A Hybrid Spectrum Sensing Method for Cognitive Sensor Networks

Wireless Pers Commun (2014) 74:953–968 DOI 10.1007/s11277-013-1332-4

A Hybrid Spectrum Sensing Method for Cognitive Sensor Networks Amir Sepasi Zahmati · Xavier Fernando · Ali Grami

Published online: 31 July 2013 © Springer Science+Business Media New York 2013

Abstract Existing spectrum sensing methods for cognitive radio do not consider the secondary network’s characteristics to obtain the frequency of spectrum sensing, i.e., the sensing period would be identical for secondary networks that have different traffic characteristics. In this paper, a hybrid sensing algorithm is proposed that finds the optimal sensing period based on both primary and secondary networks’ properties. A continuous-time Markov chain system is used to accurately model the spectrum occupancy, and a novel method is proposed that adaptively varies its parameters to avoid unnecessary sensing tasks, while guaranteeing the priority of the primary network. We conduct simulation work to evaluate the performance of the proposed method. It is shown that the proposed technique outperforms the non-hybrid approach with respect to sensing efficiency and energy consumption. A cognitive sensor network is also considered based on IEEE 802.15.4/ZigBee radios, and it is shown that significant energy savings can be achieved by the proposed method. Keywords Cognitive sensor networks · Spectrum sensing · Energy efficiency · Sensing efficiency 1 Introduction Cognitive radio (CR) has been recognized as the key enabling technology to overcome the wireless spectrum scarcity issue in the fixed allocation policy [1]. A cognitive radio network

A. Sepasi Zahmati (B) · X. Fernando Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada e-mail: [email protected] X. Fernando e-mail: [email protected] A. Grami Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Canada e-mail: [email protected]

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(CRN) is defined as a network that can change its parameters according to the interactions with the environment in which it operates [2–4]. In this framework, cognitive users also known as secondary users (SUs) are required to identify spectrum opportunities to communicate among themselves by sensing the spectrum, without imposing interference to the licensed users also called primary users (PUs). Spectrum sensing has been addressed as one of the most fundamental elements of a CSN; the task can be realized as a two-layer mechanism. The PHY-layer detection methods such as energy detection, matched filter, and feature detection aim to efficiently discover the presence of PUs by adapting its modulation/encoding schemes and parameters. On the other hand, the MAC-layer sensing determines when SUs have to sense which channels (sensing period) [25,26]. Latest developments in spectrum allocation policy and regulatory domains, including the release of the National Broadband plan, the publication of final rules for TV white spaces, and the ongoing proceeding for secondary use of the 2,360–2,400 MHz band for medical body area networks, have opened up various opportunities for the secondary use of spectrum [5,6]. CR is therefore addressed to enable and support a variety of emerging applications, ranging from smart grid, public safety and broadband cellular, medical applications to wireless sensor networks (WSNs) [7,8]. The wide range of CR applications have various design requirements. For instance, from the data rate point of view, a temperature sensor may have a very low data rate whereas an acceleration and vibration sensor may have a very high data rate. The IEEE 802.15.4 standard is another example that supports different data rates varying from 20 Kbps (868 MHz band) to 250 Kbps (2.4 GHz band). Although various CR applications have different sets of design requirements, to the best of our knowledge, existing works do not consider SU’s properties to obtain the sensing frequency (or the period of spectrum sensing). This implies that the sensing period would be identical for diverse CR applications, and the secondary network’s benefits are not fully guaranteed for all circumstances. The concern would be highlighted for energy-constrained CRs such as cognitive sensor networks (CSNs) where over-sensing of the spectrum would waste vital network’s resources including time and energy. In this paper, we consider a continuous-time Markov chain (CTMC) system that accurately models the spectrum occupancy in a CRN, and propose a novel sensing algorithm that varies its parameters to reduce the sensing frequency, and thus save the secondary network’s vital resources while guaranteeing the priority of licensed users and spectrum opportunities to be discovered by SUs. The proposed algorithm is a hybrid technique that finds the optimum sensing period according to the both primary and secondary users’ characteristics. The reminder of this paper is organized as follows: Sect. 2 presents a brief discussion to elaborate the motivations of this work, and explains the preliminaries and system model used in this paper. Section 3 describes the proposed spectrum sensing method. First a few definitions are provided, then the hybrid method’s objective functions are formulated. In Sect. 4, simulation results are presented to validate the performance of the proposed method. In addition, a case study for a cognitive sensor network based on IEEE 802.15.4/ZigBee radios has been performed to evaluate the energy consumption of the proposed method. Finally, Sect. 5 concludes the paper.

2 Motivations and Preliminaries In this section, first a brief discussion is presented to elaborate the motivations of this work. Next, the preliminaries and system model used in this paper are presented. In particular, the spectrum occupancy model and PU detection method are presented in detail.

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Fig. 1 Illustration of two CSN applications that have different arrival rates: (a) high arrival rate, and (b) low arrival rate

We note that existing sensing algorithms do not consider the SU’s characteristics to obtain the optimum sensing period. However, there exists various types of CR applications that have diverse sets of characteristics and design requirements. For instance, in prognostic health monitoring applications for aircraft, applications such as smoke detection sensor networks and ice detection sensor networks have low probability of arrival rates while applications such as cabin pressure sensor networks and engine prognostic sensor networks have high arrival rates [9]. Figure 1 depicts a concept model to illustrate two types of applications that have different arrival rates. However, notice that in conventional sensing methods, both applications would have the same sensing periods. As shown in Fig. 1 over-sensing of the spectrum in case (b) would waste the CSNs’ vital resources because there is too little demand for the spectrum in this case. Therefore, the desired sensing technique must distinguish different types of CSN applications, and provide a customized solution for each type. In this work, we propose a hybrid sensing method that considers both secondary and primary users’ characteristics to accurately find the optimum sensing period. The advantage of the proposed hybrid technique will be highlighted for CSNs. A CSN is defined as wireless network of low-power radios which gain secondary spectrum access following the cognitive radio paradigm [10]. The following characteristics distinguishes CSNs from CRNs: Firstly, a CSN is usually equipped with limited energy nodes that are batterypowered. Therefore, in such networks, energy consumption is one of the most important issues to be considered. Secondly, in general, CSNs do not require high bandwidth or high data rate communication links, similar to WSNs [7]. The remaining of this section presents the spectrum occupancy model and the PU detection method used in this work. 2.1 Spectrum Occupancy Model In CR, spectrum sharing methods are divided in two categories: underlay and overlay schemes [11]. In the former approach, SUs are allowed to operate concurrently with the PUs as long as the interference perceived at the receivers of the PUs’ signals remains below a predefined threshold [12]. In this method, SUs usually apply spread spectrum techniques to fully utilize the wide range of spectrum. However, due to the constraints on transmission power, SUs can only achieve short-range communication. In the overlay approach also refereed as opportunistic spectrum access, SUs will only use the licensed spectrum when PUs are not transmitting, so there is no interference temperature limit imposed on SUs’ transmission. Instead, SUs need to sense the licensed spectrum and detect the spectrum opportunities to avoid harmful interference to PUs [5]. In this work, the overlay approach is used.

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Fig. 2 The state transition rate diagram for the CTMC model [13]

We study a CRN with one primary and N secondary users where each SU periodically senses the primary channel. The arrival and departure of the primary and secondary users’ traffic are assumed to be independent, continuous-time Poisson processes. Therefore, a CTMC is used to model the spectrum occupancy by the primary and secondary users. The PUs’ traffic is modeled with two random processes. The service request is modeled as a Poisson Process with arrival rate α (s−1 ), and the service duration (access duration) is negative-exponentially distributed with mean time 1/β (s), so the departure of the PU’s traffic is another Poisson Process with departure rate β (s−1 ) [13–16]. Similarly, the arrival and departure rates of secondary user i (1 ≤ i ≤ N ) are modeled as independent Poisson Processes with λi (s−1 ) and μi (s−1 ), respectively. In addition, we assume that the spectrum cannot be occupied by more than one user at any time, i.e., there is no overlap between any two users. The CTMC model is depicted in Fig. 2 where the state space vector x for the CTMC model is x = {O, S1 , S2 , . . . , S N , P}.

(1)

In Eq. (1), state O means no user operates in the spectrum (idle), state Si means the secondary user i operates in the spectrum (1 ≤ i ≤ N ), and state P means the PU operates in the spectrum. The steady-state probability of state i ∈ x is denoted by πi which represents the probability of being in state i given by [13,14] ⎧ β    π0 = ⎪ λi N ⎪ (α+β) 1+ ⎪ i=1 μi +α ⎪ ⎨ i (2) πi = μiλ+α π0 , (1 ≤ i ≤ N ) ⎪ ⎪ ⎪ ⎪ ⎩ α π P = α+β 2.2 Detection Scheme In this work, we use energy detection method which is the most common way of spectrum sensing because of its low computational and implementation [17–19]. In this method, the received signal is assumed to be r (t) = s(t) + n(t), where s(t) is the primary signal to be

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detected, and n(t) is the additive white Gaussian noise (AWGN). The received signal is first pre-filtered by an ideal bandpass filter that has bandwidth W , and the output of the filter is then squared and integrated over a time interval called observation time TO to produce the test statistic [20]. The decision is made by distinguishing between the following two hypothesis [15,17]: n(t) : H0 r (t) = (3) s(t) + n(t) : H1 In Eq. (3), H0 represents the hypothesis corresponding to the absence of PU, and H1 to the presence of PU. In the above hypothesis testing, the probability of detection is Pd = Pr [Y > |H1 ] and the probability of false alarm is P f = Pr [Y > |H0 ], where Y is the test statistic, and  is a predefined system parameter known as decision threshold. In the energy detection method, the output of the integrator is known as the Chi-square distribution [22]. However, one can use the central limit theorem to approximate the Chi-square distribution with the Gaussian distribution, if the number of samples is large as follows [23]:

N (nσn2 , 2nσn4 ), : H0 Y ∼ (4)  N n(σn2 + σs2 ), 2n(σn2 + σs2 )2 : H1 where n is the number of samples, σn2 is the variance of the noise, and σs2 is the variance of the received signal. According to the Nyquist sampling theorem, the minimum sampling rate should be 2W , so n = 2W TO . The probability of false alarm P f and the probability of detection Pd is therefore obtained by

  − 2TO W σn2 Pf = Q  , (5) 4TO W σn4

  − 2TO W (σs2 + σn2 ) Pd = Q  , (6) 4TO W (σs2 + σn2 )2 Note that in the energy detection method, the RF front-end cannot differentiate between the primary and secondary signals [24]. In addition, it is assumed that secondary users cannot perform the transmission and sensing tasks at the same time. Therefore, it is necessary to periodically perform the spectrum sensing task.

3 Hybrid Spectrum Sensing Method From the secondary users’ perspective, the secondary network is either operating in the spectrum (active) or is not operating in the spectrum (inactive). We, therefore, can divide the state transition rate diagram into two phases, as shown in Fig. 3. According to the CTMC N πi , model, the secondary network is at the active phase with the probability of PA = i=1 and at the inactive phase with the probability of PI = π0 + π P . Note that whether the secondary network is at the active or inactive phase, it is necessary to perform periodic sensing. In the active phase, the sensing task is required to avoid interfering with PUs. During the inactive phase, the sensing task is required to be aware of spectrum opportunities for potential use of the opportunities by the secondary network in the future. As shown in Fig. 4, during the inactive phase, SUs perform the sensing task for TO seconds, and return to idle state for TI seconds. Furthermore, within the active phase, spectrum sensing is performed for TO seconds, and data transmission is performed for TT seconds.

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Fig. 3 The secondary network is either at the active or the inactive phase

Fig. 4 The proposed method’s operating phases

Hence, in the proposed method, there will be two objective functions, based on the secondary network’s operating phase. If the secondary network is in the inactive phase, sensing period is obtained based on both secondary and PU’s characteristics. However, if the secondary network is in the active phase, it is important to vacate the channel as soon as a PU arrives. Therefore, in this phase, sensing period is optimized based on the PU activity only, and SUs’ characteristics are not taken into consideration. Figure 5 depicts a pseudo code for this hybrid method. In the rest of this section, first a few definitions are provided; then we formulate the proposed method’s objective functions. 3.1 Problem Definitions Definition 1 The sensing efficiency (η) is the ratio of the transmission time over the sensing period when the PU is not operating in the spectrum as follows: η=

123

TT . TO + TT

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Fig. 5 Pseudo code for the hybrid method

Definition 2 The detected spectrum opportunity ratio (ζ ) is the ratio of PU’s OFF state (idle state) detected by SUs. Therefore, ζ = 1 corresponds to the ideal case where all the spectrum opportunities are detected by SUs. Definition 3 The maximum interference ratio (Imax ) is the maximum fraction of interference that the PU can tolerate. 3.2 Problem Formulation In this section, the proposed method’s objective functions are explained for the active phase and inactive phase. 3.2.1 Active Phase During the active phase, it is expected to maximize the sensing efficiency. However, it is more likely to interfere with the PU when η increases. Therefore, η can only increase when the interference level is not exceeded from a predefined threshold (Imax ). Hence, the spectrum sensing problem in this phase is defined as follows: Find : TP∗T Maximize : η Subject to : I ≤ Imax where TP∗T is the optimal sensing period within the active phase. The interference ratio (I ) is the expected fraction of the PU’s ON state interrupted by the transmission of SU given by [21]   α −μTT β I = P f + (1 − e−μTT ) e , (8) β α+β where μ = max(α, β), and I varies within active phase is obtained by (see Fig. 4)

α β

Pf ≤ I ≤

TP T = TO + TT .

α α+β .

The sensing period within the (9)

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Therefore, it is necessary to derive TT in order to find the sensing period. Note that η is an dη increasing function in respect to TT ; i.e., dT > 0. Therefore, maximization of η corresponds T to the maximization of TT . Proposition 1 I is a monotonically increasing function of TT . Proof Differentiate I w.r.t. TT , then we have   α β ∂I . = μe−μTT −P f + ∂ TT β α+β α . Hence, P f < As noted before, I varies in βα P f ≤ I ≤ α+β I is a monotonically increasing function of TT .

β α+β

(10) and

∂I ∂ TT

> 0. Therefore, 

Therefore, to obtain the maximum TT , the interference level is selected to be the largest amount that is allowed (Imax ). After some algebraic manipulation, we obtain the transmission time TT from Eq. (8) as

 α Imax − α+β 1 TT = − ln α . (11) α μ β P f − α+β Sensing period TP T is then derived by plugging Eq. (11) in Eq. (9). 3.2.2 Inactive Phase The most important feature of the proposed method which makes the algorithm outstanding is that sensing period is selected according to both primary and secondary users’ characteristics. We note that for a CSN with low service request, it is desirable to select a large idle time to save secondary networks’ vital resources. Therefore, the idle time and average service request of SUs are inversely proportional, i.e., TI ∝

1 . E[Ser vice Request]

(12)

As noted before, we assume that the SUi ’s service request is a Poisson process with arrival rate λi . Therefore Eq. 12 reduces to TI ∝ λ1i . From the perspective of the secondary network, it is expected to select the idle time as large as possible. However, a large idle time corresponds to a large sensing period as in T P I = TO + T I ,

(13)

that increases the chance to miss spectrum opportunities due to insufficient spectrum sensing. In [25], the Achieved Opportunity Ratio (AOR) is defined as the ratio of total discovered spectrum availabilities to the total existing availabilities; AO Rmax is given by   TI 1 − e−β(TO +TI ) AO Rmax = . (14) β(TO + TI ) TO + T I Hence, it is important to select the sensing period within the inactive phase in a way that • Both secondary and primary users’ characteristics are taken into consideration, and • Maximum AOR is greater than a predefined threshold ζ . In other words, the PU’s activity introduces an upper bound for the sensing period.

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Fig. 6 Original and approximated AO Rmax curves (TO = 10 ms, β = 1.5 s−1 )

We note that TO in Eq. (14) is involved with TI only within the term (TO + TI ). Therefore, AO Rmax is approximated with the following equation for TO  TI : AO Rmax =

1 − e−βTI . βTI

(15)

Figure 6 depicts that the original AO Rmax function can be accurately described by the approximated function for TO  TI . Then, TI can be derived as follows (see “Appendix”):    −1 1 1 TI = W , (16) + 1 β ζ ζeζ where W (.) is the Lambert W-function [27]. Equation (16) presents the maximum TI that does not fail the AO R requirement (AO Rmax ≥ ζ ). Therefore, the optimal TI∗ is derived by mixing Eqs. (12) and (16) as follows: ⎧

   ⎪ ⎪ 1 1 −1 1 1 ⎨ min λi , β W +ζ if λi ≤ 10T 1 O TI∗ = (17) ζe ζ ⎪ ⎪ 1 ⎩ T min if λ > I

i

10TO

Note that in Eq. (17) the idle time is inversely proportional to the SU’s arrival rate; i.e., TI decreases when λi increases. However, the idle time cannot be smaller than a predefined system parameter (TImin ) which is set due to the radio startup time. Sensing period TP I is then derived by plugging Eq. (17) in Eq. (13).

4 Simulation Results In this section, we present the simulation work that is conducted to evaluate the performance of the proposed method. As noted before, the most important feature of the proposed hybrid

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Fig. 7 Total observation time (normalized) in the hybrid and non-hybrid method

technique is that both primary and secondary users’ characteristics are taken into consideration to choose the spectrum sensing period. To investigate the effectiveness of the proposed method in this regard, we assume a homogenous CSN where λ1 = λ2 = · · · λ N = λs , and vary λs within 0.2 ≤ λs ≤ 15. We run the simulation for K iterations, and  Kstudy the total amount of time spent for spectrum sensing (total observation time), i.e., i=1 TO (i); this K

T (i)

O parameter is then normalized in i=1 , where Tsim is the simulation running time. Tsim The solid curve in Fig. 7 depicts the total observation time (normalized) for the hybrid method. As shown in the figure, the parameter is selected to be small for a SU with small arrival rate, and large for a SU with large arrival rate. The advantage of the proposed method is further realized by comparing this algorithm with a non-hybrid scheme where sensing period is selected only based on PU’s characteristics. The dotted line in Fig. 7 represents the total observation time (normalized) for a non-hybrid method. As shown, the parameter in this method does not change for different arrival rates.1 Therefore, as shown in Fig. 7, the hybrid technique manages to decrease the total observation time more than four times. As the arrival rate increases, the hybrid technique approaches to the non-hybrid method to satisfy the SU’s demand for frequent usage of spectrum. The performance of the proposed method in respect to the sensing efficiency has been also observed. At the first place, the secondary network’s arrival rate λs varies and the sensing efficiency is examined for two fixed values of the secondary network’s departure rate μs . As shown in Fig. 8, the proposed method (hybrid) yields a better sensing efficiency comparing to the non-hybrid method (up to 2.5 times better for secondary applications with low arrival rate). Note that as the arrival rate increases, the hybrid method approaches to the non-hybrid technique to satisfy the SU’s demand for more spectrum opportunities. Moreover, Fig. 8 correctly implies that the sensing efficiency is decreased for both hybrid and non-hybrid methods when μs is increased, i.e., when SUs leave the channel more frequently or there is

1 The small variation in

K

i=1 TO (i) Tsim

for the non-hybrid method is due to the fact that the network parameters are stochastically selected through the simulation.

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Fig. 8 Sensing efficiency in the hybrid and non-hybrid method, μs is fixed

Fig. 9 Sensing efficiency in the hybrid and non-hybrid method, λs is fixed

less demand for spectrum usage. At the second place, μs varies and the sensing efficiency is examined for two fixed values of λs . As shown in Fig. 9, the sensing efficiency has been improved (shifted up) in the hybrid method comparing to the non-hybrid technique. In addition, similar to the previous results, the sensing efficiency is decreased when the secondary network’s departure rate is increased. In addition, it is properly shown that η increases for both hybrid and non-hybrid methods when λs increases. To further realize how the proposed method improves the sensing efficiency, one may observe the idle time’s variation in this algorithm. As depicted in Fig. 10, in the proposed

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Fig. 10 Variation of idle time in the proposed method for different values of μs

method, the idle time is selected to be large for secondary applications with low-arrival rate. Therefore, unnecessary sensing tasks are avoided for such applications, and the sensing efficiency is improved with less frequent sensing tasks. In addition, it is shown that the idle time is increased for larger values of departure rate μs . Note that a larger departure rate for the secondary network implies that the SUs vacate the channel more often, i.e., there is less demand for the spectrum usage. Therefore, in the proposed method, the idle time is selected to be large for SUs with large departure rates to avoid unnecessary sensing tasks. Similar to  K

T (i)

i the previous simulations, the total idle time is normalized as i=1 in this experiment as Tsim well. The remaining of this section considers a cognitive sensor network based on IEEE 802.15.4/ZigBee radios, and investigates the energy consumption for the proposed method. A Chipcon CC2420 transceiver based on the IEEE 802.15.4/ZigBee standard [28] is considered to compute the energy consumption. According to [29], the sensing energy for each decision consists of two parts: the energy consumption involved in listening over the channel and making the decision, and the energy consumption of the signal processing part for modulation, signal shaping, etc. The typical circuit power consumption of ZigBee is approximately 40 mW. In addition, the processing power related to the signal processing part for a data rate of 250 kb/s, a voltage of 2.1 V, and current of 17.4 mA is approximately 150 mW. Therefore, the sensing power of each cognitive sensor is P = 190 mW [10–30]. The total energy consumption for the spectrum sensing during the K simulation iterations is

E=

K 

P TO (i).

(18)

i=1

In Fig. 11, the secondary network’s arrival rate is set to λs = 2 (s −1 ), and the energy consumption (E) is compared for the hybrid and the non-hybrid method. As shown, the proposed method significantly improves the energy consumption over the time. All simulation parameters for the above experiments are listed in Table 1.

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Fig. 11 Energy consumption in the hybrid and non-hybrid method, a case study Table 1 Simulation parameters

Parameter

Value

Parameter

Value

α (s−1 )

1

β (s−1 )

1.5

μi (s−1 )

0.3

λi (s−1 )

0.2 ≤ λi ≤ 15

TImin (s)

0.1

TO (ms)

10

W (KHz)

10

S N R(dB)

−5.8

ζ

0.9

Imax

0.1

Pf

0.1

K

75

5 Conclusion A novel spectrum sensing method was presented to improve sensing efficiency and energy consumption in cognitive sensor networks. In particular, a hybrid method was proposed that varies its parameters according to the properties of both primary and secondary users, and optimal sensing periods were derived for the inactive phase and the active phase. The proposed method avoids unnecessary sensing tasks while guaranteeing the priority of primary users and spectrum opportunities to be discovered by secondary users. Performance of the hybrid method was compared to a non-hybrid technique through simulation work. It is concluded that the proposed method outperforms non-hybrid methods in terms of sensing efficiency and energy consumption. In addition, by considering a sensor network based on IEEE 802.15.4/XigBee radios, we showed that significant energy savings can be achieved by the proposed method. Appendix: Derivation of TI The Lambert W-function is used to derive TI from Eq. (15). Firstly, Eq. (15) is written as −βT ζ = 1−eβTI I , where ζ is the minimum acceptable AO R. Assume x = βTI , then

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ζ x = 1 − e−x . Using the substitution (t = x −

1 ζ)

(19)

yields to tet =

−1 1

ζeζ

.

(20)

Secondly, we use the following property of Lambert W-function [27]

Therefore, t = W

 −1

1

ζe ζ

Y = X e x ⇔ X = W (Y ).

(21)

, or TI is given by    −1 1 1 TI = W . + 1 β ζ ζeζ

(22)

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Author Biographies Amir Sepasi Zahmati received the PhD degree in electrical and computer engineering from Ryerson University in 2013. He is currently a Research Associate at Ryerson Communications Lab (RCL). His current research interests include cognitive radio networks, cooperative sensing, wireless sensor networks, and energy efficiency in sensor networks. Amir has industrial work experience at Honeywell Aerospace, Toronto as an R&D Intern researching and developing wireless power control systems during 2010–2011. He also worked at Ericsson as an Electrical Engineer during 2006–2007. He finished his M.Sc. at the University of Science and Technology, Iran. Amir has been the Vice Chair of IEEE Toronto Section, Communications Chapter since Jan. 2012. Also, he has been the Social Media Officer of IEEE Toronto Section since Feb. 2012. He served as Publications Chair for IEEE ICNF 2011. In addition, Amir has served as a technical program committee member for various conferences, such as IEEE ICNC’14, ICNC’13, IEEE ISIEA’12, and IEEE TIC-STH’09.

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A. Sepasi Zahmati et al. Xavier Fernando (http://www.ee.ryerson.ca/~fernando) is an IEEE Communications Society Distinguished Lecturer and the Chair of the IEEE Toronto Section, Canada. He is a Professor at the Dept. of Electrical and Computer Engineering, Ryerson University, Toronto. He earned his PhD from the University of Calgary, Alberta in 2001. He has coauthored close to 100 research articles, one book and holds two patents. He is a member in the IEEE COMSOC Education Board Working Group on Wireless Communications. He is a program evaluator for ABET (USA). His work has won several awards and prizes including IEEE Microwave Theory and Techniques Society Prize in 2010, Sarnoff Symposium prize in 2009, Opto-Canada best poster prize in 2003 and CCECE best paper prize in 2001. He is the General Chair for IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) 2014.

Ali Grami received his BSc, MEng, and PhD degrees from the University of Manitoba, McGill University, and the University of Toronto, respectively, all in Electrical Engineering. Following his graduation, he joined Nortel Networks, where he was involved in the research, design, and development of North America’s first digital cellular wireless system. Later he joined Telesat Canada, where he was the lead researcher and principal designer of Canada’s Anik-F2 Ka-band system, the world’s first broadband access satellite system and the first satellite to successfully commercialize the Ka-band technology. While he was with the industry, he taught at the University of Ottawa and Concordia University, and received the United Nations TOKTEN award. In 2003, Dr. Grami, as a founding faculty member, joined the University of Ontario Institute of Technology (UOIT). In addition to his responsibilities in teaching, research, and service at UOIT, he has led the development of BEng, MEng, and PhD programs in Electrical Engineering.

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