance. The effectiveness of the MRLSL filter is shown in Figure 1(c) where a direct comparison with Figure 1(b) should be made. As a result, the application of the MRLSL filter in the presence of PTJ yields an equivalent SNR to the single tone jammer SNR as given in (20) i.e. SNR MRLSL = SNR RLSL . Using this result, in combination with the results in (14) and (27), Figure 2 (a) illustrates the theoretical SNR results for the no-filter, RLSL and MRLSL cases using two pulse rates. This is an important result since it illustrates the expected outcome: interference suppression is required in high JSR regimes and standard linear prediction algorithms are not completely effective in suppressing this type of jammer. Also, the PTJ is rendered benign by the MRLSL filter and the resulting SNR is independent of jammer power and pulsing rate. For Gaussian noise-based communication systems, the well-known relationship between the theoretical BER and SNR is (28)  SNR   1  . BER = 1 − erfc  2  2    Although the assumption of Gaussian noise statistics does not apply for SNRs containing impulsive-like residual error terms, (28) can be used to approximate the system BER using the results given in Figure 2(a). As expected, the theoretical BER

5

Although the dectector SNR will not be the same when the pulse goes “off”, the “on” residual will be considered the dominant residual. Figure 1(b) illustrates this clearly.

6

The MRLSL algorithm is restated in Appendix B.


6

results, shown Figure 2(b), indicate the bounding properties of the MRLSL filtering technique in high JSR conditions. From the theoretical SNR and BER results, the application of a jammer suppression filter has a negative effect (i.e. in low JSR regimes) when compared to the no filter case. In reality, however, the MRLSL and RLSL filters will be less detrimental than shown since the response of the actual prediction filter will spectrally flatten, altering the input signal less and less, as the jammer power drops. The simulation results provided in the following section will clearly show that this is in fact the case.

Assuming a constant CDMA signal power, the jammer power is varied to determine the desired JSR and the PTJ residual energy, Ω residual , was computed based on a filter convergence time (empirically found to be T λ = 30 samples for the selected forgetting factor). The results shown in Figure 2 illustrate that the standard RLSL suppression filter can offer some Rake detector SNR/BER improvement over the no-filter case but the significance of which is dependent on the JSR and the pulse rate of the PTJ. To confirm the theoretical BER results given in the previous section, a basic down link channel utilizing the IS-95 CDMA standard and the Rake receiver was simulated. The down-link consists of a fixed base station and 5 users operating over the same channel allocation. The number of correlators for the Rake receiver was limited to L R = 3 , the same as for the SNR analysis. For the multipath channel, a frequency selective Rayleigh fading channel with nominal delay spreads and fading characteristics was simulated. To investigate the jammer suppression performance for PTJ signals, the BER for a CDMA system was assessed for various JSR regimes and frequency hop parameters using single order prediction filters. Similar to the SNR analysis, the spreading gain was set to 64 and the RLSL forgetting factor, λ , was set to 0.95. Monte Carlo simulations of 5x105 bits were run for each JSR case. Figure 3 illustrates the performance gains and bounding properties of the MRLSL filtering approach for a simulated CDMA system.

Figure 2 (a) Theoretical SNR and (b) BER for Two Jammer Frequency Hop Ratios ( β = 0.6666 , K Ph = 1.0 ,

L R = 3 , N 0 = 0.01 , E S = 1.0 , N c = 64 ) IV. CDMA SIMULATION RESULTS For this study, the Γ factor was set to 0.5 and 1 with the higher number representing a less difficult jamming scenario. For evaluation purposes, the coefficient K hn was normalized to unity. Also, a nominal value for β = 0.6666 [16], K Ph = 1.0,

LR = 3 N 0 = 0.01 and E S = 1.0 were assumed for this exercise. The spreading gain, N c , was set to 64. The forgetting factor of the adaptive filter was set at λ = 0.98 which is found to have a relatively fast convergence time without compromising the filter’s tracking performance.

Figure 3 Simulated CDMA BER Performance for Two Jammer Frequency Hop Ratios ( λ = 0.95 , L R = 3 ,

N 0 = 0.01 , E S = 1.0 , N c = 64 ) In high JSR conditions, the BER results found using MRLSL filtering is superior to the previously published ICA results [3] evaluated under similar conditions. In fact, the ICA Rake receiver does not achieve a BER of 10-2 in high JSR conditions. Also, with the ICA Rake receiver, post-estimation of the jammer power is required to determine whether jammer suppression is required. This is not the case with the MRLSL technique since the application of linear prediction basedfiltering does not degrade system performance in low JSR


7

conditions7. Figure 3 also illustrates the sensitivity of the standard RLSL filter to changes in Γ , which is expected since the residual rate-of-occurrence is dependent on this factor. In limited JSR regimes, the MRLSL filter performs slightly worse than the standard RLSL filter due to difficulties detecting the presence of a jammer in intermediate JSR levels. False detections are expected when transitioning from high to low JSR levels. In any event, these results indicate the application of pre-interference filtering is warranted at all JSR levels, even at very low ISR levels where filtering is seen to be noninvasive. With respect to the no filter case, a close inspection shows that the BER for this case is solely dependent on the JSR. This confirms the result given in (14). Finally, the trend of the RLSL filtering BER results are in close agreement, with the exception of the lower JSR regime, to theoretical expectations. In summary, the MRLSL filter has three important merits for PTJ jammer suppression: (1) the MRLSL BER performance is independent of the pulse rate and (2) the RLSL filter performance is bounded by the no-filter and MRLSL case in high JSR regimes. These considerations make the MRLSL an obvious choice for pre-filtering of a CDMA signal when it is corrupted by a PTJ signal and operates in a transparent manner when low or non-existent levels of PTJ are present and (3) the MRLSL filter/Rake receiver performs better in terms of BER than the ICA/Rake receiver in high JSR conditions. V. CONCLUSIONS It is well known that DSSS communications systems can be used in harsh channel fading environments and also have some inherent narrowband jamming resistance built in due to its energy spreading nature. Previous studies have investigated the capabilities of DSSS systems in narrowband jamming environments and have proposed the introduction of preprocessing suppression filters to improve the overall system performance. This work, however, only provided the SNR analysis for a basic narrowband jammer type, channel model and single user system. Given the presence of PTJ systems, the impact of narrowband signals of this nature on more complex multiuser CDMA systems in the presence of fading channels should be considered. A significant contribution of this study was assessing the effect of PTJ on CDMA system performance. This study has investigated, through a detailed SNR analysis, the capability of CDMA systems augmented with linear prediction filters to limit the degradation of PTJ, inadvertent or otherwise. It was found that the CDMA BER performance using conventional RLSL filtering is bounded by the no-filter and MRLSL case in high JSR regimes and that the MRLSL filter offers good resistance to the “on/off” nature of a PTJ. The BER performance achieved by the MRLSL filter/Rake receiver in high JSR conditions was found to be 102 , lower than the recently proposed ICA/Rake receiver. Also, it was found that the application of linear prediction filters is 7 As previously discussed, this differs from the theoretical result shown in Figure 2 (b).

non-invasive at all JSR values. This also differs from the ICA/Rake receiver, which requires a preliminary step to assess the jammer power. Finally, this research has confirmed the relationship between the derived analytical SNR results, theoretical BER and the BER tests utilizing a simulated CDMA system. Given the nature of the error residuals due to PTJ, the latter is required such that the proposed analytical BER results can be confirmed using a simulated CDMA system. VI. APPENDIX A

{

}

The correlation function, RY (t1,t 2 ) = E Y * (t1)Y (t 2 ) , and

(9) yields RY (t1 , t 2 )

(29)

+∞+∞+∞ +∞

=

∫ ∫ ∫ ∫R

h

(t1 − α 1 , t 2 − α 2 ; τ 1 , τ 2 )

0 0 − ∞− ∞

x s * (t 1 − α 1 − τ 1 ) f M* (α 1 )s(t 2 − α 2 − τ 2 ) x s * (t1 − α 1 − τ 1 ) f M* (α 1 )s (t 2 − α 2 − τ 2 ) x f M (α 2 )dτ 1 dτ 2 dα 1 dα 2 , where

{

}

Rh (t1 − α1 ,t 2 − α 2 ;τ 1 ,τ 2 ) = E h * (t1 − α1 ,τ 1)h (t 2 − α 2 ,τ 2 ) . Simplifying (30) for t2 − t1
To ] = 1 − Pr[ f S ≤ To ] = e −uc (1− ρc )To .

(7)

In order to correlate the control path load to the data path load, we define the control path overloading ratio r to be: r=

ρc . ρd

(8)

Therefore, the extrinsic burst loss probability can also be expressed in terms of overloading ratio r: − u (1− r ρd )To . (9) Pext = e c The intrinsic loss probability Pint can be obtained using Erlang B Formula: Pint =

( λd

μd )h h!

h ( λ μ )k ∑ d k!d k =0

=

( h ρd ) h

h! h ( hρd )k ∑ k =0 k !

.

(10)

Burst length can have a general distribution because of the insensitivity of Erlang B Formula to service time distribution. We can calculate the overall burst loss probability as Poverall = Pext (1 − Pint ) + Pint (1 − Pext ) + Pext Pint (11) = Pext + Pint − Pext Pint , where Pext and Pint are obtained in (9) and (10),

respectively. In order to quantify the severity of control path overloading, we define the overloading impact factor F: Pint

, where Pext > 0 and Pint > 0.

(12)

8

r= 2

4

32 channels 1.75

0

1.5 1.25

1

-4 -8 -12 -16

IV. NUMERICAL RESULTS

0.2

In this section, we evaluate the numerical values of the analytical model derived in Sect. 3. The analysis is also validated by computer simulations. Fig. 2 and Fig. 3 show the overall burst loss probability Poverall for OBS routers with 32 and 64 WDM channels per link,

1.00E+00

r = 2 1.75 1.5

Poverall

1.00E-01

1.25

1

1.00E-02

Intrinsic discard

1.00E-03 1.00E-04

Fig. 4.

1

Overloading impact factor (32 channels)

30 25

Severely Overloaded

64 channels

20

r= 2 1.75 1.5

15 10 5

1.25

0

1

-5 -10 0.2

0.4

0.6

Data Path Offered Load

Fig. 5.

0.8

ρd

1

Overloading impact factor (64 channels)

greater than the intrinsic burst loss probability. The plots show that an OBS router with 64 channels per link goes into the severely overloaded region much more quickly than an OBS router with 32 channels per link. V. CONCLUSION While the OBS router architecture with input FDLs can provide a variety of benefits such as optical scheduling, deflection routing and interoperability, engineering of such system presents a challenge due to the often competing design goals (e.g., burst loss performance, latency, system cost). In this letter, we have analyzed such system under stress conditions to quantitatively understand the interplay of the control and data path loads on system performance. The analysis has revealed that systems with more WDM channel counts are more vulnerable to stress conditions than systems with smaller channel counts. With the analytical model derived in this letter, we have also identified a global minimum in the overloading impact factor, which cannot be easily obtained by computer simulations. REFERENCES

1.00E-06 0.2

0.4

0.6

0.8


1

ρd

Fig. 2. Overall burst loss probability (32 channels)

[1]

[2] [3]

1.00E+00

r = 2 1.75 1.5

1.25

1

1.00E-01

Poverall

0.8

32 channels

1.00E-05

[4]

1.00E-02

[5]

Intrinsic discard

1.00E-03 1.00E-04

64 channels

1.00E-05

[6]

1.00E-06 0.2

0.4

0.6


Fig. 3.

0.6

35

Impact Factor F

respectively. The results from the analysis are the solid lines with no markers. The markers shown in the figures are the results from the simulations. The curves from our analysis match the simulation results closely. As we can see, the overall burst loss probability increases dramatically as the overloading ratio r increases from 1 to 2. Fig. 4 and Fig. 5 show the overloading impact factors F for 32 and 64 channels, respectively. The solid lines are the results from the analysis, and the markers are the results from simulations. The analytical model is able to obtain results in regions where it is infeasible for the simulations to obtain due to the extremely low burst loss probability ( < 10−9 ). The analytical model also reveals a global minimum for each r with respect to data path offered load, which can not be easily discovered by computer simulations. Note that when F = 0 , the intrinsic burst loss probability is equal to the extrinsic burst loss probability. We draw a thick line at F = 0 , and divide the plot into two regions: 1) the lightly overloaded region ( F < 0 ) where the extrinsic burst loss probability is smaller than the intrinsic burst loss probability; and 2) the severely overloaded region ( F > 0 ) where the extrinsic burst loss probability is

0.4

Data Path Offered Load ρ d

Lightly Overloaded

Pext

12

Impact Factor F

F = ln

Severely Lightly Overloaded Overloaded

ISAST Transactions on Communications and Networking, No.1 Vol. 2, 2008 Yuhua Chen and Wenjing Tang: Critical Region Analysis of Optical Burst Switching Routers with Input Fiber Delay Lines

13

0.8

1

ρd

Overall burst loss probability (64 channels)

[7]

C. Qiao, M. Yoo, “Optical burst switching (OBS) - A new paradigm for an optical internet,” Journal of High Speed Networks, vol. 8, no. 1, 1999, pp. 69-84. J. S. Turner, “Terabit burst switching,” Journal of High Speed Networks, vol. 8, no. 1, 1999, pp 3-16. Yang Chen, C. Qiao, X. Yu, "Optical burst switching: a new area in optical networking research," IEEE Network, May/June, 2004. N. Barakat, T. E. Darcie, "Control-plane congestion in OBS networks," Workshop on Optical Burst/Packet Switching (WOBS), Oct. 2006. Y. Chen, J. S. Turner, P.-F. Mo, “Optimal burst scheduling in optical burst switched networks,” IEEE/OSA Journal of Lightwave Technology, vol. 25, no. 8, Aug. 2007, pp. 1883-1894. J. Li. C. Qiao, J. Xu, and D. Xu, “Maximizing throughput for optical burst switching networks,” IEEE/ACM Transactions on Networking, vol. 15, no. 5, Oct. 2007, pp.1163 – 1176. A. Leon-Garcia, Probability and random processes for Electrical Engineering, 2nd ed. Prentice Hall, 1993, pp. 145-148.

ISAST Transactions on Communications and Networking, No.1 Vol. 2, 2008 Ildoko Moussa et al.: Dynamics of a Secure Communication Module based on Chaotic Synchronization

14

Dynamics of a Secure Communication Module based on Chaotic Synchronization Ildoko Moussa , Doctoral School of Electronics and Information Technology (UDETIME), University of Dschang, Dschang, Cameroon e-mail: [email protected]

*

J. C. Chedjou , K. Kyamakya Institute for Smart-Systems Technologies, University of Klagenfurt, Klagenfurt, Austria e-mails: [email protected], [email protected]

Abstract—

We investigate the dynamics of a secure communication module made-up of three coupled nonidentical oscillators in a Master-Slave-Auxiliary configuration and show the possibility of exploiting some characteristic features of such a module in secure communication. A chaotic secure communication scheme is proposed. Various bifurcation diagrams associated to the graph of their corresponding largest one dimensional (1D) numerical Lyapunov exponent are obtained showing routes to chaos. Synchronization phenomena are observed in terms of the coupling parameters. One of the most important contributions of this work is to provide a set of reliable analytical expressions to explain and predict the various types of synchronization expected. These expressions are of great importance for design engineers as they can be used to explain and predict the behavior of the system under investigation.

Keywords- Secure Communication; Coupled Oscillators; Bifurcation; Chaos; Synchronization; Analog Simulation I.

INTRODUCTION

Over the past few years, synchronization has attracted massive research attention [1-8] due to its multiple technological and fundamental applications. Indeed, synchronization problems can be found in a wide variety of phenomena. In biology for instance, fireflies provide one of the most spectacular examples of synchronization in nature [9-12], which are being exploited for time synchronization in wireless ad hoc networks. Still in communication engineering, chaotic synchronization is widely considered and exploited in chaotic secure communication processes [13-18]. In this paper, we study synchronization phenomena in a master-slave-auxiliary configuration made-up of three non-identical coupled oscillators of the Rössler type and show the possibility of exploiting chaotic synchronized phenomena exhibited by the coupled oscillators in chaotic secure communication. Here, the principle consists of the transmission of an information signal containing a message, using chaotic signal as a broadband carrier. For details on the use of chaotic modulation in communication, one should have a look at Reference [22] and the special

Van Duc Nguyen Faculty of Electronics and Telecommunications, C9-P403 Hanoi, University of Technology e-mail: [email protected]

issue of the IEEE Transaction on Circuits and Systems, Vol.47, No.12 (of December 2000) where a good overview is provided. The synchronization process is achieved and aids to recover the information at the receiver [19-20]. The interest devoted to such a configuration (with the Rössler oscillators) is due to the chaotic synchronized waveforms the coupled systems can exhibit both at low and high frequencies [19-20]. Nowadays several types of synchronization representing different degrees of correlation between the coupled systems have been identified: Full synchronization, generalized synchronization, phase synchronization, and lag synchronization just to name a few [19]. Despite the very large literature to be found concerning these phenomena their mathematical analysis in a master-slave-auxiliary configuration is a challenging problem since to date, the literature does not provide clear and in-depth analytical expression to explain and predict the occurrence of the phenomena. A first attempt to explain and predict the appearance of synchronization is found in Refs. [5, 19-21]. Ref. [21] considers synchronization phenomena in a system of two coupled self-sustained chaotic oscillators. It is demonstrated that with the increase of coupling strength the system first undergoes the transition to phase synchronization. With a further increase of coupling, a new synchronous regime is observed, where the states of two oscillators are nearly identical, but one system lags in time to the other. This regime is described by the authors as a state with correlated amplitudes and a constant phase shift. The transitions observed are traced in the Lyapunov spectrum. In Ref. [5] Rulkov focused on the images of synchronized chaos. He uses experimental observations of chaotic oscillations in coupled nonlinear circuits to discuss a few forms of cooperative behavior that are related to the regimes of synchronized chaos. Along his paper, the author outlines the collection of examples that illustrate the state of the art of chaos synchronization. In Refs. [19-20] is considered the master-slave-auxiliary configuration made up of three coupled non-identical oscillators of the Rössler type. We show the occurrence of various types of synchronization in the regular and chaotic states of the coupled systems. The analog simulation of the model describing the behavior of the coupled systems was carried out and some numerical and experimental phase portraits

15


were obtained to show the appearance of both chaotic and regular synchronization. To date, no theoretical study has been carried out to explain and predict the occurrence of the synchronization phenomena that were observed experimentally in such coupled systems. Moreover, chaos was observed experimentally and no theoretical analysis is available to confirm its existence and its possible synchronization in the master-slave-auxiliary system under investigation. Our aim in this paper is to contribute to the general understanding of the behavior of this system and complete the results obtained so far by (a) carrying out a systematic and methodic analysis to explain and predict the occurrence of different types of synchronization; and (b) pointing out some of the unknown behavior exhibited by the system under investigation. The paper is organized as follows. In section 2 we review some basic concepts of securing communication using chaotic synchronization. We discuss two different methods of the coding process. Section 3 is concerned by the analytical study of some types of synchronization namely phase synchronization, lag synchronization and complete synchronization. Some sample results are presented to illustrate the concepts and also to confirm the analytical results obtained. Various bifurcation diagrams associated with their corresponding graphs of the largest 1D numerical Lyapunov exponents are obtained to show the complex and striking phenomena exhibited by the coupled systems and to define routes to chaos. These plots are used to confirm the existence of chaos in the coupled systems. Section 4 deals with conclusions and proposals for further works. II. ANALOG CODING AND DECODING SCHEMES OF

Fig. 1. Scheme of the chaotic coding in the source (base-band).

The second scheme (Fig. 2) integrates a HF chaotic carrier. The advantage of this scheme is the coding during modulation which provides very simple securing communication architecture. Nevertheless, this scheme is very difficult and constringent to be realized since it does integrate HF analog components. In addition to this, the message is not well secured if the transmission is via amplitude modulation as the message is located in the envelope of the modulated signal. Such a coding scheme may be exploited only for frequency modulation.

Fig. 2. Scheme for chaotic coding during modulation.

INFORMATION SIGNAL

A signal is said to be analog if the signal characteristics representing the information can take at any time any value of a continuous interval of time. Two basic schemes of secure communications exist. The first is based on the coding process before modulation. The second is the coding during modulation. In the first scheme (Fig. 1), the coding is realized in base-band before modulation. This permits a shift in frequency domain for an electromagnetic (EM) transmission. The advantage of such a scheme is the convenience to realize chaotic systems both at transmitter and receiver, since it is easy to find low frequency analog components. For this scheme amplitude modulation is appropriate since the information signal is mixed in a chaotic carrier (results in cheap receivers). Moreover, the carrier used in the modulation process can be a high frequency regular oscillator to facilitate the demodulation at the receiver. Here the information is recovered by exploiting the deterministic character of the chaotic phenomenon.

The coding schemes considered here integrate both coding in base-band and coding with chaotic carrier. The LNA network is a selective circuit. Its bandwidth should be enough for baseband frequencies (message to be secured). The limits and advantages of each of the schemes show that their exploitation depends upon the characteristics of the information to be secured and also on the type of modulation (AM, FM, or PM) used. In each of these schemes, the message can be recovered if the chaotic signal at the transmitter and that at the receiver are synchronized. This justifies the interest devoted to the next section since a mathematical analysis is of high importance to predict and control the occurrence of synchronization phenomena in the coupled systems. III. ANALYTICAL STUDY OF SYNCHRONIZATION Synchronization is considered as the ability of coupled self-sustained oscillators with different frequencies to switch their behavior from the regime of independent oscillations (characterized by beats) to the regime of cooperative oscillations (stable periodic or unstable oscillations), as the strength of the coupling is monitored. We use an auxiliary system (a third oscillator of the Rössler type), which is considered as an ideal predictor that is able to indicate the current state of the response system by processing the driving system [19] to achieve


16

the synchronization process. A similar approach was carried out by Rulkov [5]. Here, simplified cases of nonidentical synchronized chaotic oscillations were observed in directionally coupled circuits with different parameters. This work generalizes the definition of chaos synchronization as the ability to predict the current state of the response system from the chaotic data measured from the driving system. The general goal of this section is to carry out the analytical study to predict and control the occurrence of synchronization.

A.

dr1, 2,3

dt + ε 1, 2,3 r2,1,1 cos φ 2,1,1 + r3,3, 2 cos φ3,3, 2

[

(

(

)

)

dφ1, 2,3 dt −

(

+ Slave x2 , y2 , z2

(

dz1, 2,3

x&1, 2,3 = −ω1, 2,3 y1, 2,3 − z1, 2,3 +

ε 1, 2,3 (x2,1,1 + x3,3, 2 − x1, 2,3 ) (1a)

(

where

ωi

)

(1c)

are the natural frequencies of the oscillators,

εi

The dotes stand for time derivatives. This system can be assimilated to a sequential system (that is a system which output depends on both inputs and previous state of the output). To analyze phase synchronization we express the following relationships between solutions of Eqs. (1) and their fundamental parameters (that is the amplitudes r1, 2 , 3

φ1, 2, 3

φ1, 2, 3 ):

⎛ y1, 2, 3 ⎞ ⎟ = arctg ⎜ ⎟ ⎜x ⎝ 1, 2, 3 ⎠

(

r1, 2, 3 = x12, 2, 3 + y12, 2, 3

(2a)

)

1 2

dt

]

(

= f 1, 2,3 + z1, 2,3 r1, 2,3 cos φ1, 2,3 − U 1, 2,3

(3b)

)

(3c)

The theory of phase synchronization can be developed by expressing the detuning Δ i =1, 2 , 3 between the natural

(1b) frequencies of the oscillators as follows: ω1, 2 = ω0 ± Δ1

are the elastic coupling coefficients (couplings through solutions) and ai , f i , and U i are the system parameters.

and phases

cos φ 2,1, 2 + r3,3,1 cos φ3,3,1

)

+ Auxiliary x3 , y3 , z3 system under investigation is

z&1, 2,3 = f1, 2,3 + z1, 2,3 x1, 2,3 − U 1, 2,3

[(r

2 ,1, 2

r1, 2,3

− r1, 2,3 cos φ1, 2,3 ε 1, 2,3 − z1, 2,3

modeled by the following set of equations:

(3a )

= ω1, 2,3 + a1, 2,3 cos φ1, 2,3 sin φ1, 2,3

sin φ1, 2,3

)

y&1, 2,3 = ω1, 2,3 x1, 2,3 + a1, 2,3 y1, 2,3

]

)

− r1, 2,3 cos φ1, 2,3 − z1, 2,3 cos φ1, 2,3

Phase synchronization The Master x1 , y1 , z1

= r1, 2,3 a1, 2,3 sin 2 φ1, 2,3

(2b)

Eqs. (2) represent the polar form of the solutions in Eqs. (1). These equations (Eqs. (1) and (2)) can be used to establish the following differential system describing the evolution of the fundamental parameters of solutions:

(4a)

ω 2, 3 = ω 0 + Δ 2

(4b)

ω1, 3 = ω0 ± Δ 3

(4c)

and defining the total phases of master, slave and auxiliary systems by the relations

φ1, 2, 3 = ω 0 t + θ 1, 2, 3

(4d)

To analyze phase synchronization from Eqs. (3) we use the averaging over rotations of the total phases [21]. We restrict our analysis to the case of weak variation of the amplitudes of the stationary states. Thus, the following set of first order differential equations can be obtained to describe the temporal evolution of the phase shift between the systems (master, slave, and auxiliary)

[

] (

d θ1 − θ 2 ε r ⎞ 1⎛ε r = ω1 − ω 2 − ⎜⎜ 1 2 + 2 1 ⎟⎟ sin θ 1 − θ 2 dt 2 ⎝ r1 r2 ⎠ εr ε r − 1 3 sin θ 1 − θ 3 + 2 3 sin θ 2 − θ 3 (5a) 2r1 2r2

[

[

[

)

] (

]

[

]

]

d θ1 − θ 3 ε r ⎞ 1 ⎛ε r = ω1 − ω 3 − ⎜⎜ 1 3 + 3 1 ⎟⎟ sin θ 1 − θ 3 2 ⎝ r1 dt r3 ⎠ −

[

)

ε 1 r2 2r1

[

]

sin θ 1 − θ 2 −

ε 3 r2 2r3

[

sin θ 2 − θ 3

]

] (5b)


17

[

] (

ε r ⎞ d θ2 − θ3 1⎛ε r = ω 2 − ω 3 − ⎜⎜ 3 2 + 2 3 ⎟⎟ sin θ 2 − θ 3 2 ⎝ r3 dt r2 ⎠ ε r ε r + 2 1 sin θ 1 − θ 2 − 3 1 sin θ 1 − θ 3 (5c) 2r2 2r3

[

)

[

]

]

[

]

from which the stationary states of phase shifts are governed by the following relations: ⎛ε r ⎞ ⎛ ε 3 r2 ε 2 r3 ⎞ ε r ⎜⎜ ⎟⎟ω1 − ⎜⎜ 3 2 ω 2 + 2 3 ω 3 ⎟⎟ + 2r3 2r2 ⎠ 2r2 ⎝ 2r3 ⎠ − sin θ 1 − θ 3 = ⎝ 2 ⎛ ε 1ε 3 r2 ε 1ε 2 r3 ε 2 ε 3 r1 ⎞ ⎜ ⎟ ⎜ 4 r + 4 r r + 4r ⎟ 1 1 2 2 ⎝ ⎠

[

]

⎛ ε 1ε 3 r22 ε 1ε 2 r3 ε 2 ε 3 r1 ⎞ ⎜ ⎟ ⎜ 4 r r + 4 r + 4r ⎟ 1 3 ⎝ 13 ⎠ sin θ − θ 1 2 2 ⎛ ε 1ε 3 r2 ε 1ε 2 r3 ε 2 ε 3 r1 ⎞ ⎜ ⎟ ⎜ 4r + 4r r + 4r ⎟ 1 1 2 2 ⎝ ⎠

[

]

(6a)

⎛ε r ⎛ ε 3r1 ε1r3 ⎞ εr ⎞ ⎟⎟ω2 − ⎜⎜ 3 1 ω1 + 1 3 ω3 ⎟⎟ ⎜⎜ + 2r 2r1 ⎠ 2r1 ⎠ ⎝ 2r3 + sin θ 2 − θ3 = ⎝ 3 2 ⎛ ε1ε 3r2 ε1ε 2 r3 ε 2ε 3r1 ⎞ ⎜⎜ ⎟ + + 4r1r2 4r2 ⎟⎠ ⎝ 4r1

[

]

⎛ ε1ε 3r2 ε1ε 2 r3 ε 2ε 3r12 ⎞ ⎟ ⎜⎜ + + 4r2 4r2 r3 ⎟⎠ ⎝ 4r3 sin θ1 − θ 2 ⎛ ε1ε 3r2 ε1ε 2 r32 ε 2ε 3r1 ⎞ ⎜⎜ ⎟ + + 4r1r2 4r2 ⎟⎠ ⎝ 4r1

[

]

(6b)

]

[

⎡ ⎛ ε 3r1 ε1r3 ⎞ ⎛ε r εr ⎞ ⎤ ⎟⎟ω2 − ⎜⎜ 3 1 ω1 + 1 3 ω3 ⎟⎟ ⎥ + ⎢ ⎜⎜ 2r1 ⎠ ⎥ ⎝ 2r3 ⎢ ⎝ 2r3 2r1 ⎠ + ⎥ ⎢ ⎛ ε1ε 3r2 ε1ε 2 r32 ε 2ε 3r1 ⎞ ⎜⎜ ⎟⎟ + + ⎥ ⎢ 4r1r2 4r2 ⎠ ⎥ ⎢ ⎝ 4r1 + arcsin ⎢ ⎥=0 2 ⎛ ⎞ ⎥ ⎢ ⎜ ε1ε 3r2 + ε1ε 2 r3 + ε 2ε 3r1 ⎟ ⎥ ⎢ ⎜⎝ 4r3 4r2 4r2 r3 ⎟⎠ sin θ1 − θ 2 ⎥ ⎢ 2 ⎥ ⎢ ⎛⎜ ε1ε 3r2 + ε1ε 2 r3 + ε 2ε 3r1 ⎞⎟ ⎟ ⎥ ⎢ ⎜ 4r r r r 4 4 1 2 2 ⎠ ⎦ ⎣⎝ 1

[

]

(7)

]

The phase shift between the master and slave systems is solution in Eq. (7). Here, the amplitudes r1, 2 , 3 are obtained by computing Eqs. (3). Equation (7) is the characteristic form governing the phase shift between master and slave systems. The dependence of the phase

ε1 , ε 2 , ε 3 , ω1 , ω2 ,

and

ω3

is clearly shown. Each set of these parameters leads to the following: (a) various sets of r1, , r2 , r 3 if the motion

(

)

is quasi-periodic or chaotic; (b) a unique set of r1, , r2 , r 3 in case of regular motion with stationary

(

)

amplitudes. These sets are obtained from Eqs. (3) and are used to solve Eq. (7) numerically. Thus, phase synchronization occurs if and only if there exists real values solution of Eq. (7). To illustrate the concepts, we have chosen the values of the system parameters: following ω1 = 0.9700 , a1 = 0.2650 , f1 = 1.1500 ,

U 1 = 4.1596 , ε 1 = 0.0176 , ω 2 = 0.9750 , a 2 = 0.2650 , f 2 = 1.1500 , U 2 = 4.2796 , ε 2 = 0.0460 , ω 3 = 0.9650 , a3 = 0.2650 , f 3 = 1.1500 , U 3 = 4.2796 . Thus the regime of phase

synchronization is investigated by monitoring the coupling coefficient ε 3 . Figures 3 and 4 show sample results illustrating the achievement of both regular (Figs. 3) and chaotic (Figs. 4) phase synchronization between the master and slave systems. Indeed, both regular and chaotic phase synchronization are obtained respectively for ε 3 = 0.0050 and ε 3 = 0.0155 . The chaotic case is characterized

by

λ max = −0.0000001

Eqs. (6) can be rewritten in the form ⎡ ⎛ ε 3r2 ε 2 r3 ⎞ ⎛ε r εr ⎞ ⎤ ⎟⎟ω1 − ⎜⎜ 3 2 ω2 + 2 3 ω3 ⎟⎟ ⎥ + ⎢ ⎜⎜ 2r2 ⎝ 2r3 ⎠ −⎥ ⎢ ⎝ 2r3 2r2 ⎠ 2 ⎥ ⎢ ⎛ ε1ε 3r2 ε1ε 2 r3 ε 2ε 3r1 ⎞ ⎜⎜ ⎟⎟ + + ⎥ ⎢ 4r1r2 4r2 ⎠ ⎥ ⎢ ⎝ 4r1 θ1 − θ 2 − arcsin ⎢ ⎥ 2 ⎥ ⎢ ⎛⎜ ε1ε 3r2 + ε1ε 2 r3 + ε 2ε 3r1 ⎞⎟ ⎥ ⎢ ⎜⎝ 4r1r3 4r1 4r3 ⎟⎠ sin θ1 − θ 2 ⎥ ⎢ 2 ⎥ ⎢ ⎛⎜ ε1ε 3r2 + ε1ε 2 r3 + ε 2ε 3r1 ⎞⎟ ⎟ ⎥ ⎢ ⎜ 4r r r r 4 4 ⎝ 1 1 2 2 ⎠ ⎦ ⎣

[

shift upon the parameters

λmax = 0.0150

,

while

characterizes the regular case.

Figures 3 show respectively the projection of the attractors

(x1 , x2 ) (Fig. 3a), (r1 , r2 ) (Fig. 3b), and (θ1 − θ 2 , t )

(Fig. 3c) for

ε 3 = 0.0050 .

In Figures 4 are shown

respectively the projection of the attractors 4a),

(r1 , r2 ) (Fig.

ε 3 = 0.0155 .

4b), and

(θ

1

(x1 , x2 ) (Fig.

− θ 2 , t ) (Fig. 4c) for

Both quasi-periodic and chaotic behavior

of the fundamental parameters (that is the amplitudes and phases) of the oscillations exhibited by the master and slave systems are shown. The achievement of both regular and chaotic phase synchronization is clearly shown. This can be observed from Fig. 3c and 4c which show the locked character of the phase shift between master and slave systems in the regular and chaotic states of the coupled systems.

18


Fig. 4. Projection of the attractors of the master and slave systems in the

(x1 , x2 ) (Fig. 4a), (r1 , r2 ) (Fig. 4b), and (θ1 − θ 2 , t ) (Fig. 4c) showing the achievement of chaotic (λ max = 0.0150)

plans Fig. 3. Projection of the attractors of the master and slave systems in the plans

(x1 , x2 ) (Fig. 3a), (r1 , r2 ) (Fig. 3b), and (θ1 − θ 2 , t )

(λmax

(Fig. 3c) showing the achievement of regular

= −0.0000001) phase synchronization. The values of the system parameters are defined in the text.

phase synchronization. The values of the system parameters are defined in the text.

During our various numerical computations we found for each real value solution of Eq. (7) that the phase shift between the master and slave systems computed from Eqs. (3) was always locked. Nevertheless, a significant divergence was observed between the analytical and numerical values of ε 3 for the occurrence of phase synchronization. This divergence can be explained by the


19

fact that our analytical assumptions became obsolete when the values of the coupling parameters become large. B.

behavior of the master-slave-auxiliary configuration systems. To analyze lag synchronization, we introduce the lag variable τ 0 as follows:

~ r2 = r2 (t + τ 0 )

Lag synchronization

Now we perform, as in the previous case, the averaging over rotations of the phases and assume slow variation of the amplitudes of stationary states. Here, the variation of the terms containing both zi and φi cannot be neglected

~ z 2 = z 2 (t + τ 0 )

because zi are pulse waveforms. Under these conditions

with

(10a) (10b)

the following differential equation can be established:

(

)

1 = r1, 2,3 a1, 2,3 − z1, 2,3 cos ω 0 t + θ1, 2,3 + dt 2 ⎡ r2,1,1 cos θ1,1,1 − θ 2, 2,3 ⎤ ε 1, 2,3 ⎢ ⎥ + (8a ) ⎢ ⎥ 2 ⎢ ⎥ ⎣r3,3, 2 cos θ1, 2, 2 − θ 3,3,3 − r1, 2,3 ⎦

dr1, 2,3

(

)

(

dz1, 2,3 dt

)

[

(

)

= f1, 2,3 + z1, 2,3 r1, 2,3 cos ω 0 t + θ1, 2,3 − U 1, 2,3

]

(8b)

⎛ ⎝

ω0 ⎞ ⎟ 2π ⎠

(

+ dz1, 2,3 dt

ε 1, 2,3 2

[r

2 ,1,1

+ r3,3, 2 − r1, 2,3

[

(

]

ε1 2

[r

2

(

)

) ]

(

cos θ1 − θ 2 + r3 cos θ1 − θ 3 − r1

[r

1

(

)

)

(

[

(

)

dz1 = f1 + z1 r1 cos ω 0 t + θ1 − U 1 dt

(9a)

)

of the same order (that is a1 ≈ a2 ≈ a3 ,…..). Similar results were reported in Ref. [21] when considering a system of two identical coupled Rössler oscillators. We now consider the effects of the phase shifts θ1 − θ 2 ≠ 0 ,

(

on the dynamical

(11a )

) ]

cos θ1 − θ 2 + r3 cos θ 2 − θ 3 − ~ r2 (11b)

[

Eqs. (9) represent a system of three coupled identical chaotic oscillators, with transition to complete synchronization to be observed if the values of the parameters ai , ε i , f i and U i of the coupled systems are

θ 2 − θ3 ≠ 0)

)

]

d~ z2 = f2 + ~ z2 ~ r2 cos ω 0 t + θ1 − U 2 dt

(9b)

and

(

dr1 1 = r1 a1 − z1 cos ω 0 t + θ1 + dt 2

2

)

= f1, 2,3 + z1, 2,3 r1, 2,3 cos ω 0 t + θ1,1,1 − U 1, 2,3

θ1 − θ3 ≠ 0

Eqs. (10) can be exploited to demonstrate the transition to lag synchronization. Indeed, these equations introduce the lag variables of the slave system. They can be used to establish the following differential system:

ε2

shifts between the driving forces that can be obtained from Eqs. (6). If we neglect the effects of these phase shifts (θ1 ≈ θ 2 ≈ θ3 ) , equations (8) become:

1 = r1, 2,3 a1, 2,3 − z1, 2,3 cos ω 0 t + θ1,1,1 dt 2

(10c)

ω0

(

is identical. This is due to the phase

dr1, 2,3

(θ1 − θ 2 )

d~ r2 1 = ~ r2 a 2 − ~ z 2 cos ω 0 t + θ1 + 2 dt

Eqs. (8) constitute a system of three coupled periodically driven oscillators. Here the driving forces in the three systems are different despite the fact that their period ⎜ T =

τ0 =

]

(

)

(11c)

]

(11d)

Here are not presented the equations describing the auxiliary system due to the fact that we are considering lag synchronization between the master and slave systems. Eqs. (11) represent a system of coupled identical a1 ≈ a2 , f1 ≈ f 2 and oscillators ε1 ≈ ε 2 ,

(

U1 ≈ U 2 ) , driven with the same force but where the coupling term (ε1 ≈ ε 2 ) contains two amplitudes that are

time shifted. Hence, for weak values of the coupling this time shift does not influence the full synchronization r2 and significantly, so we get from Eqs. (11) r1 ≈ ~

z1 ≈ ~ z2 . From Eqs. (10) one can deduce the following

relationships:

20


x1 (t ) = x 2 (t + τ 0 )

(12a)

largets 1D numerical Lyapunov exponent versus ε 3 . The other parameters are defined in the text.

y1 (t ) = y 2 (t + τ 0 )

(12b)

z1 (t ) = z 2 (t + τ 0 )

(12c)

which mean the achievement of lag synchronization between the master and slave systems. To illustrate the phenomenon of lag synchronization, we have considered some fundamental restrictions defined during the demonstration of the achievement of lag synchronization that is: a) the parameters of the coupled systems are of the same order, and b) the values of the coupling coefficients are weak. These restrictions help us to choose the following set of the system parameters: ω1 = 0.9700 , a1 = 0.2650 , f1 = 1.1500 ,

U 1 = 4.1596 , ε 1 = 0.0176 , a 2 = 0.2650 , f 2 = 1.1500 , ε 2 = 0.0460 , ω 3 = 0.9650 , f 3 = 1.1500 , U 3 = 4.2796 . Thus

ω 2 = 0.9750

,

U 2 = 4.2796 , a3 = 0.2650 , the regime of lag

synchronization is investigated by monitoring the coupling coefficient ε 3 .

Fig. 5. Bifurcation diagram showing the coordinate

x2

of the attractor

(slave) in the Poincaré cross section and corresponding graph of the

Figure 5 shows some bifurcation structures encountered during the scanning process: period- doubling, sudden transition, and torus breakdown transition routes to chaos are shown in narrow windows of ε 3 . The latter transition (torus breakdown) is clearly justified by the graph of 1D largest numerical Lyapunov exponent. Fig. 5 also shows the weak degree of chaos in the master-slave-auxiliary system. Our numerical studies have revealed that the behavior of the system is very sensitive to small changes in the coupling parameter ε 3 . Indeed, the coupled system exhibits complex bifurcation structures with chaotic points or small windows randomly and suddenly alternating with points or small windows of regular motion. The scanning process performed within the range of 0 < ε 3 < 0.1 as revealed the existence of both regular and chaotic lag synchronization between the master and slave systems. We found in the case of Fig. 3a that lag synchronization occurs for the lag time τ 0 ≈ 0.015 while in Fig. 4a, lag synchronization is observed for the lag time τ 0 ≈ 0.110 IV. SIMULATION USING CNN (Cellular Neural Network) For solving the model described by equations (1) (Master-Slave-Auxiliary system) calculation programs were written in Turbo C to resolve the fourth order Runge Kutta algorithm [23]. During these computations we faced divergence phenomena for the given sets of parameters for Eqs. (1). This divergence can be explained by the high degree of stiffness of the system described by Eqs. (1) for those particular parameters values. Another problem faced during numerical computations has been the difficulty to predict the duration of the transient phase of the various computations that were performed [23]. This duration does depend on the systems’ parameters values and does rise with the increase of the system’s degree of nonlinearity. Therefore, the numerical computations using the above software code have been very time consuming and also of less accuracy due to rand-off errors accumulated during computations. In fact, these errors do increase as the degree of stiffness increases in the system described by Eqs (1) and sometimes manifest themselves by floating point overflows during computations. Therefore, there is a clear and high need of an efficient simulation tool/environment which is robust with regards to those difficulties. Otherwise, getting full insight in synchronization issues related to the models described by Eqs. (1) could be impossible. Full insight in all synchronization aspects does require computations in wide ranges of the systems parameters values, even in cases some of these values make Eqs. (1) be of high stiffness. The efficiency of calculations using CNN makes it a good candidate to perform analog computations in cases of high stiffness and therefore an appropriate tool to tackle the difficulties faced above. The structure of the elementary CNN cell (see Figure 6a) shows the possibility

21


of controlling both saturation and divergence phenomena automatically. Moreover the various feedback connections in Figure 6a aim to train the elementary cell under the linear functioning portion/domain/zone of its output characteristics. Therefore, we have designed the complete scheme of figure 6b in Matlab-simulink to solve Eqs. (1). This scheme is made-up of three layers with a total

number of nine coupled CNN cells (colored blocs) whereby each layer uses three coupled CNN cells and represents the Rössler oscillator (i.e. the Master or the Slave, or the Auxiliary system). All cells are of the same structure due to the symmetry of the Master-SlaveAuxiliary system configuration.

Fig. 6a. Graph for a CNN cell in Simulink.

Fig. 6b. Complete scheme of the CNN simulator in Simulink.


22

To test the state of the CNN simulator of figure 6b, we have chosen the same values of the parameters in figures 3 and 4 to obtain the curves in Figs. 7 a) and b). These curves represent the projections of the attractors (master and slave) in the plans x1 , x 2 in the cases of regular synchronization (Fig. 7a) and chaotic synchronization (Fig. 7b). These results obtained from the CNN simulator are identical to those obtained using Turbo-C. However, the computation using CNN is more appropriate since it is very fast and more efficient (results of very good accuracy) than the computation performed using Turbo-C.

(

)

Fig. 7a. Projection of the regular attractors of the master and slave systems in the plans

(x1 , x2 ) . Same values of the system parameters as in Fig. 3.

Fig. 7b. Projection of the chaotic attractors of the master and slave systems in the plans

(x1 , x2 ) . Same values of the system parameters as in Fig. 4.

V CONCLUSION

This paper has presented the study of synchronisation properties of three mutually coupled self-sustained chaotic oscillators. The coupled oscillators are non-identical and are of the Rössler type. This paper was motivated by a previous one accepted for publication (see Ref. [19]) which lacks a mathematical explanation of synchronisation phenomena. Yet, no mathematical tool is available in the literature to explain and predict the occurrence of synchronization phenomena in such a coupled system in a master-slave-auxiliary configuration. We have presented two analog coding and decoding schemes of an information signal. Both limits and advantages of these schemes were discussed. A mathematical analysis has been carried out to show the co-existence of phase synchronization and lag synchronization both in the regular and irregular states of the coupled system. In order to illustrate the concepts and verify the analytical results we have chosen some sets of the system parameters to show the occurrence of these types of synchronization. A numerical analysis was carried out to show the complex dynamical behaviour of the coupled systems and also to define the nature of transitions to chaos. The largest 1D numerical Lyapunov exponent and the bifurcation diagram of the attractor (slave) were used as indicators of chaotic phenomenon. It is found that chaos can arise from phase to lag synchronization through various striking scenarios such as period-doubling, sudden transition, and torus breakdown routes to chaos. Moreover, the extreme sensitivity of the coupled system to tiny variations in the coupling coefficient ε 3 has been shown. An interesting question under investigation is that of proposing a method to study the phenomenon of lag synchronization in cases where the coupling parameters are strong. Another problem under consideration is that of controlling the various types of chaotic synchronization exhibited by the master-slave-auxiliary system in the particular case where the coupling coefficients are large.

23


ACKNOWLEDGMENTS J. C. Chedjou would like to acknowledge the financial support from both the Swedish International Development Cooperation Agency (SIDA) through the International Centre for Theoretical Physics (ICTP), Trieste, Italy. Further, he does express his profound gratitude to the Institute for Smart-systems Technologies (IST), Faculty of engineering, University of Klagenfurt, Austria. REFERENCES [1]

[2] [3]

[4]

[5] [6] [7] [8]

[9] [10] [11] [12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

A. Pikovsky, M. Rosemblum, J. Kurths, Synchronization, A universal Concept in Nonlinear Sciences, Cambridge University Press, Cambridge, 2001. J. M. Gonzalez-Miranda, Synchronization and Control of Chaos, Imperial College Press, London, 2004. G. chen, X. Yu (Eds.), Chaos Control Theory and Applications in: Lecture Notes in Control and Information Sciences, Vol. 292, Springer Berlin, 2003. Boccaletti, J, Kurths, G. Osipov, D. L. Valladares, C. S. Zhou, The synchronization of chaotic systems, Phys. Rep. 366, pp. 1-101, 2002. N. F. Rulkov, ‘‘Image of Synchronized Chaos: Experiments with Circuits,’’ CHAOS, 6, pp.262-279, 1996. C. Hayashi, in “Nonlinear Oscillations in Physical Systems”, New York: McGraw-Hill, 1964. A. Andronov, A. Vitt and S. Khykin, Theory of Oscillations, Pergamon Press, Oxford, 1966. A. S. Pikovsky, M. G. Rosenblum, and J. Kurths, ‘‘Synchronization in a population of globally coupled chaotic oscillators,’’ Europhys. Lett., 34, pp. 165-170, 1971, 1996. J. Bluck, Synchronous rhythmic flashing of fireflies, II, Quart. Rev. Biol., Vol. 63, pp. 265-289, 1988 J. Bluck, and E. Buck, Synchronous fireflies, Scientific American, Vol. 234, pp. 74-85, 1976. F. E. Hanson, Comparative studies of firefly pacemakers, Proc., Vol. 37, pp. 2158-2164, 1978. E. R. Mirollo, and S. H. Strogatz, “Synchronization of pulse-coupled biological oscillators,” Siam J. Appl. Math., Vol. 50, pp. 1645-1662, 1990. K. M. Cuomo, and A. V. Oppenheim, "Circuit implementation of synchronized chaos with applications to communications," Phys. Rev. Lett. 71, 65-68, 1993. M. G. Rosenblum, A. S. Pikovsky, ‘‘Controlling Synchronization in an Ensemble of Globally Coupled Oscillators,’’ Phys. Rev. Lett., Vol. 92, pp. 1141-1152, 2004. A. A. Koronovskiĭ, A. E. Hramov, and I. A. Khromova, ‘‘The Time of Synchronisation of Oscillations in Two Coupled Identical Subsystems,’’ Technical Phys. Lett., 30, pp. 253-255, 2004. U. Parlitz, and I. Wedeking, ‘‘Chaotic Phase Synchronization Based on Binary coupling Signals,’’ Int. J. Bifurcation Chaos, 10, pp. 2527-2532, 2000. S. Bowong, and F. M. Moukam Kakmeni, ‘‘Chaos Control of Uncertain Chaotic Systems via Backstepping Approach,’’ JVA, 128, pp. 21-27, 2006. C. W. Wu, and L. O. Chua, ‘‘A simple way to synchronize chaotic systems with application to secure communication systems,’’ Int. J. Bifurcation Chaos, 3, pp.1619-1627, 1993. J. C. Chedjou, K. Kyamakya, W. Mathis, I. Moussa, A. Fomethe, A. V. Fono, "Chaotic Synchronization in Ultra Wide Band Communication and Positioning Systems," Journal of Vibration and Acoustics, 130, pp. 11012-11023, 2008.

[20] J. C. Chedjou, J. P. Dada, I. Moussa, C. Takenga, R. Anne,

and K. Kyamakya, "On the Analysis of the Dynamics and Synchronization of chaotic modulation and demodulation in UWB Communication and Positioning Systems," The Ultra-Wideband, Short-Pulse Electromagnetic 7 Book (UWB SP 7), Kluwer Academic / Plenum Publishers, 2007. [21] M. G. Rosenblum, A. S. Pikovsky, “From Phase to Lag Synchronization in Coupled Chaotic Oscillators,” Phys. Rev. Lett.., Vol. 78, pp. 4193-4196, 1997. [22] ––, URL: http://www.mit.bme.hu/research/chaos/index.html#publicat ion (Last access: February 3rd, 2008) [23] . J. C. Chedjou, , K. Kyamakya, , I. Moussa, , H. -P Kuchenbecker, W. Mathis, “Behaviour of self-sustained electromechanical transducer and Routes to chaos” Journal of Vibration and Acoustics, Vol.128, pp. 282-293, 2006,.

ISAST Transactions on Communications and Networking, No.1 Vol. 2, 2008 Danyan Luo et al.: IPv6 Address Assignment in Wireless Sensor Networks

24

IPv6 Address Assignment in Wireless Sensor Networks Danyan Luo, Decheng Zuo, Xiaozong Yang

Abstract—The auto assignment of IP address is required in order that the wireless sensor networks can be applied effectively and communicate with the Internet easily. In this paper, we present SNMAAP (Sensor Networks Multi-agents Address Assignment Protocol), an IPv6 address assignment protocol based on multi-agents for sensor networks, utilizing the IPv6 to meet the wireless sensor network’ requirement of considerable network addresses. By using agent nodes in the network, not only each node can get an IP address in a short period of time, but also the probability of the duplicate address and overhead is low. We simulate the SNMAAP and the IPv6 Stateless Autoconfiguration protocol on the OPNET Modeler. The simulation results demonstrate that under the same condition, SNMAAP guarantees a unique IP address assignment and has lower latency, communication and duplicate address detection overhead. Index Terms —- wireless sensor networks; address assignment; IPv6

I.

INTRODUCTION

Recent rapid advances in microelectronic and wireless communication have made it possible to integrate microsensor, low-power signal processing, computation and low-cost wireless communication into a sensor node. Wireless sensor networks consist of large number of sensor nodes which are scattered over a region of interest and has a feature of multihop communication[1]. As the next generation of Internet protocol, IPv6 has great advantages over IPv4. IPv6 can provide enormous IP addresses and add so many improvements to IPv4 in areas such as routing and network autoconfiguration. Wireless sensor nodes need considerable addresses. Accordingly, IPv6 is preferable to IPv4 in sensor networks. In this paper, we present SNMAAP (Sensor Networks Multi-agents Address Assignment Protocol), an IPv6 address assignment protocol based on multi-agents for sensor networks, utilizing the IPv6 to meet the wireless sensor networks’ requirement of considerable network addresses. By using agent nodes in the network, not only each node can get an IP address in a short Manuscript received May 19, 2008. This work is supported by China 863 Hi-tech Research and Development Program (2008AA01A204); China Preparatory Research Project (513160301); China Preparatory Research Project (513160303). Danyan Luo is with the Harbin Institute of Technology, Harbin, Heilongjiang 150001, CHINA (phone: 86-451-86413754; fax: 86-45186414093; e-mail: [email protected]). Decheng Zuo is with the Harbin Institute of Technology, Harbin, Heilongjiang 150001, CHINA (e-mail: [email protected]). Xiaozong Yang is with the Harbin Institute of Technology, Harbin, Heilongjiang 150001, CHINA (e-mail: [email protected]).

period of time, but also the overhead and the probability of the address collision is low. The remainder of this paper is organized as follows. Related research efforts are discussed in section 2. The protocol details for SNMAAP are given in section 3. Section 4 presents the simulations and results obtained. Section 5 concludes the paper. II.

RELATED WORK

Network address configuration protocol can be classified in stateful and stateless. Stateful address configuration protocol, like DHCP[2] assumes that there is a central entity in network which assigns IP address to new node and stores the address information in the address allocation table. In DHCP, DHCP server is responsible of address allocation. Stateless address configuration protocol allows that there is no central entity in the network and node selects its address by itself. Stateless address configuration protocol must use duplicate address detection (DAD) mechanism to verify the uniqueness of the addresses. There are mainly three duplicate address detection mechanisms: Strong DAD[3], Weak DAD[3] and Passive DAD[4]. Based on the assigning node and the duplicate address detection mechanism, stateless address configuration protocol can be classified into three kinds: (1) Independent address configuration protocol In this kind of protocol, a new node coming into a network selects an address randomly or in a certain way. In order to avoid duplicate address, duplicate address detection is needed. IPv6 Stateless Autoconfiguration[5] specifies three steps a node takes to configure its interfaces in IPv6. The steps include construction of link-local address, Duplicate Address Detection, and construction of a site-local address. During Duplicate Address Detection, flooding is required, thus making the approach unscalable. To overcome this scalability issue, an extension is proposed in [6] by building a hierarchical structure. But the cost incurred in maintaining such a hierarchical structure may be high. Perkins et al. [7] propose a solution for address autoconfiguration in ad hoc networks. An address is randomly chosen within the range 2,048 to 65,534 from the 169.254/16 address block. A node floods Route Requests for the selected IP address. If no Route Reply is received within a timeout period, the node retries for RREQ_RETRIES times. At the end of all the retries, if no response is received, the chosen IP address is assumed to be free. The node assigns itself that IP address. This approach requires the routing protocol to have a

25


“route discovery” phase. It does not address the network partitioning issues. Reference [8] presents an IPv6 address assignment protocol based on GPS information. This protocol guarantees the uniqueness of IP address and the less overhead. However, this method requires sensor node to have GPS module and increases the cost. (2) Address assignment protocol based on neighbor By using this kind of protocol, new node sends address request to its neighboring node, and neighboring node allocates IP address to the new node. Flooding is used to ensure the uniqueness of IP address. MANETconf[9] can be categorized into this kind of protocol. Mansi Ramakrishnan Thoppian et al. [10] propose a distributed protocol for dynamic IP address assignment. This protocol is based on the buddy system used for memory management and dynamically distributes the IP address block. However, flooding limits the scalability of network. Dynamic Registration and Configuration Protocol (DRCP)[11, 12] extends DHCP for wireless networks. In this protocol, each node acts as both server and client and owns an address pool. The address pool distribution is done using Dynamic Address Allocation Protocol (DAAP)[13]. Each node obtains the address pool by requesting half of the addresses from the address pool of a neighboring node. This protocol does not discuss the network partitioning issues and the impact of message loss. (3) Address assignment protocol based on address agent This kind of protocol assumes that there is an agent node in the network. Agent node maintains the IP addresses of whole network, allocates IP address to new node and handles the network merging and partition. Reference [14] proposes a scheme for the assignment of unique addresses to facilitate self-organization in a wireless sensor network. The scheme is based on the concept of hierarchical levels and can only be used in a special hierarchical network structure. Owing to the feature of large amount, no infrastructure, dynamic changeable network topology, stateless address assignment protocol is preferable for wireless sensor networks. In this paper, we present a stateless address assignment protocol SNMAAP, an IPv6 address assignment protocol based on multi-agents for sensor networks, which enables sensor node to obtain a unique address when it enters the network. III.

SENSOR NETWORKS MULTI-AGENTS ADDRESS ASSIGNMENT PROTOCOL

A. Term Definitions According to the state and the responsibility, sensor nodes can be classified into three kinds of nodes. Definition 1 NEW node NEW node is defined as the node which just comes into the network and doesn’t have a legal IP address. Definition 2 AGENT node

AGENT node is defined as the node which has obtained a legal IP address and can allocate IP address for other nodes. Definition 3 ACCOMPLISH node ACCOMPLISH node is described as the node which has obtained a legal IP address and helps AGENT node allocate IP address for other nodes.

Figure 1. Node State Transferring Graph

Figure 1 shows the transferring graph of these three kinds of nodes. Definition 4 Subnet Subnet is a network that consists of an AGENT node and the ACCOMPLISH nodes which obtained IP address from this AGENT node. Subnet is usually a subset of whole sensor networks. B. IPv6 Address Format The format of IPv6 address can be defined as Figure 2 and is composed of 64-bit network prefix, 32-bit subnet ID, 16-bit time and 16-bit random number. 0

63 64 FEC0 0000 0000 FFFE

95 96 111112 127 Subnet ID

Time

Random number

Figure 2. IPv6 Address Structure of SNMAAP

Network prefix: Ad hoc networks’ prefix is defined as FEC0:0:0:FFFE::/64[15]. Because there is no similar definition for sensor networks now, the prefix of sensor networks is also defined as FEC0:0:0:FFFE::/64. Subnet ID: Subnet ID is used to identify each subnet within the networks and created by the AGENT node. All nodes in a subnet have the same subnet ID. Different subnets have different subnet ID. RFC 2373[16] states that all IPv6 addresses that use the prefixes 001 through 111 must also use a 64-bit interface identifier that is derived from the Extended Unique Identifier (EUI)-64 address[17]. In EUI-64, the U/L bit, the seventh bit of the first byte, is set to 1. The prefix of sensor networks is in that range. In order to distinguish sensor networks from ad hoc networks and Internet, the U bit in subnet ID is set to 0. Because subnet ID is a part of IPv6 address, the IP address of different subnet will not be duplicate as long as the subnet ID of different subnet is different. Time: This field records the time when AGENT node allocates IP address to this node. Because every node’ time when AGENT node allocates IP address to it is different, in other words, an AGENT node can not allocate IP address to


26

two nodes simultaneously, this format can guarantee the uniqueness of IP address in a subnet thereby. Random number: This 16-bit random number is created when AGENT node allocates IP address to NEW node. By means of this format, the amount of available address reaches to 263, and the probability of duplicate address is only 2-63. C. Address Assignment Algorithm The address assignment algorithm of SNMAAP is described in Figure 3. SNMAAP Address Assignment Algorithm // when a NEW node comes in the network Begin broadcast AGENT _REQ ; start a timer ; if (the NEW node does not receive any NB_ACK && elapsing time ≥ MAX_TIME_OUT ) { stop the timer and choose a subnet ID ; allocate a IPv6 address to itself and change itself to AGENT node ; } if (the NEW node receives NB_ACK ) { stop the timer ; if ( there is only one subnet ID in all NB_ACK && the AGENT node in all NB_ACK is the same one) { AGENT node allocate a IPv6 address to NEW node ; NEW node change itself to ACCOMPLISH node ; } else { if ( some AGENT nodes have the same subnet ID ) merge () ; select a AGENT which will allocate a IPv6 to NEW node ; NEW node change itself to ACCOMPLISH node ; } } End merge() { do { make AGENT nodes select a new subnet ID ; }while ( all AGENT nodes have different subnet ID) ; AGENT nodes update their IP address and tell every node which got IP address from it to update their IP address ; } Figure 3. SNMAAP Address Assignment Algorithm

When a node without a legal IP address comes in the sensor networks, it becomes a NEW node. NEW node first broadcasts an agent request packet (AGENT_REQ) to neighboring nodes in order to find an AGENT node. If this node doesn’t receive any neighbor acknowledgement packets (NB_ACK) from its neighbors in the max reply time MAX_TIME_OUT, it believes that it is the only node in the network and makes itself an AGENT node. The node will create a subnet ID and a random number, and fill them, together with the current time, to the corresponding fields of IPv6 addresses which will be

this node’s IPv6 address. The NEW node therefore obtains an IP address and a subnet under the charge of this node comes into being. When the NEW node receives NB_ACK from its neighbors, it will examine the subnet ID in the NB_ACK. If both the subnet ID and the IP address of AGENT node in these NB_ACK packets are same, the NEW node will select a shortest route and send address request packet (ADDR_REQ) to AGENT node through this route. When AGENT node receives ADDR_REQ, it will create an IPv6 address for this NEW node, add this IP address to its address list, and unicast a agent acknowledgement packet (AGENT_ACK) containing this IPv6 address back to the NEW node. As long as this NEW node receives this packet, it will use this IPv6 address as its address and record the subnet ID, the IP address of the AGENT node, the route to the AGENT and the hops. When NEW node is assigned an IP address, it will make itself an ACCOMPLISH node and join in the subnet under the charge of this AGENT node. If the NEW node finds that the subnet ID in all NB_ACK packets are same, however, the IP addresses of the AGENT node are different, subnets start merging. Because there might be duplicate IP address in these subnets, merging handling process is needed. The NEW node first sends duplicate address update packet (DAU) to these AGENT nodes, and commands all AGENT nodes with the same subnet ID to reselect subnet ID. When all AGENT nodes have different subnet ID, these AGENT node update their IP addresses with new subnet ID and send address update packet (AU) to all ACCOMPLISH nodes that obtain IP address from them in order to update their subnet ID and their IP address too. Merging handling process ends when there is no duplicate address in these subnets. Then the NEW node will select the closest AGENT node with the smallest IP address and send ADDR_REQ to it through the shortest route to obtain an IP address. In SNMAAP, the NEW node cannot transmit and reply any packets of other NEW nodes. Accordingly, if all the neighbors of a NEW node are the NEW node, this NEW node will believe that there are no other nodes in the network and make itself an AGENT node to obtain an IP address. Because the probability of two nodes having the same subnet ID and IP address is 2-63, extremely low, SNMAAP doesn’t consider this case. D. Merging and Partition Handling The network topology of wireless sensor networks changes when new nodes come in the network, sensor nodes move, and nodes fail when the energy exhausts. When these happen, network merges or partitions. Address assignment protocol must be able to handle the network merging and partitioning and guarantee the address assignment and the uniqueness of the address. If network merges due to the appearance of the NEW node, NEW node will use the method mentioned in III.C to handle the merging and obtain a unique IP address. Figure 4(a) and 4(b) describe the network merging due to the node movement.


Figure 4(a) shows that there are two subnets in the network before the movement, one is composed of node A, B, C, and the other is composed of node D and E. In Figure 4(b), after the movement, ACCOMPLISH node E realizes that network merges and it becomes ACCOMPLISH node C’s neighbor by the periodic broadcasting of HELLO packet. If two ACCOMPLISH nodes have the same subnet ID, the node with the smaller IP address will handle the network merging by means of the method mentioned in III.C. If one of them is AGENT node or both nodes are AGENT node, same method is used. As can be seen from the IPv6 address format, the probability of two AGENT node having the same subnet ID but the different IP is only

2 32 − 1 , accordingly, it is seldom to 2 63

reselect subnet ID when two subnets merge.

Figure 4. Network Partitioning and Merging

Figure 4(c), 4(d), 4(e) describe the network partition because of the node movement. In Figure 4(c), there are four nodes in the sensor networks: A is an AGENT node, and B, C, D are ACCOMPLISH node. In Figure 4(d), C moves and A and D know C’s departure by the periodic HELLO packet. D modifies its route table and sets the hop counter to AGENT A to infinity, and doesn’t process any AGENT_REQ from other nodes. Figure 4(e) describes the network partition due to the movement of AGENT node A. B and C know the departure by the HELLO packet and modify their hop counter to A to infinity in their route table. They also inform the intermediate nodes which transmit requests through them that the previous route is not available. When these intermediate nodes receive the information, they also modify their route table and propagate the information sequentially. After the modification, these ACCOMPLISH node will not transmit any AGENT_REQ. If all neighbors of a NEW node are this kind of node, the NEW node will make itself an AGENT node. For the network partition because of the exhausted energy and the failure of the node, it can be handled by the above method as long as we regard this node as a moving node. E. Handle During the Data Transmission In SNMAAP, the nodes with the duplicate address only exist in the subnets which have the same subnet ID. Because the range of sensor networks is usually very wide and the two subnets which have the same subnet ID may appear on the two

ends of network, it is impossible to find these subnets only by network merging. The monitoring during the data transmission is needed. In SNMAAP, the IP address of AGENT node is contained in the data packets during the transmission. When intermediate node finds that it has the same subnet ID but the different AGENT node, duplicate IP address may exists in these two subnets. The intermediate node will handle it and change two subnet’s IP addresses by the method mentioned in III.C. IV.

SIMULATION AND RESULTS

In order to evaluate the protocol, we have simulated SNMAAP by OPNET Modeler and compared with IPv6 Stateless Autoconfiguration. The main objective of our simulation is to show that SNMAAP not only guarantees the unique IP address assignment in a short period of time, but also has a low overhead and latency. A. Simulation Environment The following is the parameters used in the simulations. We use IEEE 802.11 CSMA/CA as MAC protocol. Transmitter rate is 1024bps. Bandwidth is 10kHz. Transmission radius is 20m. When simulation began, sensor nodes were deployed randomly and then fixed there. Nodes start to work one by one. In order to evaluate the protocol under the influence of node density and amount, two simulation scenarios are used during the simulation. Scenario 1: A small square network with 10 nodes. The network size is 100m×100m. Scenario 2: A large square network with 50 nodes. The network size is 10km×10km. B. Results and Discussion Our first objective is show that SNMAAP can provide a low protocol overhead. This indicator is used to measure the amount of control packets which nodes send so as to obtain an IP address and guarantee the uniqueness of the IP address. 2500

protocol overhead (number of packets)

27

SNMAAP ISA

2000

1500

1000

500

0 0

10

20

30

40

50

node amount

Figure 5. Protocol Overhead in Large Scale Network

Figure 5 shows that in scenario 2, the overhead of every protocol increases as the node amount increases. The growth of SNMAAP protocol’ overhead is liner, and the growth of IPv6 Stateless Autoconfiguration is exponential. The overhead


28

of IPv6 Stateless Autoconfiguration is obviously larger than the overhead of SNMAAP when the amount of nodes in two protocols are same. This is mainly because that IPv6 Stateless Autoconfiguration needs flooding to detect the duplicate addresses and this will produce a great deal of control packets. In SNMAAP, nodes obtain their IP address from AGENT node which guarantees the uniqueness of IP address. SNMAAP avoids the flooding and reduces the amount of control packets consequently. protocol overhead (number of packets)

1100 1000

scenario1 scenario2

900 800 700 600 500 400 300 200 100 0 0

10

20

30

40

2

50

during the simulation. In order to evaluate this indicator, we force two subnets to have the same subnet ID during the simulation. We deploy ten nodes in scenario 1 and make node_0 and node_8 AGENT node with the different IP address but the same subnet ID. node_9 is the NEW node without a legal IP address which connects two subnets under the charge of node_0 and node_8. From the results, we can see that SNMAAP has the lower duplicate address detection overhead.

node amount

Figure 6. The Influence of The Size of Network Scenarios

40 35

SNMAAP ISA

30

total protocol latency (s)

Figure 6 shows the overhead of SNMAAP with the different node density. From the results, we can see that overhead increases as the density increases. Figure 7 describes two protocols’ total latency in scenario 2. This indicator measures the total length from the time that node initializes to the time that all sensor nodes obtain unique IP address. Results show that both protocols’ latency increase when node amount increases, but the increase rate of SNMAAP is obviously lower than IPv6 Stateless Autoconfiguration. We also compare two protocols’ average latency in Figure 8. This indicator is the average length from the time a node initializes to the time a node obtains a unique IP address. From the results, we can see that both protocols’ average latency increases as node amount increases, but SNMAAP has a lower increase rate. Table 1 shows two protocols’ overhead of duplicate address detection. In SNMAAP, two nodes with duplicate IP address exist in two subnets with the same subnet ID. The probability 32 of this condition is 2 63− 1 . Accordingly, it is hard to happen

25 20

TABLE I. OVERHEAD OF DUPLICATE ADDRESS DETECTION

15

protocol

10

Amount of control packets

IPv6 Stateless Autoconfiguration

5 0 0

2

4

6

8

10

node amount

23

SNMAAP

12

Figure 7. Total protocol latency

V. 4.0

SNMAAP ISA

average protocol latency (s)

3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0

2

4

6

node amount

Figure 8. Average Protocol Latency

8

10

CONCLUSION

Address assignment is one of the most important issues of wireless sensor networks. Sensor nodes can communicate with each other only when they have the unique IP addresses. Because IPv6 can provide enormous IP address, it is preferable to IPv4 in sensor networks. In this paper, we present SNMAAP (Sensor Networks Multi-agents Address Assignment Protocol), an IPv6 address assignment protocol based on multi-agents for sensor networks, which enables sensor node to obtain a unique address when it enters the network. In SNMAAP, we design the IPv6 address in detail to reduce the probability of duplicate IP address, and classify the nodes into three kinds. Every NEW node obtains the IP address from an AGENT node. Simulation results show that SNMAAP not only guarantees the unique IP address assignment in a short period of time, but also has a low overhead and latency.


29

REFERENCES [1]

REN FY, HUANG HN, LIN C. Wireless Sensor Networks. Journal of Software, 2003, 14(7), 1282-1291. [2] Ralph D. Dynamic Host Configuration Protocol. Network Working Group, RFC 2131, Mar. 1997. [3] Nitin V. Weak Duplicate Address Detection in Mobile Ad Hoc Networks. Proc. ACM Int'l Symp. Mobile Ad Hoc Networking and Computing (MobiHoc), June 2002. [4] Kilian W. Passive Duplicate Address Detection in Mobile Ad Hoc Networks. 0-7803-7700-1/03(C) IEEE. 2003: 1504-1509. [5] S. Thomson and T. Narten, “IPv6 Stateless Address Autoconfiguration,” RFC 2462, Dec. 1998. [6] K. Weniger and M. Zitterbart, “IPv6 Autoconfiguration in Large Scale Mobile Ad-Hoc Networks,” Proc. European Wireless 2002, Feb. 2002. [7] C. Perkins, J. Malinen, R. Wakikawa, E. Royer, and Y. Sun, “IP Address Autoconfiguration for Ad Hoc Networks,” Internet Draft, Nov. 2001, work in progress. [8] ZHAO YL, YANG XZ, YANG N GAO ZG. IPv6 Address Assignment in MANETs. Journal of Astronautics, 2005, Vol.26, No.1, 52-59. [9] S. Nesargi and R. Prakash, “MANETconf: Configuration of Hosts in a Mobile Ad Hoc Network,” Proc. INFOCOM 2002, 2002. [10] Mansi RT, Ravi P. A Distributed Protocol for Dynamic Address Assignment in Mobile Ad Hoc Networks. IEEE TRANSACTIONS ON MOBILE COMPUTING, JANUARY 2006, Vol.5, No.1:4-19. [11] McAuley AJ, K. Manousakis K. Self-Configuring Networks. 21st Century Military Comm. Conf. Proc, Oct. 2000, vol. 1, 315-319.

[12] Misra A, Das S, McAuley A, Das SK. Autoconfiguration, Registration, and Mobility Management for Pervasive Computing. IEEE Personal Comm., Aug. 2001, 24-31. [13] Patchipulusu P. Dynamic Address Allocation Protocols for Mobile Ad Hoc Networks. Master's thesis, Texas A&M Univ., Aug. 2001. [14] Jobin J, Krishnamurthy SV, Tripathi SK. A Scheme for the Assignment of Unique Addresses to Support Self-Organization in Wireless Sensor Networks. IEEE. 2004, 4578-4582 [15] Charles EP, Jari T, Ryuji W. IP Address Autoconfiguration for Ad Hoc Networks. Internet Draft, Mobile Ad Hoc Networking Working Group. November, 2001 [16] http://www.ietf.org/rfc/rfc2373.txt [17] Robert H, Stephen D. IP Version 6 Addressing Architecture, RFC 2373, July 1998

Danyan Luo received her BS and MS in computer science and technology from Harbin Institute of Technology in 2001 and 2003 respectively. Now he is a PHD student and lecturer in HIT. His research interest includes ad hoc network, wireless sensor network. Decheng Zuo is a professor in HIT. His research interest includes fault tolerant computing technology, ad hoc network, wireless sensor network. Xiaozong Yang is a professor in HIT. His research interest includes fault tolerant computing technology, computer architecture, ad hoc network, wireless sensor network.

ISAST Transactions on Communications and Networking, No.1 Vol. 2, 2008 H. Ramazanali et al.: Evaluation of ZigBee Networking in a Campus Environment

30

Evaluation of ZigBee Networking in a Campus Environment H. Ramazanali, J. Olsson, J. Lönn, A. Huynh, Q. Ye, and S. Gong

Abstract—By using our own-developed ZigBee modules, evaluations of ZigBee networking in an outdoor environment and in an indoor campus environment have been performed. It is shown that the outdoor radio link between two ZigBee modules can reach 144 m with a negligible packet error rate. In the indoor campus environment, walls or floors reduce the radio link range drastically and the packet error rate becomes significant. Moreover, latency measurements have shown that one hop of a data packet between two ZigBee modules requires 5 ms delay time, while two and three hops require 8.8 and 14.0 ms, respectively.

Applications Applications

OEM

Application Application profiles profiles Application Application framework framework

S/W

ZigBee Alliance

Network & Security Network & Security Layers Layers MAC Layer MAC Layer

Index Terms— ZigBee, modules, networking, packet error rate, latency.

MAC Layer MAC Layer

IEEE 802.15.4

H/W

I.

INTRODUCTION

Z

igBee is an open and global standard for wireless sensor networks (WSN). The first version v1.0 was ratified in December 2004 by the ZigBee alliance [1]. The aim of the standard is to define low cost, low power, and wireless networks for short range and embedded applications. The ZigBee stack is the architecture of the technology and defines the functionality. As shown in Fig. 1, the two first layers, i.e., the physical (PHY) and Medium Access Control (MAC) layers, are specified with the IEEE 802.15.4-2003 standard [2-3]. The other layers that build on the PHY and MAC layers are specified by the ZigBee alliance [4]. The network (NWK) layer is responsible for the network control functions. It controls the mechanism for joining and leaving a network and for creating a network for those devices which have the capability to do so. The NWK layer can also apply network security to prevent the ZigBee network from exposing the network topology and routers. The NWK layer is responsible for discovery and storing information about the neighbors in the network. This layer also performs routing between devices and routing of packets to their destination.

Manuscript received Dec. 14, 2007. The Swedish Energy Authority (Energimyndigheten) is acknowledged for financial support of this study. H. Ramazanali was with the Department of Science and Technology, Linköping University, but he is now with a consulting company. A. Huynh, Q. Ye, and S. Gong are with the Department of Science and Technology, Linköping University, SE-601 74 Norrköping, Sweden (phone: +46 11 363459; fax: +46 11 363270; e-mail: [email protected]). J. Olsson and J. Lönn are with Tritech Technology AB, SE-581 02 Linköping, Sweden.

Physical Physical layer layer

Fig. 1. ZigBee stack.

The services of the NWK layer enable different network topologies. The Application framework layer contains application objects which can be specified by a manufacturer. Each device can contain up to 240 application objects that are specified through endpoints. An example of an application object is a power switch or an analog to digital (A/D) signal converter. The lowest two layers are implemented in hardware (H/W) and the up-layers are implemented as software (S/W), e.g., Z-Stack from [5]. By combining the star and peer-to-peer topologies, ZigBee can form so-called mesh networks that may extend over a large area and contain thousands of nodes [4]. Each full function device (FFD) in the network also acts as a router to direct messages. The routing of the network can dynamically change, so as to take evolving conditions into account. This enables an extremely reliable network, since the network can heal itself if one node is disabled. A new network node may be recognized and associated in about 30 ms. Waking up a sleeping node takes about 15 ms, as does accessing a channel or transmitting data. ZigBee applications benefit from the ability to quickly attach information, detach, and go to deep sleep, which results in low power consumption and thus extended battery life [6]. As seen in Fig. 1, wireless systems like ZigBee have a software stack where the information must be processed for delivery in the air. This signal is then received at the receiver side where it is processed in the RF (radio frequency) front-


31

end, i.e., the PHY and MAC layers, and up through the software stack to the appropriate layer depending on types of packets and end receivers. The delay time from an operation being processed in a transmitter to the operation being performed and received in the receiver is the latency. This kind of latency depends on not only the communication protocol, but also the used software stack and compiler [7-8].

II. ZIGBEE MODULES Fig. 2 shows our own-developed ZigBee module. Detailed information about the module can be found in [9]. The block diagram of the module is shown in Fig. 2a. The RF part utilizes the TI CC2420 chip [5], while the micro-controller is the ATmega128L chip [10]. The module has been designed to support in-system programming via the JTAG interface. It has a wide range of external data interfaces, which allows the micro-controller to communicate with almost any external device. The key characteristics of the module are listed as follows: • Size: 24x40mm • Compact module for quick prototyping • USARTs, SPI, TWI and JTAG interfaces • 5 digital I/O ports, and 5 pieces of 10bit ADC ports • 32.768kHz real time clock (RTC) • Integral or external antenna • 2.7-10V voltage supply

(c) Back side Fig. 2. The developed ZigBee module.

III. MESUREMENTS Measurements can be divided into three categories, i.e., RF measurements, range and packet error rate measurements, and latency measurements. The devices and instruments used for measurements are listed in Table I. TABLE I. MEASUREMENT INSTRUMENTS ZigBee modules Spectrum analyzer, Agilent (9 kHz ‐ 26.5 GHz) Oscilloscope Infiniium, Agilent 54833D MSO 6x 10 RG 58 cables with BNC, 6x BNC T adapter TI ZigBee DK, incl. packet sniffer and programmer

Connector

RF

(a)

BalUn Circuit

Block diagram

(b) Front side

RF Transceiver Circuit (CC2420)

Microcontroller Circuit (ATmega128L)

A. RF Measurement The radio output spectrum from the ZigBee module is measured with reference to the IEEE 802.15.4-2003 standard [2]. Three modules are used for the measurement in order to get average results. The modules are connected to the Spectrum analyzer with SMA connectors, RG 58 cables and an N-Type connector. Software is developed in Z-Stack for RF power measurements and it is used for setting up test signals on the modules that are required for the measurements. The measurement result is exported from instruments as screen shots and edited in Microsoft Excel as tables and diagrams. B. Range and Packet Error Rate This measurement is made to evaluate the radio range of the ZigBee system, and to characterize its overall performance. The ZigBee system uses direct sequence spread spectrum (DSSS) with symbol synchronization. That is, data are sent using packets and frames. The result of this is that the packet error rate (PER) shows the accuracy of the data transmission better than bit error rate (BER). The packets sent are secured with a frame check sequence at the MAC level. If the frame check sequence of any packet is not correct it is dismissed. The PER represents the percentage value of the packets that are not detected correctly on the receiver side.


32

The software which measures the PER is developed in the Z-Stack. One module sends out a defined number of packets which are received by a receiving module where the PER value is calculated. The receiving module is connected to a ZigBee-enabled USB dongle connected to a computer. Measurements are performed in a building at Linköping University, Campus Norrköping for both line-of-sight (LOS) and obstruction measurements. Outdoor measurements are made in a field with free LOS between two modules.

IV. RESULTS Measurement results of RF characterization, radio range and packet error rate, and latency are presented below. A. RF Characteristics Fig. 4 shows the output signal spectrum using an unmodulated continues-wave carrier at 2.405 GHz. The output signal is directly connected to the Spectrum Analyzer using a co-axial cable.

C. Latency Latency is important to measure for ZigBee with a mesh network. The delay time becomes especially important in applications with a large sensor network. The goal here is to characterize the latency for both peer-to-peer and multi-hop mesh networks. Therefore, measurements are made not only on a peer-to-peer network with a coordinator and an end device, but also on a mesh network with a coordinator, routers and an end-device. The specification for the measurement setup is illustrated in Fig. 3. A command is performed in Module 1 to set pin 1 high on the same device and also on Module 2. An oscilloscope is trigged on pin 1 from Module 1. When the operation is performed on Module 1, pin 1 is set high and the oscilloscope starts a measurement. At the same time an over-air command is sent to Module 2 to set pin 1 high. When this pin is also set high the measurement on the oscilloscope is completed. The latency can then be read out on the oscilloscope as the delta time between the two up-flanks of the signal on the pins. The software for measuring latency was developed with ZStack version 1.0 [5]. Programming of modules was made in AVR Studio 4.0 from Atmel [10]. A packet sniffer was used to detect packets in the air for controlling and verifying the measurements.

Module 2

Module 1 Set pin 1 on module 1

Set pin 1 on module 2

Receive “Set pin command”

ZigBee Stack

Fig.4. Un-modulated signal spectrum.

Table II summarizes the measured output power of unmodulated signal at three different carrier frequencies, i.e., 2.405, 2.44, and 2.48 GHz, respectively. Deviations from 3 modules are also listed in the table. TABLE II. OUTPUT POWER OF UN-MODULATED SIGNAL Frequency

Module 1

Module 2

Module 3

Deviation

Average

[GHz]

[dBm]

[dBm]

[dBm]

[dB]

[dBm]

2.405

1.14

1.82

2.50

1.36

1.82

2.44

0.67

1.51

2.30

1.63

1.49

2.48

0.38

0.59

2.21

1.83

1.06

Deviation

0.76

1.23

0.30

Average

0.73

1.30

2.34

Fig. 5 shows the modulated output signal spectrum with a carrier at 2.44 GHz. The output signal is directly connected to the Spectrum Analyzer using a co-axial cable.

ZigBee stack

Over air Pin 1 high

Pin 1 high

Oscilloscope

Fig.3. Latency measurement setup.

Fig.5. Modulated signal spectrum


33

Table III summarizes the measured output power of modulated signal at three different carrier frequencies, i.e., 2.405, 2.44, and 2.48 GHz, respectively. Deviations from 3 modules are also listed. TABLE III. OUTPUT POWER OF MODULATED SIGNAL Frequency

Module 1

Module 2

Module 3

Deviation

Average

[GHz]

[dBm]

[dBm]

[dBm]

[dB]

[dBm]

2.405

-5.70

-5.64

-4.73

0.96

-8.03

2.44

-5.83

-6.63

-5.56

1.07

-6.00

2.48

-6.93

-7.18

-4.93

2.25

-6.35

Deviation

1.24

1.54

0.83

Average

-6.15

-6.48

-5.07

B. Line-of-Sight Range This measurement is performed outdoors in a field called Himmelstalund in Norrköping, see Fig. 5. The terrain is pretty even with no obstruction. The weather condition was fairly cold, approximate 10 °C. It was rather foggy and cloudy with high humidity in the air. The modules are set up on a tripod at a height of 1.5 m, see Fig. 6. The first module⎯the Coordinator is stationary and connected to a computer. The second module is the end-device and can be freely moved around. The distance is increased after a measurement and the movement follows a straight line. The setup of the modules and the PER measurement are made on the same place. The measured results are summarized in Table IV. It is seen that a good LOS radio range of about 144 m can be achieved.

Fig.6. Terrain and weather during light-of-sight measurement

TABLE IV. LINE-OF-SIGHT RANGE AND PER Range

PER

[m]

[%]

70.2

0.00

80.2

0.00

99.6

0.00

113.6

0.00

122.9

0.00

133.6

0.00

143.6

0.00

145.0

No connection

For measurements No. 1-13 modules are placed at a height of 1 m. No. 20-22 modules are placed 1.0-1.5 m above the floor. Another module, No. 23 that is not seen in Fig.7, is placed at the same location as the coordinator (No. 20) but a floor up.

Fig.5. Site for line-of-sight measurement.

C. Packet error rate The indoor measurements are performed to evaluate the ZigBee network in a typical campus environment, where multi-floor classrooms and corridors are present, see Fig. 7.

Fig.7. Setup for PER measurements.


34

The building shown in Fig.7 is a modern building that is built of light materials as wood and plaster with frame of steel and concrete. The classrooms have all large windows to the outside and small windows near the ceiling to the corridor. The area was controlled with a spectrum analyzer and a 2.4 GHz antenna to insure that there was no other interferer signal present during the measurement. Table V lists the range and PER together with module numbers shown in Fig.7. It is seen that good radio link in corridors can be achieved even with a 90 ° turning. TABLE V. RANGE AND PER IN CORRIDORS Module No.

Range

PER

[m]

[%]

1-8

5-60

0.00

9

15+5

0.00

10

15+10

0.00

12

15+30

0.00

13

15+37

0.00

Fig.8. Setup for latency measurement.

Table VI lists the range and PER for the modules No. 20-22 shown in Fig. 7 and another module (No. 23) placed a floor above the coordinator module (No. 20) in Fig. 7. Significant PERs are seen in theses cases, except the case between adjacent classrooms.

TABLE VI. RANGE AND PER WITH OBSTACLES Location

Range

PER

[m]

[%]

Adjacent classroom

10

0

Alternate classroom

18

1.9

One floor up

~4

1.12

Classroom to corridor

5

0.18

The values for measurements with obstacles should not be interpreted as a result of the radio penetrating the building. It can be a result of the classrooms having windows so that radio signals propagate through those windows. D. Latency These measurements are performed in the same campus building as for the PER measurement. As shown in Fig. 8, four modules (C, 1, 2, 3) are placed in the environment. The coordinator C is stationary and is not moved. Device 1 is placed in the window. All devices are placed with line-ofsight only to the adjacent module. This ensures that a mesh network is build up rather than a star network when automatching is used to bind the modules. The distance between devices C-1 is 10 m, between 1-2 is 20 m, and between 2-3 is 10 m.

Fig.9. Measured delay time with three hops.

One hop Two modules are used for this measurement. The coordinator C is placed as shown in Fig. 8 and Device 1 is the sending end-device. Two hops This is a mesh network. The coordinator C is placed as shown in Fig. 8, Device 1 is a router and Device 2 is the sending end-device. Three hops This is a mesh network. The coordinator C is placed as shown in Fig. 8, Devices 1 and 2 are routers and Device 3 is the sending end-device. The measured delay time values with one, two and three hops are listed in Table VII. Fig. 9 shows delay time with three hops on the oscilloscope.


35

REFERENCES TABLE VII. DELAY TIME Device

Hop times

Delay (ms)

1

1

5.0

2

2

8.8

3

3

14.0

V. DISCUSSION Results from RF measurements show that our ZigBee modules fulfill the standard specified by IEEE 802.15.4 and the ZigBee alliance. Thus, the evaluation results shown in this paper can be generalized to other ZigBee devices that fulfill the ZigBee standard. The PER measurements show that ZigBee has a long LOS range (144 m) with negligible PER. However, walls and floors in a building will hind the radio signal, resulting in a drastic reduction of the range to only a few meters, see Table VI. In such an environment, PER becomes considerable. These phenomena must be aware of, when building a large ZigBee network for real time applications. It seems that the most reliable way for a good radio link is to place neighboring modules in LOS, while building up a network. As seen in Table VII, the latency for a hop between the coordinator and the end-device is 5 ms. This is the packet delay time in a star network. When multi-hop is involved in a mesh network, the delay time will increase, proportional to the number of hops, i.e.. number of nodes (see Table VII). The more nodes exist in a ZigBee mesh network, the longer latency will be. This can be a problem in critical real time applications, when a large ZigBee network is involved. One way to solve this problem is to increase the output power of ZigBee modules, e.g., from 0 to 10 dBm so that the radio range increases, resulting in reduced number of nodes in a ZigBee mesh network.

VI. CONCLUSION The outdoor line-of-sight radio range between two ZigBee modules can reach 144 m, when they fulfill the IEEE 802.15.4 or ZigBee standard. The packet error rate is negligible within this range. The indoor measurement shows that a radio range up to 60 m with negligible packet error rate can be obtained in a corridor of a campus building. However, walls and floors hind the radio signal, reducing the radio range to only a few meters with large packet error rates. Based on our ZigBee modules and the used Z-Stack, the latency for one hop between the coordinator and the end-device is 5 ms. Two and three hops result in a latency of 8.8 and 14.0 ms, respectively. Thus, latency is roughly proportional to the number of routers in a mesh network when transmitting packets.

[1]

ZigBee Specification, ZigBee Alliance, ZigBee Document 053474r05, Version 1.0, 2005-06-20 [2] IEEE 802.15.4-2003 Standard, IEEE Computer Society ISBN0-7381-3677-5 SS95127, 2003-10-1 [3] Jon T. Adams, “An introduction to IEEE STD 802.15.4,” Proceedings of IEEE Aero Space Conference 2006, pp. 1-8, 4-11. March. 2006. [4] IEEE Std 802.15.4 Wireless Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs), May, 2003. [5] Texas Instruments Inc., http//www.ti.com, 2006-08 [6] Jin-Shyan Lee, “An Experiment on Performance Study of IEEE 802.15.4 Wireless Networks,” Proceedings of 10th IEEE Conference on Emerging Technology and Factory Automation, vol. 2, pp. 4511-458, Sep. 2005. [7] Niels Aakvaag, Mogens Mathiesen, and Gilles Thonet, “Timing and power issues in wireless sensor networks - an industrial test case,” Proceedings of International Conference Workshops on Parallel Processing (ICPP) 2005, pp. 419-426, 14-17. June. 2005. [8] Bernard Kai-Ping Koh and Peng-Yong Kong, “Performance Study on ZigBee-Based Wireless Personal Area Networks for Real-Time Health Monitoring,” ETRI Journal, Vol. 28, No. 4, pp. 537-540, Aug. 2006. [9] Johan Lönn, Jonas Olsson, and Shaofang Gong, Proc. of the First Workshop on Real-World Wireless Sensor Networks REALWSN 2005, Stockholm, Sweden, 2005 [10] Atmel Corporation, http://www.atmel.com, 2006-01

ISAST Transactions on Communications and Networking, No.1 Vol. 2, 2008 Abhijeet Shirgurkar and M. I. Hayee: Transoceanic Networks for 44Gb/s WDM Transmission using π/2 Alternate-Phase RZ Modulation Format

36

Transoceanic Networks for 44 Gb/s WDM Transmission using π/2 Alternate-Phase RZ Modulation Format Abhijeet Shirgurkar and M. I. Hayee, Senior Member, IEEE

Abstract— We have explored the feasibility of 44 Gb/s WDM transmission over a 6000 km transoceanic link using π/2Alternate-Phase RZ modulation format. We have found that the π/2 APRZ outperforms regular RZ or CSRZ by more than 3 dB, thereby enabling successful 40Gb/s WDM transmission over transoceanic distance with sufficient system margin for 200 GHz channel spacing. Furthermore, we added synchronous phase modulation (chirp) to the π/2-APRZ modulation format, and found that the optimal chirp gives an additional 1 dB advantage in system performance when the channel spacing is 200 GHz. However, the additional advantage of the chirp goes away as the channel spacing is reduced to 120 GHz. To further improve the system performance for small channel spacing, we explored a pre-filtering technique and found that more than 2 dB of Q improvement is obtained for a π/2-APRZ system at the channel spacing of 100 GHz for 40 Gb/s WDM systems, thereby making 0.4 Gb/s/Hz spectral efficiency for transoceanic 40 Gb/s WDM systems viable. Index Terms — Fiber optics communications, optical communications, spectral efficiency, modulation format, wavelength division multiplexing, fiber nonlinearity

T

I. INTRODUCTION

o achieve successful 40 Gb/s wavelength division multiplexed (WDM) transmission over a transoceanic distance (6000 km or more), fiber nonlinearity needs to be overcome either by using a nonlinearity-resistance modulation format [1-4], a nonlinearity-combatant dispersion map [5-7], or a combination of both [8-10]. Various types of modulation formats have been proposed to successfully transmit 40 Gb/s WDM channels over long-haul distances [11-14]. All these modulation formats could be grouped into two categories. The first category encodes the information in the intensity of the optical pulse and the second category encodes the information in the relative phase of the two adjacent pulses. The most successful schemes to combat fiber nonlinearity in the intensity modulation category are return-to-zero (RZ), carriersuppressed RZ (CSRZ) and chirped RZ. Similarly, the most successful schemes in the differential phase modulation category are RZ differential phase shift keying (RZ-DPSK), Abhijeet Shirgurkar and M. I. Hayee are with the Electrical and Computer Engineering Department in University of Minnesota Duluth, Duluth, MN 55812 USA (phone: 218-726-6743; fax: 218-726-7267; e-mail: [email protected]) .

CSRZ-DPSK, chirped RZ-DPSK and similar versions of differential quaternary phase shift keying (DQPSK). Until now, there has been very little success in obtaining practical 40 Gb/s transmission performance over transoceanic distances using intensity modulation schemes. An example of a successful WDM 40 Gb/s transmission experiment using intensity modulation was reported in the year 2002 where a strong forward error correction (FEC) scheme had to be employed to narrowly obtain the necessary system margin [10]. Meanwhile, there have been numerous successful experimental demonstrations for transoceanic distances by utilizing some kind of differential phase shift keying modulation format [11]. This is due to the much improved performance of differential phase modulation formats in the presence of optical noise and fiber nonlinearities as compared to their intensity modulation counterparts [14]. However, to achieve the improved performance, the receiver of differential phase modulation formats needs to have a Mach-Zehnder delay interferometer, which is costly and harder to implement in product-grade systems. Therefore, intensity modulation schemes could be more desirable for implementing practical 40 Gb/s transoceanic systems. Recently, π/2 alternate phase (AP) intensity modulation formats have been shown to be extremely robust against fiber nonlinearities as compared to regular RZ or carrier-suppressed RZ [15-19]. In this paper, we have explored 44 Gb/s (we assumed 10% FEC overhead) WDM transmission using π/2-APRZ modulation format over a 6000 km transoceanic link. We have found that the π/2 APRZ outperforms regular RZ or CSRZ by more than 3 dB, thereby enabling successful 40Gb/s WDM transmission over transoceanic distance with sufficient system margin. Furthermore, we added synchronous phase modulation (chirp) to the APRZ modulation format and found that the optimal chirp gives an additional 1 dB advantage in system performance when the channel spacing is 200 GHz. However, the additional advantage of the chirp diminishes as the channel spacing is reduced to 120 GHz. To further improve the system performance for small channel spacing, we explored the prefiltering technique and found that more than 2 dB of Q improvement is obtained for π/2 APRZ system at the channel spacing of 100 GHz for 40 Gb/s WDM systems. In section II of this paper the simulated system model for transoceanic distance is described. The dispersion map and Raman amplifier architectures are also discussed. In section III, the results of transmission simulations for APRZ


37

modulation formats are analyzed and elaborated. Comparison of various alternate phase modulations is performed to ascertain the feasibility of 40 Gb/s transoceanic transmission

networks. Finally, the conclusions are summarized in section IV.

λ1 LD

NRZ

RZ

AP

22 GHz 44 Gb/s data

Chirp

44 GHz 22 GHz

λ2

pump-signal combiner

40km 20km M U X

+D -D

λN

Raman Pumps

D E M U X

λ1 λ2

λN

Figure 1. Schematic diagram of modeled system including transmitter and fiber link

I. SIMULATED SYSTEM MODEL The transmitter model consists of four modulation stages as shown in Figure 1. The first stage encodes 44 Gb/s data with 12 dB extinction ratio and the second stage carves out 33% duty cycle RZ pulses. Please note that 44 Gb/s includes 40 Gb/s data and 10% overhead to accommodate forward error correction (FEC) [20]. The third modulation stage imposes alternate phase (AP) modulation of the desired magnitude on the adjacent data pulses. The last stage is to impose synchronous phase modulation (chirp) when desired. Outputs from similar transmitters having different wavelengths are then optically multiplexed together before launching into the fiber link (Figure 1). The fiber link consists of 100 spans of 60-km each followed by a Raman amplifier. The fiber span consists of two slope compensating fiber types typically used for transoceanic dispersion maps. The parameters of the fibers used are given in Table I. The path average dispersion of each span is -0.1 ps/nm-km. Fifty percent of the total accumulated dispersion is compensated after the optical multiplexer and before launching the signal into the fiber. The remaining 50% of dispersion is compensated at the receiver to optimize the eye opening of each channel. TABLE I Fiber Parameters Length Dispersio Loss (km) (dB/km) n

Aeff (μm2)

(ps/nm-km)

Fiber 1 Fiber 2

40 20

+20 -40

0.20 0.25

110 27

Simulation of the propagation of optical signal through the fiber is performed by numerically solving the nonlinear Schrödinger equation (NLSE) using the split step Fourier method. We simulated only single polarization, thereby including the worst case with respect to fiber nonlinearity but excluding the polarization mode dispersion effect. The included fiber effects are dispersion and nonlinearity accounting for self-phase modulation (SPM), cross-phase modulation (XPM) and four-wave mixing (FWM). Our receiver model consists of an optical de-multiplexer followed by a photo-detector. We used an optical bandpass filter of the

bandwidth equivalent to the channel spacing of the WDM system as an optical de-multiplexer. After the photo-detector, the electrical signal is passed through a lowpass filter of bandwidth of 30 GHz. In our simulations, we used a super Gaussian optical bandpass filter and a 10th order Bessel electrical lowpass filter. Transmission Fiber +D

-D

pump-signal combiner +D -D

Pump Combiner

Pump Wavelengths (nm)

1425

1435

1449

1460

1469

Pump Powers (mW)

85.4

75

62.7

60.7

45

Figure 2. Schematic of backward pumped Raman amplifier used at the end of each span. Please note the given pump powers are for the input signal launched power of -5 dBm/channel.

The total span loss including the splice losses, pump-signal combiner loss, and gain equalizing filter loss is 14 dB. The loss of each span is compensated by a Raman amplifier. The fiber loss term in the NLSE is replaced by the Raman gain which is simulated separately as described in [21,22]. The Raman amplifier consists of 5 backward pump wavelengths of 1425, 1435, 1449, 1460, and 1469 nm as shown in Figure 2. The achieved amplifier bandwidth was 32 nm, ranging from 1530 nm to 1562 nm. The gain fluctuations were minimized using pump powers within 1.0 dB with a minimum gain of 14 dB. The excess Raman Gain is shown in Figure 3a which overcomes the loss of gain equalizing filters. A total of 20 signal wavelengths with 1.6 nm channel spacing were simulated with 5 Raman pumps. The power profiles of three signal channels evolving through the transmission fiber are shown in Figure 3b. When the signal power was -5 dBm/channel, the required pump powers to achieve equalized gain were 85.4, 75.1, 62.8, 60.7 and 45 mW, respectively, for the shortest to longest pump wavelengths. The signal-to-noise ratio (SNR) ranged from 39.4 to 40.3 dB for each span, with the worst SNR being at the smallest signal wavelength as shown in Figure 3a.


38

Excess Gain

1.2

40.6

0.9

40.2

0.6

1538

1546

Wavelength (nm)

39 1562

1554

11

7 Longest Wavelength Middle w avelength Shortest Wavelength

-5 -7

-8

(a)

-7

-6

-5

-13 0

5

10

15

20

25

30

35

Distance (km)

40

45

50

55

60

Figure 3. (a) Excess Raman Gain and OSNR versus wavelength for single Raman amplifier span, and (b) Power versus distance for a single Raman amplified span for three wavelengths (longest, shortest and middle wavelength). The input power was -5 dBm/channel for part (a).

Each data channel was modulated with an independent 27 pseudo-random bit pattern at a bit rate of 44 Gb/s. The multiplexed optical data was propagated through the fiber link using the power profile obtained in the Raman amplifier simulations (Figure 3b). The noise was not added at each amplifier during the propagation through the fiber to estimate nonlinear degradation. Total noise calculated from the Raman amplifier simulations was added at the receiver before the photo-detection to estimate the Q factor [23]. II. RESULT AND DISCUSSION We first simulated a 44Gb/s WDM system with 200 GHz channel spacing by setting the AP to 0, π/2 and π while keeping the chirp at zero. The resulting Q factor of the worst channel (having the least SNR and highest path average power) versus the launched channel power is shown in Figure 4a for 0, π/2 and π APRZ systems. The system performance of both RZ (0 APRZ) and π-APRZ systems is comparable, and the maximum Q is only 10 dB at the optimum signal power of -7 dBm. On the other hand, the performance of the π/2-APRZ system outperforms the other two by more than 3 dB and can achieve a maximum Q of 13.5 dB at a power level of -4 dBm. This performance improvement is due to the extraordinary ability of the π/2-APRZ modulation format to suppress intrachannel FWM as compared to regular RZ (0-APRZ) and carrier-suppressed RZ (π-APRZ) [15-18]. Please note that conventional carrier-suppressed RZ has 66% duty cycle pulses while we simulated 33% duty cycle pulses. We also simulated conventional carrier-suppressed RZ and found no improvement in performance (not shown here). The eye diagrams for the three systems with the launched channel power of -5 dBm are also shown in Figure 4a as insets. These

-3

-2

-1

-2

-1

π/2 APRZ

13

-11

-4

Launched power/channel (dBm)

15

-9

Q (dB)

Power (dBm)

π APRZ RZ

9

-3

(b)

π/2 APRZ

13

Q (dB)

39.4

0 1530

15

39.8

OSNR

0.3

(a)

eye diagrams show that π/2-APRZ suppresses the ghost pulses generated by intra-channel FWM very efficiently for the transoceanic system as compared to 0 and π-APRZ systems.

41

OSNR (dB)

Excess Gain (dB)

1.5

π APRZ

11

π/2 APRZ without chirp

RZ

9 7 (b)

-8

-7

-6

-5

-4

-3

Launched power/channel (dBm)

Figure 4. System Q versus launched channel power for 0, π, and π/2 APRZ modulation formats with (a) zero chirp and (b) optimal chirp. The transmission distance is 6000 km and the channel spacing is 200 GHz. The eye diagrams shown as insets are at the power level of -5 dBm.

We then added chirp (synchronous phase modulation) in addition to AP, and simulated chirped versions of the 0, π/2 and π APRZ modulation formats. The performance of the chirped RZ, π-APRZ and π/2-APRZ systems is shown in Figure 4b. When compared with the corresponding results in Figure 4a, the chirp gives an additional advantage of ~1 dB improvement in Q for each of the RZ, π-APRZ and π/2-APRZ modulation formats. For reference, the performance of the π/2APRZ system with zero chirp (from Figure 4a) is also shown in Figure 4b as a dashed line with hollow triangles. This shows that chirp not only improves the performance of regular RZ or carrier suppressed RZ system, but also the π/2-APRZ system, equally efficiently. Synchronous phase modulation or chirp suppresses fiber nonlinearity by spreading the signal power spectral density and consequently reducing the signal power per unit bandwidth. Therefore, the tendency of chirp to improve the system performance by suppressing the fiber nonlinearity works similarly regardless of the magnitude of the AP modulation. Since chirp spreads the spectrum, the optimum amplitude of the chirp depends upon the initial spectrum width as well as the channel spacing. As a result, the magnitude of the chirp was optimized to obtain the best performance for each modulation format of the 200 GHz WDM system (Figure 4b). The optimal chirp was 1.25, 1.5 and 1.5 rad, respectively, for the RZ, π-APRZ and π/2-APRZ modulation formats. The overall data carrying capacity using a limited amplifier bandwidth i.e., spectral efficiency is determined by


39

Optimum Q (dB)

15 Chirped π/2 APRZ

13

π/2 APRZ

11

π APRZ

9 RZ

7 100

120

140

160

Channel Spacing (GHz)

180

200

Figure 5. Optimum Q versus channel spacing for RZ, π-APRZ, π/2-APRZ, and chirped π/2-APRZ systems. The transmission distance is 6000 km.

the channel spacing. .Although, π/2-APRZ outperforms 0 or πAPRZ modulation formats for a 200 GHz WDM system, to see the spectral efficiency of the AP modulation formats, we next varied the channel spacing of the RZ, π-APRZ and π/2APRZ systems and repeated the simulations for the 6000 km transmission distance. The best achievable Q at optimal power versus channel spacing for all three modulation formats is

shown in Figure 5. As channel spacing is decreased, both linear and nonlinear crosstalk increase and the system performance is degraded. This trend is common in all three intensity modulation formats. Even though π-APRZ is supposed to be more bandwidth efficient [24], the transmission performance is not much better than regular RZ even at 100 GHz channel spacing. On the other hand, π/2-APRZ consistently performs significantly than the other two modulation formats up to 100 GHz channel spacing. However, the linear cross-talk tends to dominate IFWM suppression capability of π/2-APRZ beyond 120 GHz (Figure 5). We also analyzed the chirped π/2-APRZ modulation format with varying channel spacing. The resulting Q values at the optimum chirp are superimposed in Figure 5. The additional advantage of chirp vanishes at about 120 GHz channel spacing as there is no room to further spread the spectrum by chirping. However, π/2-APRZ still can achieve ~12.5 dB of Q at channel spacing of 120 GHz, which when combined with the gain of 10% overhead FEC allows a sufficient system margin be achieved [20].

1 95% energy-BW line

0.8

Normalized Power

80% energy-BW line Set up for estimating percentage energy bandwidth

0.6 RZ

0.4

Power Meter

BPF

Tx

π/2 APRZ

0.2 π APRZ

0 0

20

40

60

80

100

120

Pre Filter Bandwidth (GHz)

140

160

180

200

Figure 6. Normalized output power versus bandwidth of Bandpass Filter (BPF). Inset shows the setup used to determine the y axis.

As noticed from Figure 5, the π-APRZ modulation format does not perform better or even comparable to that of the π/2APRZ modulation format in spite of being more bandwidth efficient [24,25]. To estimate the bandwidths we passed the output of the APRZ transmitter through a band-pass filter (BPF) having a variable bandwidth and determined the output power as shown in the inset of Figure 6. The normalized output power versus the bandwidth of the variable BPF is shown in Figure 6 for the RZ, π-APRZ, and π/2-APRZ modulation formats. As the bandwidth of the variable bandpass filter is increased, more and more energy of the transmitter signal is passed through the BPF and the output increases until the bandwidth of the BPF is large enough to let all of the energy of the transmitter signal pass through. Please note that the definition of the bandwidth of a modulated signal is subjective and depends upon what percentage of signal energy needs to be included. We define percentage-energy

bandwidth as a bandwidth which includes a desired percentage of signal energy. The curves shown in Figure 6 can give an estimate of the percentage-energy bandwidths of the three APRZ modulation formats. Apparently, 80%-energy bandwidth of 44 Gb/s π-APRZ is the smallest (~50 GHz) as compared to that of π/2-APRZ (~70GHz) and regular RZ (~87GHz). However, if 95%-energy bandwidth is compared (Figure 5) for all three modulation formats, it turns out to be very comparable for all three modulation formats and is close to ~96 GHz. The ability of APRZ modulation formats (especially π/2APRZ ) to suppress IFWM comes from the balance and interaction of the spectral components. On one hand, the spectral spread of the APRZ modulation format is responsible for the suppression of IFWM, and on the other hand causes linear crosstalk in WDM systems. If a pre-filter is used to limit the bandwidth of the APRZ modulation format before transmission to the fiber, linear crosstalk could be reduced, but


40

Differential Q (dB)

at the same time IFWM might increase. We first explored the 0 -1 -2

π APRZ Regular RZ

-3

π/2 APRZ

-4 -5 60

70

80

90

100

110

Pre Filter Bandwidth (GHz)

120

200

Figure 7. Differential Q of a single channel system for RZ, π-APRZ, and π/2APRZ systems versus bandwidth of pre-filter for 6000 km transmission link.

balance and extent of the spectral spread needed to efficiently suppress the IFWM for APRZ modulation formats. We used a pre-filter in a single channel APRZ system and simulated the performance for a 6000 km transmission link. Please note that the power of each modulated signal is adjusted to obtain the same power at the output of the pre-filter regardless of the bandwidth of the pre-filter. When the bandwidth of the prefilter is decreased, the performance decreases because some of the spectral components which could otherwise suppress IFWM are not transmitted through the fiber transmission link. The differential Q for 0, π/2 and π-APRZ modulation formats versus the bandwidth of the pre-filter is shown in Figure 7. From Figure 7, we notice that up to 100 GHz of bandwidth the Q decreases uniformly for all 0, π/2 and π-APRZ modulation formats up to 1 dB. After that, the Q decreases at a much rapid rate for π/2-APRZ versus other modulation formats. This shows that in order to obtain the efficient capability of the π/2APRZ modulation format to suppress IFWM, more than 95% energy (95% energy bandwidth is ~96 GHz) of the signal needs to be transmitted through the fiber.

modulation format loses its capability to suppress IFWM so a viable Q performance could not be obtained. At the channel spacing of 100 GHz, a performance improvement of ~2.3 dB is obtained using the pre-filter for the π/2-APRZ modulation format (Figure 8). Therefore, the π/2-APRZ modulation format using the pre-filter could achieve a Q of 11.7 dB with 100GHz channel spacing for a 44 Gb/s data rate per channel. When combined with the 10% overhead FEC gain, the performance could prove to be a viable option for transoceanic systems with 0.4 Gb/s/Hz spectral efficiency [20]. We also repeated this exercise for 0 and π-APRZ modulated systems. The Q improvement at 100 GHz was less than 1 dB by using the pre-filter. I. CONCLUSION In conclusion, we have explored the feasibility of 44 Gb/s WDM transmission over a 6000 km transoceanic link using Alternate-Phase RZ modulation formats and Raman amplifiers. We have found that the π/2-AP modulation format outperforms regular RZ (0-AP) and carrier suppressed RZ (πAP) by more than 3 dB. The improved performance of the π/2APRZ modulation format could enable successful 40Gb/s WDM transmission over transoceanic distances with sufficient system margin for 200 GHz channel spacing. We further explored the feasibility of the π/2-AP modulation format with 100 GHz channel spacing for 40Gb/s transoceanic distances. We concluded that by using pre-filtering and 10% overhead FEC gain, it may become feasible to transmit 40 Gb/s WDM channels over a 6000 km transmission link with 0.4 Gb/s/Hz spectral efficiency. REFERENCES [1]

Optimal Q (dB)

14 With Pre Filter

[2]

12 Without Pre Filter

10 8 100

[3]

120

140

160

channel spacing (GHz)

180

200

Figure 8. Optimal Q versus channel spacing for π/2-APRZ with and without pre-filter. The transmission distance is 6000 km.

Finally, we repeated the WDM simulations with the prefilter for the π/2-APRZ modulation format by varying channel spacing. The optimal Q versus channel spacing is shown in Figure 8 for the π/2-APRZ modulation format with and without the pre-filter. Please note that pre-filter bandwidth is optimized for each channel spacing to obtain the optimal Q. As the channel spacing is decreased up to 100 GHz channel spacing, the improvement using the pre-filter is more pronounced due to minimized linear crosstalk. When channel spacing was decreased beyond 100 GHz, the π/2-APRZ

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[21] Idan Mandelbaum and Maxim Bolshtyansky, “ Raman Amplifier Model in Single mode optical fiber”, IEEE Photonics Technology Letters, vol. 15, no. 12, pp 1704-1706, December 2003. [22] Howard Kidorf, Karsten Rottwitt, Morten Nissov, Matthew Ma and Eric Rabarijaona, “Pump Interactions in a 100-nm Bandwidth Raman Amplifier”, IEEE Photonics Technology Letters, vol. 11, pp. 530-532, May 1999 [23] Neal S. Bergano, “Undersea communications systems”, in Optical Fiber Telecommunication IVB Systems and Impairments, Ivan Kaminow and Tingye Li, Eds. San Diego, CA: Academic Press 2002, pp154-197. [24] Y. Miyamoto, A. Hirano, K. Yonenaga, A. Sano, H. Toba, K. Murata and O. Mitomi, “320Gbit/s (8 x 40 Gbit/s) WDM transmission over 367 km with 120 km repeater spacing using carrier-suppressed return-to-zero format”, Electronic Letters, Vol. 35 No. 23, pp. 2041-2042, November 1999. [25] Anes Hodžic´, Beate Konrad and Klaus Petermann, “Alternative Modulation Formats in N×40 Gb/s WDM Standard Fiber RZTransmission Systems”, Journal of Lightwave Technology, Vol. 20, No. 4, pp. 598-607 April 2002.

ISAST Transactions on Communications and Networking, No.1 Vol. 2, 2008 Shamila Makki and Subbarao Wunnava: Next Generation Networks and Code Mobility

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Next Generation Networks and Code Mobility Shamila Makki, Subbarao Wunnava, IEEE Member Abstract The next generation networks are packet-based networks and they transmit a huge amount of voice and data. The networks continue to increase in size, complexity and importance through developing connections of a variety of devices with different architectures to the networks. They also need new software for handling data, incorporating and integrating the existing communication technologies. In many cases, centralized model of network management are unsuitable and incapable to serve large and different group of networks. Instead of one centralized and huge system for managing a network, several smaller systems with agents, will be required to manage and control the networks in a common way. Mobile agents offer many possibilities for designing the next generation of networks, and thus allowing them to be managed and used to their full potentials. This paper describes the integration of the next generation networks with mobile codes. It will also identify the key points of using mobile agents for managing next generation networks. Key Words: Centralized Systems, Distributed Network Management, Mobile Agents, Next Generation Networks.

1. Introduction In recent years telecommunication networks have changed from time division multiplexing and circuit switched networks to next generation networks. The next generation network is known as packet and frame based networks and they need to transmit high speed voice and data in integrated packets-based manner. The services, network architectures and traffic patterns in the next generation networks will extensively be different from the current networks. Therefore heterogeneity and complexity of next generation networks carry a number of challenges for its network management. These networks need to manage and control a huge number of different technologies and set up flexible, platform independent applications. Distributed systems and distributed algorithms have become obvious as a way to deal with size and complexity in networks. The two best recognized methods for implementing distributed algorithms are active networks and agent-based systems [1]. In the case of agents, this usually occurs at the application layer,

while in active networks the entire network itself becomes programmable, including the network layer using active packets. The term mobile code explains any program that can be sent un-changed to a heterogeneous collection of processors and executed with the same semantics on each processor. Mobile code supports a flexible form of distributed systems, where the desired non-local computations do not have to be known in advance at the execution site [2]. The remainder of this paper is organized as follow: Section 2, reviews the related works regarding moving towards the decentralization in communicating applications. Section 3, explains next generation networks and code mobility. Section 4, identifies the problems in centralised network management systems by utilizing mobile agents. Section 5, describes distributed network management and introduces the key points of employing mobile agents in this environment. Finally, section 6, provides the conclusions.

2. Related works The next generation networks consist of various access technologies and the growing complexity of such networks requires the use of complicated management techniques. Centralized network management systems based on client/server is known by a low degree of scalability and flexibility, and can not answer to the needs of today’s telecommunication networks. Thus, it requires moving toward decentralization in this kind of communicating applications with approaches like management by delegation, Common Object Request Broker Architecture (CORBA), Web-based management, intelligent agents, active networks and mobile agent technology, in order to solve most of the problems that exist in centralized network management. The early work toward decentralized management used different style of management [3]. They have started with management by delegation and automation of management tasks by dynamically delegating management roles to stationary agents. They use a decentralized model that takes advantage of the increased computational power in network agents and decrease pressure on centralized network management. Goldszmidit et al. [4] present a more flexible model as the manager-agent delegation framework. It supports the ability to extend the functionality of servers (agents) at

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execution time, allowing flexible distribution of management responsibilities in a distributed environment. CORBA is an open distributed object computing infrastructure being standardized by the Object Management Group (OMG). It offers equal mechanisms for communication between distributed objects. CORBA can be used to implement real-life heterogeneous distributed applications [5]. CORBA can be seen as a basis providing interoperability for distributed system status, system reconfiguration, prompt reaction to malfunctions and failures [6]. Web-based management also integrates all types of information systems into the Internet or intranet environments. Internet technology provides a perfect user-interface for management applications, whereas the data, which is put on a web server, is directly available from everywhere on the network. Web-based management is significant approach for monitoring and managing the distributed networks [7]. Intelligent agent ideas and technologies have been widely influenced in telecommunications industry. Generally, the term intelligent agent ranges from adaptive user interfaces, recognized as interface agents, to communities of intelligent processes that cooperative with each other to achieve a common task [8]. Active networks are a framework where transporting components such as routers and switches, are programmable and able to execute arbitrary code in order to achieve higher flexibility and to present new capabilities. This code is provided in some systems by special network packets (active packets), and injected into the network by normal users [9]. Mobile agents representing transportable or even active objects, they may move from one system to another to access remote resources or even meet other agents and cooperate with them. They provide a huge number of complicated services in large network systems as shown in Figure 1.

Figure 1: A wireless node and a mobile agent model.

Mobile agents use object oriented programming languages (e.g. Java) that makes them very simple to implement. Mobile agent model fits very well into the conceptual foundations of distributed network management [10].

3. Next generation networks and code mobility The next generation networks are characterized by growing worldwide communications, increasing mobility (mobility of human, devices, and software) in our global society, accessing and exchanging information at anytime, anywhere and with any volume, the integration of voice and data transmission and computing. The next generation networks allow the end users to access large bandwidth with good flexibility services. Although, this is dependent on an end-to-end Etherent networking infrastructure and its development to carry voice, video and data at wire speed with an expected quality of service including satisfactory bandwidth, throughput, reliability, superficial quality, and reducing costs [11]. As the networks are becoming more complex and dynamic, new ways of designing and managing them are required. The attention about code mobility has been increased mostly by a new family of programming languages, generally called mobile code languages [13, 14]. Mobile codes are recognized as software that moves to a heterogeneous networks environment, crosses different administration domains, and are automatically executed upon arrival at their destinations [12]. Mobile code models do not connect the code that performs services to one or more hosts, somewhat they permit migration of code describing a service and probably the associated state of execution to a different host, enabling the design of distributed mobile code applications. The languages belonging to this family are being developed both in industry and academia. The most well-known mobile code languages are Telescript by General Magic [13], and Java by Sun Microsystems [14]. These mobile code languages can be interpreted and allow codes to continue to exist over heterogeneity. Thus, tend to be independent of the operating system and hardware architecture; also they present good platforms for code mobility. Mobile codes are classified as follow [15]: 1) Remote Evaluation, client can send the code describing the service to the server. The server owns the resources and provides an environment to execute the client’s code. 2) Code on demand, client downloads required code from a code server and initialises it in order to do a task. Thus, the client owns the resources needed by a task but lacks the required code to do it. 3) Mobile agent, mobile agent owns the code to perform a service but does not own the resources needed to accomplish it.

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Programs that use mobility as a mechanism to adapt to resource changes have three requirements that are not shared with other mobile programs. First, they need to monitor the level and quality of resources in their operating environment. Second, they need to be able to react to changes in resource availability. Third, they need to be able to control the way in which resources are used on their behalf (by libraries and other support code) [16]. Some of the advantages of mobile codes are [17]: 1) Efficiency: when the latency of the network is high, it is efficient to send the computation to the remote site and to interact locally instead of repeated interactions with a remote site. 2) Simplicity and flexibility: the maintenance of a network is simpler, since the applications are placed on a server and the clients themselves download applications automatically. 3) Storage: clients can load a code on site whenever they want, rather than having all the codes duplicated on all sites. Thus reducing the total storage requirement. Mobile agents are program instances or processes that can reside in some environment, and move their codes and states from host to host and act autonomously on behalf of their users [18]. The next generation technologies like agent technology reduce the costs of delivering existing services and increase the ability of delivering new, faster and integrated services [19]. Therefore, mobile agents are recognized as useful and applicable technologies for distributed systems and network management. Mobile agent-based network management can reduce the network traffic, saving of network bandwidth and increasing of the overall performance by allowing the application to process data on or near the source of data, and easily support disconnected operation. Adapting the mobile agent technology reduces the need for the administrator to regularly supervise many network management activities e.g. installation and upgrading of software and cyclic auditing of the network [20].

4. Identifying problems in centralised network management systems by utilizing mobile agents The network management has traditionally concentrated on the physical level. Nevertheless, with the advent of network technologies the network management will need to focus more on the logical level which allows more flexibility, scalability and improved integration. Generally network management system contains these sections, network management station, agents running on managed nodes, management protocols, and management information. Existing network management applications are based on one of these two protocols, Simple Network Management Protocol (SNMP) [21] and Common

Management Information Protocol (CMIP) [22]. They both address the problem of interoperability in heterogeneous environments. These protocols are based on static and centralized client/server model, where every element of the network sends all the data to a central location. In centralize management system agents monitor the system and collect data, which can be accessed by applications via management protocols. This system is highly dependent on central management station and it is the only spot of failure. Sometimes the management host does not fail, but a fault appears in the networks, causes the other part of the networks to be without any management functionality. For this reason, the management applications are not robust, flexible, and hard to be modified or improved. Centralize management systems produce too much traffic in networks as shown in Figure 2.

Figure 2: A central network server queries the network’s components. However the agents are simple and normally only communicate when they have to respond to queries for management information base. Also they are not able of doing self-management when global knowledge is required [23]. Centralize management systems follow a platform-centered model and are not scalable therefore, if the number of devices and managed variables, or speed of the network increases or if management communications rates are restricted, then the system quickly becomes unmanageable. The only advantage of the centralized systems is that, applications are not concern about protocol complexity and heterogeneity [24]. The growing complexity of networks in next generation networks needs the development of existing management models towards distributed management intelligence in order to overcome the limitations of centralised network management system. In distributed network management there is not one central network manager and the manager acts as peers. One of the goals of next generation networks is to present a universal and flexible service architecture that can support multiple types of services and management applications. Mobile agent technology is

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the best candidate for implementing distributed intelligent management system which is an answer to the quality of service control, flexibility, and scalability problems of centralized models. The main objective of mobile agent based distributed computing is to minimize the volume of networks’ traffic exchanged between systems while maintaining relatively low task execution time, especially for time-critical tasks [25]. There are different parameters to allow agents to move across a network [26]: 1) Common execution language, in order a process to move from one host to another, both hosts must use a common execution language. Mobile agency is a heterogeneous environment, thus an interpreted scripting language can execute machine code. 2) Process persistence, in order a process to move to a remote machine, agent must be able to save its execution state, or generate a new process whose execution state will be saved. The process persistence would be built into the mobile agent language or architecture. 3) Communication mechanism, in order an agent to move within a network some communication mechanisms (Transmission Control Protocol/ Internet Protocol (TCP/IP)), or a higher level communication protocol (Hypertext transfer protocol (HTTP)) must exist between agent hosts. 4) Security, it is critical when executable code is moved in a network. Malicious or badly written codes attack to unsuspecting host and destructive hosts look for to break down or modify the agents [27]. A failure of the client/server model is the incapability of servers to communicate with clients. Mobile agents are considered to be peer entities and they can adopt in any position that is most suitable to their current needs. This allows for having flexibility in dealing with network entities and distributed networks.

5. Distributed network management and the key points of employing mobile agents Centralized network management is inefficient because every management action depends on the network management system. The use of mobile code has been introduced in network management as an alternative to the more traditional centralized approach, thus the network management station regularly accesses the data collected by a set of software component placed on the network elements, by using a suitable protocol. The centralized paradigm adapted by the SNMP is appropriate in several network management applications, but the quick expansion of networks has created the problem of the scalability [25]. The CMIP‘s agent is not an intelligent or mobile agent, but it has more functionalities and autonomy compared with an SNMP‘s agent. In addition, some researchers have used Internet remote monitoring

(RMON) agents to distribute management intelligence in order to solve these problems. Nevertheless, these solutions have had insufficient achievement, and they are not capable to deal with the restrictions of centralized network as the networks grow [28]. The centralized network management system implemented by the SNMP has focused on the physical level. Although, with the increase to the number of managed elements, it needs to concentrate on the logical level in order to improve the problems of scalability, flexibility. That is, because the manager relies on checking each element or polling to collect the information. In addition a huge amount of information continuously is sent to the manager, which it needs to control and process. Thus, these would increase networks traffic. Therefore they have made feasible the idea of distributed management. Furthermore, the appearances of large-scale and composite networks are starting to change the centralized network management architecture toward the distributed network management. Distributed network management offers several advantages [29]: 1) Distributed network management is inherently more robust without depending on continuous communications between the network management systems and network elements. 2) Networks’ traffic and processing load in the network management systems can be both reduced by performing data processing closer to the network elements. 3) Searches can be performed closer to the data, improving speed and efficiency. 4) Scalability to large networks is improved. 5) Processing capabilities for routers and switches have improved considerably and the popularizations of Java and CORBA have brought mobile code concepts closer to mainstream acceptance [30]. The paradigm of moving management logic close to the data is a technique that has been considered early in development of management architectures, the relevant framework known as management by delegation [31]. The ability of task delegation in network management allows the utilization of mobile agents for effective usage of all the resources of network, minimization of down time and reduction of the cost of network operations [32]. Agents can be characterized by the following attributes [33]: 1) Agent intelligence quality indicates the process that is used for developing the agent logic or intelligence, and is directly related to agent languages. 2) Mobile agents provide an asynchronous task execution. Thus, the dependency between clients and server applications can be reduced and an automation of a task processing is introduced.


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3) Mobile agent cooperation shows that the agent system allows for cooperation between agent entities. The difficulty of cooperation may range from a client/server method of interaction to negotiations and cooperation based on artificial intelligence techniques. 4) Agent mobility is accepted as the most challenging property of mobile agent and it is the ability to change its physical location by moving through network in order to do their tasks and is able to control the traditional way of communications. Agents may be transported to remote sites in a particular run-time environment [34].

6. Conclusions As the next generation networks become more complex and distributed, centralized network management systems are more exposed to failure, poor performance, security issues, lack of efficient management, restrictions for real time response and limited scalability. Therefore, they require moving towards the decentralized intelligent network management to overcome these shortcomings. The application of mobile codes for network management allows us to overcome some limitations of centralized management system. Mobile agent is a competent model in distributed systems and distributed network management. The important point is that the traditional management architecture can not work well with the growing complexity of the current networks. As they require some level of flexibility and dynamic adaptation that it is one of the major bases for using the mobile agent technology. In addition the key features of mobile agents that differentiate them from traditional distributed programming are autonomous, intelligence, mobility, flexibility, communicative, goal oriented, and ability to learn. They can overcome certain limitations of the traditional client/server model, distributed systems and improve management of the networks.

References [1] S. S. Manvi, P. Venkataram, “Application of Agent [2] [3]

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Technology in Communications: a Review”, Computer Communications, Elsevier, Vol. 27, pp. 1493-1508, 2004. T. Thorn, “Programming Languages for Mobile Code”, ACM Computing Surveys Vol. 29, pp. 213-239, 1999. G. Goldszmidt, Y. Yemini, and S. Yemini, “Network Management by Delegation”, Proceedings of the 2nd Int. Symposium on Integrated Network Management (ISINM’91), Washington, DC, 1991. G. Goldszmidt, “Distributed Management by Delegation”, In Proceedings of the 15th International conference on Distributed Computing Systems, 1995. S. Vinoski, “CORBA: Integrating Diverse Applications Within Distribute Heterogeneous Environments”, IEEE Communications Magazine, Vol. 35, No. 2, 1997.

[6] S. Lipperts, “CORBA for Inter-Agent Communiocation of Management Information”, In 5th International Workshop on Mobile Multimedia Communication, 1998. [7] A. Liotta, G. Knight, and G. Pavlou, “Modeling Network and System Monitoring Over the Internet with Mobile Agents”, IEEE, 1998. [8] T. Magedanz, K. Rothermel, and S. Krause, “Intelligent Agents: An Emerging Technology for Next Generation Telecommunications”, INFOCOM ’96, pp.24-28, 1996. [9] D. Raz, B. Shavitt, “An Active Network Approach for Efficient Network Management”, Proceedings of the 1st International Working Conference on Active Networks (IWAN’99), LNCS Vol. 1653, pp. 220-231, 1999.

[10] D. Gavalas, D. Greenwood, M. Ghanbari, and M. O’ Mahony, “Advanced Network Monitoring Applications Based on Mobile/Intelligent Agent Technology”, Computer Communication, Vol. 23, pp. 720-730, 2000. [11] R. S. Gray, G. Cybenko, D. Kotz, R. A. Peterson, and D. Rus, “D’ Agents: Applications and Performance of a Mobile-Agent System”, Software Practice and Experience, Vol. 32, No. 6, pp. 543-573, 2001. [12] A. Acharya, M. Ranganathan, and J. Saltz, “Dynamic linking for mobile programs, In Mobile Object Systems: Towards the Programmable Internet”, pp. 245-262, Springer-Verlag, 1997. [13] J. E. White, “Telescript Technology: The Foundation for the Electronic Marketplace”, General Magic White Paper, 1994. [14] B. Joy et. al., “The Java Language Specification”, 2nd ed. Reading, MA: Addison Wesley, 2000. [15] A. Fuggeta, G. P. Picco, and G. Vigna, “Understanding Code Mobility”, IEEE Transactions on Software Engineering, Vol. 24, No. 5, pp. 436-361, 1998. [16] M. Baldi, S. Gai, and G. P. Picco, “Exploiting Code Mobility in Decentralized and Flexible Network Management, Proceedings of the 1st International Workshop on Mobile Agents (MA’97), LNCS Vol. 1219, pp. 13-26, 1997. [17] S. Papavassiliou, A. Puliafito, O. Tomarchio, and J. Ye, “ Mobile agent-Based Approach for Efficient Network Management and Resource Allocation: Framework and Applications”, IEEE journal on Selected Areas in Communications, Vol. 20, No. 4, 2002. [18] I. Satoh, “Software Testing for Wireless Mobile Computing”, IEEE Wireless Communications, Vol. 11, No.5, pp.58-64, IEEE Communication Society Press, 2004. [19] H. Chamas, W. Bjorkman, and M.A. Ali, “A Novel Admission Control Scheme for Ethernet Services”, Proceeding of IEEE ICC 2005. [20] M. D’Arienzo, A. Pescape, and G. Ventre, “Dynamic Service Management in Heterogeneous Networks”, JNSM: Vol. 12, No.3, 2004. [21] B. Pagurek, Y. Wang, and T. White, “Integration of Mobile Agents with SNMP: Why and How”, Network Operations and Management Symposium (NOMS 2000), J.W. Hong and R. Weihnayer, Eds, IEEE/IFIP, pp. 609-622, 2000. [22] ISO/IEC 9596, Information Technology, Open Systems Interconnection, Common Management Information Protocol (CMIP) – Part 1: Specification, Geneva, Switzerland, 1991. [23] S. S. Manvi, P. Venkataram, “An Agent Based Adaptive Bandwidth Allocation Scheme for Multimedia

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Applications”, Journal of Systems and Software, Vol. 75, No. 3, pp. 305-318, 2005. [24] I. Satoh, “Building reusable mobile agents for network management”, IEEE Transactions on System, Man, and Cybernetics, Part C Vol. 33, No.3, pp. 350-357, 2003. [25] T. Fahad, S. Yousef, and C. Strange, “A study on the Behavior of the Mobile Agent in the Network Management Systems”, ISBN: 1-9025-6009-4, 2003. [26] A. R. Tripathi, T. Ahmed, and N.M. Karnik, “Experiences and Future Challenges in Mobile Agent Programming”, Microprocessors and Microsystems, Vol. 25, pp. 121-129, 2001. [27] R. Stephan, P. Ray, and N. Paramesh, “Network Management Platform Based on Mobile Agents”, International Journal of Network Management, Vol. 14, pp. 59-73, 2004. [28] A. L. G. Hayzelden, J. Bigham, S. J. Poslad, and P. Buckle, “Communications Systems Driven by Software Agent Technology”, Journal of Network and Systems Management, Vol. 8, No.3, 2000. [29] T. M. M. Cheikhrouhou, P. Conti, K. Marcus, and J. Labetoulle, “ A software Agent Architecture for Network Management: Case Studies”, Journal of Network and Systems Management, Springer Netherlands, Vol. 8, No.3, pp. 349-372, 2000. [30] T. C. Du, E. Y. Li, and A.P. Chang, “Mobile Agents in Distributed Network Management”, Communications of the ACM, Vol. 46, No.7, 2003. [31] H. E. Bal, J. G. Steiner, and A. S. Tanenbaum, “Programming Languages for Distributed Computing Systems”, ACM Computing Surveys, Vol. 21. No.3. 1998. [32] W. Jansen, “Countermeasures for mobile agent security”, In computer Communications, Special Issue on Advances in Research and Application of Network Security, 2000. [33] P. Marques, P. Simes, L. Silva, F. Boavida and J. Silva, "Providing Applications with Mobile Agent Technology", in Proceedings of the 2001 IEEE Open Architectures and Network Programming Conference (OpenArch'2001), IEEE Computer Society Press, pp. 129-136, 2001. [34] M. D’Arienzo, A. Pescape, and G. Ventre, “Dynamic Service Management in Heterogeneous Networks”, JNSM: Vol. 12, No. 3, 2004.

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ISAST Transactions on Communications and Networking, No.1 Vol. 2, 2008 P.Priakanth and Dr. P.Thangaraj: A Traffic Aware Adaptive Scheduling MAC Protocol for Energy Efficiency and Fairness in Mobile Ad hoc Networks

A Traffic Aware Adaptive Scheduling MAC Protocol for Energy Efficiency and Fairness in Mobile Ad hoc Networks P.Priakanth and Dr.P.Thangaraj

Abstract—In the design of ad-hoc networks, Energy preservation and fairness are the major necessities. The network throughput can degrade under heavy traffic in existing MAC protocols, because the duty cycle is fixed. Unnecessary energy utilization may take place, under light loads. In this paper, we propose a Traffic Aware Scheduling MAC (TA-MAC) protocol for mobile adhoc networks. According to the node’s own traffic and of its neighbors, the sleep-wakeup cycles of the nodes are adaptively determined in this protocol. This protocol also comprises an efficient transmission control technique to accomplish fairness, by recognizing the contention. The proposed protocol attains more power savings under different loads and network sizes compared to the existing MAC protocols. This is revealed through the analytical and experimental results. Simply the nodes engaged in communication wake up regularly and for this reason, energy is conserved in other nodes. The simulation results show that our proposed protocol provide substantial energy savings, better fairness, increase battery life of network nodes, enhance packet delivery ratio. Index Terms—MAC, Sleep, Scheduling, Energy, Traffic.

I.INTRODUCTION

R

EHABILITATED attention is been acknowledged by Adhoc wireless networking. Although, permits several motivating usage scenarios, it poses a number of challenges. Wireless networking has been applied to cellular telephony and Internet connectivity through radio modems customarily. To a fixed wired base station, these systems provide one hop connectivity. Without pre-configured network topologies, adhoc wireless network systems try to form multi-hop networks. Unlike in cellular networks where nodes are in touch with a centralized base station, there is peer-to-peer communication among nodes. Vigorously changing topologies typifies Ad-hoc networks. These topologies are direct outcome of the mobility of the nodes. Several benefits could be presented by such systems. Through the usage area, they never depend on widespread and costly installations of fixed base stations. Based on different metrics like robustness and energy expenditure, they can carry out route selection with the availability of multiple routes to the same node or base station. Rather than using a distant base station, nodes can correspond directly with each other when possible. This ca1n help to preserve energy and improve throughput. To backing communication in military combat zone and inhabitant

disaster recuperation scenarios, these systems enable a variety of applications arraying from the observing of droves of animals. Nodes, being mobile and deployed with slight network planning are required by several of these applications. Their size and the energy reserves accessible to them are restricted through the mobility of nodes. Therefore, in the design of adhoc networks, energy preservation is a main requirement. Dependening on each mobile node’s battery capability, an ad hoc network’s life span is specified. Any of three different modes [17] could be activated by the network interface hardware at a receiver node: • Active mode: can transmit or receive data packet. • Idle mode: only listen through receiver and can’t transmit any packet through transceiver. • Sleep mode: neither transmits nor receive packet. Significantly more energy is consumed in the sleep mode, though it is less than the active mode Considerable energy can be saved, by effectively switching to sleep mode whenever possible. The network throughput can degarde under heavy traffic in existing MAC protocols, since the duty cycle is fixed. Unnecessary energy utilization may take place, under light loads. In this paper, we propose a Traffic Aware Scheduling MAC (TA-MAC) protocol for mobile adhoc networks. According to the node’s own traffic and of its neighbors, the sleep-wakeup cycles of the nodes are adaptively determined in this protocol. This protocol also comprises an efficient transmission control technique to accomplish fairness, by recognizing the contention. The proposed protocol attains more power savings under different loads and network sizes compared to the existing MAC protocols. This is revealed through the analytical and experimental results. Simply the nodes engaged in communication wake up regularly and for this reason, energy is conserved in other nodes. Also better fairness is also achieved. The rest of the paper is organized as follows. Section 2 discusses the related work. Section 3 presents our proposed protocol. In Section 4, we present the analytical model and in Section 5, we compare the protocol with SMAC and 802.11 MAC protocols through NS2 simulations. Section 6 concludes the paper.

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II. RELATED WORK Borja Peleato et al. [1] have proposed a medium access control (MAC) protocol that is suitable for non-synchronized ad-hoc networks, and in particular for the energy-constrained underwater acoustic net-works which are characterized by long propagation delays. Stephan Mank et al. [2] have introduced Mobile LMAC, a novel TDMA protocol for mobile wireless sensor nodes and their Mobile LMAC is based on the LMAC protocol but in their scheme each node can spontaneously establish a TDMA schedule on demand or join/leave existing schedules while nodes are moving. Yu-Chee Tseng et al. [3] developed a scheme called GRID; by which a mobile host can easily determine which channel to use based on its current location for considers the channel assignment problem in a multi-channel MANET environment and they show that this can improve the channel reuse ratio then propose a multi-channel MAC protocol, which integrates GRID. S Gowrishankar et al. [4] have studied the effects of various random mobility models on the performance of AODV for experimental purposes, we have considered three mobility scenarios and their experimental results illustrate that performance of the routing protocol varies across different parameters like number of nodes, packet delivery ratio and end to end delay. Zeyad M. Alfawaer et al. [5] gives MANHSI (Multicast for Ad hoe Network with hybrid Swarm Intelligence) protocol, which relies on swarm intelligence based optimization technique to learn and discover efficient multicast connectivity and their proposed protocol instances that it can quickly and efficiently establish initial multicast connectivity and/or improved the resulting connectivity via different optimization techniques. Umut Akyol et al. [6] study the problem of jointly performing scheduling and congestion control in mobile adhoc networks so that network queues remain bounded and the resulting flow rates satisfy an associated network utility maximization problem and they described the wGPD protocol for combined congestion control and scheduling in wireless ad-hoc networks and compared its performance with the standard 802.11+TCP protocols via simulation. Chiung-Ying Wang et al. [7] propose an efficient power saving protocol for multi-hop mobile ad hoc networks, called p-MANET and their p-MANET consists of two mechanisms. First, the efficient power saving mechanism avoids power consumption on unnecessary tasks. Next, the low latency next hop selection mechanism provides heuristic strategies to efficiently select next hop neighbor node on packet forwarding. Benjie Chen et al. [8] presents Span, a power saving technique for multi-hop ad hoc wireless networks that reduces energy consumption without significantly diminishing the capacity or connectivity of the network and they give randomized algorithm where coordinates rotate with time, demonstrating how localized node decisions lead to a connected, capacity preserving global topology.

Ya Xu et al. [9] introduced a geographical adaptive fidelity (GAF) algorithm that reduces energy consumption in ad hoc wireless networks that their GAF conserves energy by identifying nodes that are equivalent from a routing perspective and then turning of unnecessary nodes, keeping a constant level of fidelity. Joseph Y. Halpern and Li (Erran) Li [10] consider how to adjust a node’s transmission power to minimize its energy consumption and improve network performance in terms of network lifetime and throughput and they discuss how network lifetime can be increased, the subtleties of defining it precisely, and the difficulties of achieving optimal network performance in practice. Wen-Zhan Song et al. [11] proposed several novel localized algorithms that construct energy efficient routing structures, where each node has a bounded degree and the structures are planar, for wireless ad hoc networks modelled by unit disk graph (UDG) and they conducted extensive simulations to study these new sparse network topologies and compared them with previously known efficient structures. Roger Wattenhofer et al. [12] developed a simple distributed algorithm where each node makes local decisions about its transmission power and these local decisions collectively guarantee global connectivity and they give an approximation scheme in which the power consumption of each route can be made arbitrarily close to the optimal by carefully choosing the parameters. Ya Xu et al. [13] introduced a geographical adaptive fidelity (GAF) algorithm that reduces energy consumption in ad hoc wireless networks that their GAF conserves energy by identifying nodes that are equivalent from a routing perspective and then turning of unnecessary nodes, keeping a constant level of fidelity. EunSun Jung et al. [14] proposes a power control protocol which does not degrade throughput and yields energy saving for present a power control MAC protocol that allows nodes to vary transmit power level on a per-packet basis and they show that these schemes can degrade network throughput and can result in higher energy consumption than when using IEEE 802.11 without power control. Alberto Cerpa and et al. [15] have described the design, implementation, analysis, simulation, and experimental evaluation of ASCENT, an adaptive self-configuration topology mechanism for distributed wireless sensor networks and their paper reports on results from experiments using real radios, demonstrating the importance of self-configuring techniques that react to the operating conditions measured locally. Ali Assarian et al. [16] propose an efficient power saving protocol for multi-hop mobile ad hoc networks. The main goals of this paper are to reduce and balance power consumption, and extending the lifetime of the network without partitioning. Also a dynamic method calculates sleep duration of each node. In fact this algorithm is executed in a fully distributed way within the limits of a cluster.


50

Fig.2 Example of Vss

III. PROPOSED PROTOCOL Let the network has n nodes N1, N2, N3…Nn. Let the time period P has k slots t1, t2, t3…tk. Let the pattern bit 0 indicates sleep mode and 1 indicates awake mode. Then we form the matrix M1 for the period P1 containing the sleep wakeup bits for all the nodes. This matrix will be generated at the beginning of each time period and will be updated during every slot. At the end of the current period, this matrix will be exchanged by all the nodes. Then based on the values of the matrix, our MAC protocol will prepare the schedule for each node. Based on this schedule, each node may be in sleep mode when there is no traffic or in awake mode when there is a traffic. Thus depending on the traffic conditions, only those nodes, which participate in the data traffic, will be awake and other nodes are put in sleep mode, there by saving the energy. A. Generating the Sleep-Status-Matrix

ns nr Vssns Ts N S ns S nr Psn ti N rn

Sending Node

Receiving Node

Sleep Status Vector for node

Size of Vss of node n s is calculate as follows . bai

Awake indication bit

Ts

Time slot

ti

Time Interval during any slot

Vssn = Size(Ts ) / ti + 1 The first bit represents awake indication bit Let the power status of sending node ns

(bai ) let it be 0,

( S ns ) be 1 and let

there be a packet in it to be sent to a receiver. If the power status of receiving node ( S nr ) is also 1 then the schedule status for node n is set to 1. That is the node should be awaken hence schedule will be 1 during that period of time of Let

Ts .

bai be 1, Let the power status of sending node n s

( S ns ) be 1 and let there be a packet in it to be sent to a

ns

receiver. If the power status of receiving node

Time slot

( S nr ) is 0 then

No of time slot in each period

the schedule status for node n is set to 1.That is the node should be awaken hence schedule will be 1 during that period

Power status of sending node

of time of

Power status of receiving node

Packet to send

Let

Ts .


( S ns ) be 0 and let there is no packet to be sent to a receiver. If

Time Interval during any Slot

the power status of receiving node

Total no of neighbor nodes

schedule status for node n is set to 0.That is the node should be sleep hence schedule will be 0 during that period of time of Ts .

A sleep-status-matrix vectors (Vss ) .

( M ss ) is collection of sleep-status-

Vss contains collection of bits indicating the

tentative sleep wakeup plan for a sensor node over several slot times. First bit in

Vss represents wakeup alert to the receiving

node nr . 0 denotes neighbor node should sleep for the next and 1 denotes sleep.

Ts

Let

( S nr ) is also 1 then the


( S ns ) be 0 and let there be a packet in it to be sent to a receiver. If the power status of receiving node

( S nr ) is also 0

then the schedule status for node n is set to 1.That is the node should be awaken hence schedule will be 1 during that period of time of Let

Ts .

bai be 0, Let the power status of sending node ns ( S ns )

be 1 and let there is no packet to be sent to a receiver. If the power status of receiving node

Fig.1 Example of

M ss

Following figure shows the sample

Vss for node ns


schedule status for node n is set to 0.That is the node should be sleep hence schedule will be 0 during that period of time of

Ts . Let


( S ns ) be 0 and let there is no packet to be sent to a receiver. If the power status of receiving node


schedule status for node n is set to 0.That is the node should

51


be sleep hence schedule will be 0 during that period of time of

Ts

B. Detailed Examples Example for Calculating size of

Let Size (Ts ) = 8 Ti = 2 bai = 1 Vssn = 8 / 2 + 1 Size of Vss = 5 Example 1:

Example 2:

Example 3:

Vss

C. Algorithm for Schedule and Generation

Vssns

Sleep Status Vector for node ns

Ts N S ns

Time slot

No of time slot in each period

Power status of sending node

S nr

Power status of receiving node

Psn

Packet to send

Ts

Time Slot

ti

Time Interval during any Slot

N rn

Total no of receiver’s nodes

52

D. Fairness in


TA − MAC

Initially, Every node forwards its frame size and slot number to its two-hop neighborhood. Thus, a node knows about the slot and frame information of its one-hop and twohop neighbors at the beginning of the TA − MAC phase. In TA − MAC , a node can be in one of two modes: NonBusy Mode (NBM) or Busy Mode (BM). Whenever a node receives a contention notification (CN) message from its twohop neighbors, it will be in BM. Otherwise the node is in NBM. In NBM, any node can compete to transmit in any slot, but in BM, only the holders of the current slot and their one-hop neighbors are allowed to compete for the channel access. In NBM, If a slot does not contain its holder or its holder does not have data to send, non-holders can send their data through the slot. This feature achieves high channel utilization even under low contention as a node can transmit as soon as the channel is available. Transmission Control As a node i acquires data to transmit, it checks whether it is the owner of the current slot. If it is the owner of the slot, it takes a random backoff within a fixed time period To. When the backoff timer expires, it runs Clear Channel Assessment (CCA) [17] and if the channel is clear, it transmits the data. If the channel is not clear, then it waits until the channel is not busy and repeats the above process. If node i is a non-owner of the current slot and it is in NBM, or if it is in BM and the current slot is not owned by its two-hop neighbors, then it waits for to and then performs a random backoff. Within a contention window [To, Tno]. When the backoff timer expires, it runs CCA and if the channel is clear, then it starts transmission. If the channel is not clear, then it waits until the channel is clear, and repeats the above process. If node i is a non-holder of the current slot and is in BM, postpones its transmission (it may sleep) until it finds a time slot that either (1) is not owned by a two-hop neighbor or (2) is its owner. After waking up, it repeats the above process. A node i verifies the holder of the recent slot, as it obtains data to broadcast. It obtains a arbitrary retreat inside a preset time period To, if it is the holder of the slot. It precedes Clear Channel Assessment (CCA) [17] as soon as the retreat timer finishes. Furthermore, it broadcasts the data if the channel is apparent. The aforementioned process is reiterated subsequent to waiting till the channel becomes free, if the channel is not clear. Subsequently, it stays for to and then performs an arbitrary backoff, if node i is in NBM or in BM and its twohop neighbors do not own the recent slot. As soon as the backoff timer finishes inside a contention window [To, Tno], it sprints CCA. And the broadcasting is initiated, if the channel is clear. The aforementioned process is reiterated subsequent to waiting till the channel becomes free, if the channel is not clear. Subsequently, it stays for to and then performs an arbitrary backoff, if node i is in NBM or in BM and its two-hop neighbors do not own the recent slot. Its broadcasting (it may sleep) is postponed until it locates a time

slot that either (1) is not owned by a two-hop neighbor or (2) is its owner, if node i is a non-holder of the recent slot and is in BM. Subsequent to waking up, the above process is repeated. Contention Notification Message When there is high contention, CN messages are notified to the two-hop neighbors so that they will not be hidden terminals to the holder of each slot. To estimate the two-hop contention, noise level of the channel is measured. When high contention occurs, it increases the noise level. The noise level can be measured passively during data transmission. To measure the noise level passively, we measure the average number of noise back offs that a sender takes before transmitting a packet. A noise backoff is taken by a transmitter when it senses the channel using CCA before packet transmission. When the noise level is higher than the CCA threshold, the node takes backoff. In order that not to be concealed terminals to the owner of each slot, CN messages are informed to the two-hop neighbors whilst there is elevated contention. Noise level of the channel is calculated to approximate the two-hop contention. It enhances the noise level at what time high contention happens. Throughout data transmission, the noise level could be considered inactively. The standard number of noise back offs occupied by a dispatcher prior to broadcasting a packet is considered to measure the noise level inactively. While it senses the channel by means of CCA ahead of packet transmission, a spreader obtains a noise backoff. The node obtains backoff, at what time the noise level is higher than the CCA threshold. Whenever a transmitting node detects high contention, it unicasts a one-hop CN message to a destination to which the node is experiencing contention. It broadcasts the information about the multiple destinations, when they experience contention. When a node j receives a one-hop CN message triggered by its one-hop neighbor it first checks whether j is the destination of the CN message. If so, it then broadcasts the CN to its one-hop neighbors. If j is not the destination, it simply discards the one-hop CN. When a node receives a twohop CN, then it sets its BM flag. It unicast a one-hop CN message to a target to which the node is feeling contention, every time a transmitting node identifies high contention. While they experience contention, it transmits the information regarding the numerous targets. It initially verifies whether j is the target of the CN message, whilst a node j obtains a one-hop CN message activated by its one-hop neighbor. Accordingly, it subsequently broadcasts the CN to its one-hop neighbors. It merely abandons the one-hop CN, if j is not the target. After that it sets its BM flag, while a node receives a two-hop CN. Figure 1 shows an example of CN forwarding.


53

Hence TTA - MAC = N r

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