Extensive Simulation Performance Evaluation of Minimum Shift Keying Scheme with Error Correcting Codes in Wireless Sensor Networks Rajoua Anane?,?? , Bouallegue Ridha? , Member, IEEE, and Kosai Raoof?? ?
Innovation of communication and cooperative mobiles, InnoV’COM Lab, University of Carthage,Tunisia ?? Laboratory of Acoustics at University of Maine, LAUM UMR CNRS n 6613, France
[email protected],
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Abstract—Link reliability and power consumption are a prime concern in the design of wireless sensor networks. The data exchanged between nodes are vulnerable to corruption by errors induced by random noise, signal fading and other factors. Error control codes (ECC) is an efficient technique for increasing reliability of links and minimizing power transmitted. In this context, the choice of energy efficient ECC with a suitable modulation scheme is a vital task at the physical layer of wireless sensor networks to improve their lifetime. A performance analysis of energy consumption referring to MSK modulation with suitable error control codes approach is presented in this paper. The particularly analysis is evaluated in terms of energy consumption and probability of Bit Error Rate (BER) for various error control codes combined with optimal modulation scheme through a Gaussian channel (AWGN). Based on simulations results, we observe that the benefit of error correcting approach varies with the transmission distance. The combination MSK modulation and Reed Solomon code yields a significant improvement power consumption. Keywords—Wireless Sensor networks (WSN); Power consumption; MSK Modulation; Error control codes (ECC); Reed Solomon (RS); Energy savings.
I.
I NTRODUCTION
n the last few years, wireless sensor networks have Iresearchers increasingly attracted considerable attention among in the field of Telecommunications. They are considered as one of the most active areas of technology development due to their unique characteristics, low cost, easy deployment and flexibility [1]. Battery powered sensor nodes are able to sense, collect and share data with other nodes and could be deployed in various applications such as habitat monitoring, military, agriculture, science and industry applications [2]. However, links reliability and limited energy resources of sensor node are two of the major constraints in wireless sensor networks. Sensor nodes use some optimization techniques to decrease energy consumption, like switching from active mode to sleeping mode when there is no information to send. This strategy contributes to energy conservation because power consumed during sensor communication is significantly higher than power consumed during sensing and processing. On the other hand, following a bad channel condition that can be caused by random noise or fading, nodes can
receive corrupted data. This phenomenon generates successive retransmissions and of course a waste of energy in the network. In order to increase the lifetime of sensors, an energy efficient transmission method should be considered at the transceiver node [3]. In general, coding technique refers to channel coding. The choice of selecting modulation and coding strategy permits to get better energy conservation. Indeed, applying ECC techniques on the communication part of sensor nodes contribute to minimize transmitted power and increase link reliability at the cost of extra power consumption by the decoder at the receiver. Therefore, the choice of a particular coding approach depends on the type of application and other constraints of WSN such as channel condition, number of sensors, distance and type of modulations scheme. Substantial research has been dedicated to increasing the lifetime of wireless sensor by minimizing energy consumption at the transceiver level with different methods. The authors in [5] considered various ECC using a BPSK modulation technique under AWGN channel conditions, and consider that decoding process is done at the base station, which is equipped with sufficient power supply. They concluded that turbo codes are more efficient compared to the other codes. CRC control mechanism (CRC-4 and CRC-12) is implemented in [6] for mobile sensor networks. In order to check the integrity of data transmitted at the receiver, authors proposed to add CRC field at the end of the packet. Analysis of Redundant Residue Number System (RRNS) with BPSK, QPSK modulation techniques under flat-fade channel condition was developed in [7]. The analysis considered only a point to point communication, between sensor node and the base station (BS), which means that decoder process occurs in the BS. In addition, in [8] authors compared adaptive modulation such as BPSK, QPSK 16QAM and 64-QAM with convolution code for all participate relay sensor network to achieve a better overall system performance. The performance of Reed Solomon code for different code rates was analyzed with a binary phase sift keying (BPSK) modulation scheme in [9] The simulation results showed that the BER performance improves as the code rate decreases.
This paper develops the work of earlier research to optimize the energy consumption in the network by applying optimal modulation technique and suitable coding processing at the source sensor node before transmission. II.
O UE C ONTRIBUTIONS
Performance analysis of different modulation techniques was studied in our earlier work [4]. It investigated the best modulation strategy to minimize the total energy consumption required to transmit a given number of bits. Energy consumption with digital modulation schemes including M-ary QAM (MQAM), M-ary PSK (MPSK), M-ary FSK (MFSK) and MSK are analytically analyzed and simulated over transmission time, modulation rate and transmission distance. In that study, we concluded that MSK modulation becomes more advantageous than its counterparts from the point of view of energy consumption. In this paper, we aim to extend our precedent results and we focus on improving the performance of MSK scheme with coding mechanism. Indeed, if the coding and decoding process cost less than additional retransmission cost, introducing a coding method can be a good choice for energy efficient WSNs. Through extensive simulation we evaluated suitable error correction code based on the BER (Bit Error Rate) versus signal-to-noise ratio (SNR) and power consumption. Appropriate ECC will be implemented in the limited-source nodes. Various simulations are performed using MATLAB/Simulink software simulator. The remainder of this paper is organized as follows: Section III presents channel coding overview. Section IV offers a detailed performance analysis of various ECCs. An energy model of coding MSK scheme is developed in Section V. In Section VI simulation results are discussed, followed by the conclusion in Section VII. III.
Broadly, Channel error control codes are classified into two main approaches [9]: •
Error detection codes with retransmission known as automatic relay request.
•
Forward error correction codes (FEC).
In the first method, if an error is detected, a feedback (negative acknowledge) is sent from the destination node to the source node, which request retransmission of data. In the second case, the errors are detected and corrected by ECC process at the receiver side. Coding techniques introduce redundancy into an information sequence and these extra bits are used to detect noisy received bits at the receiver node. For a block of length k bits, (n-k) check bits are added [10]. This means that the length of the code-word at the output of encoder block is n bits. The error correction codes can be classified in to two types [11]: •
Convolutional Code takes a stream of data bits and converts it into a stream of transmitted bits, using a shift register bank. This type of coding often uses a Viterbi algorithm for decoding process.
•
Linear Block Code encodes data in blocks with fixed lengths. There are a vast number of examples for block codes such as: Hamming, Golay, Reed Solomon, BCH. A linear code of length n, dimension k, and minimum distance d is called an [n, k, dmin ] code with a rate R = k/n (Figure 2). dmin represents the minimum number of different bits between any of the code-words and can be calculated as follows: dmin = n − k + 1.
W IRELESS S ENSOR N ETWORK C HANNEL C ODING
During the transmission process, the transmission data passes through a noisy channel. Due to this random noise, errors can be introduced in the received signal. These errors can be detected and sometimes corrected using coding methods. Therefore, Channel coding helps to minimize the effects of a noisy transmission channel(Figure 1).
Fig. 2: Structure of linear block code.
Applying a coding approach over a noisy channel can improve the performance of BER for the same value of SNR compared to a communication without coding process, or can also give the same bit error rate for a lower SNR. Thus, the difference in required signal-to-noise ratio to obtain a certain BER for a specific code compared to un-coded system is called the coding gain. There is a tradeoff between decoder complexity and coding gain. Indeed, to obtain higher gain we must use a long code that demands more complex decoder and subsequently high energy consumption. IV. Fig. 1: Communication system model with coding channel.
P ERFORMANCE A NALYSIS OF ECC
It is clear that error correcting code permits minimization of the required transmitter signal energy due to its coding gain, but not all coding methods are suitable for the wireless network.
In this study, we have considered different ECC performance analyzes in terms of their BER as a function of SNR curves with MSK modulation scheme under AWGN channel model. Through extensive simulation we selected the optimal code among various coding methods (e.g. Hamming, Golay, Convolutional and Reed Solomon (RS) Codes).
Fig. 4: Performance Evaluation of RS code, MSK (AWGN).
Fig. 3: Bit error rate analysis of various coding techniques.
Figure 3 depicts the comparison between uncoded and coded MSK scheme with several coding techniques. We see that Reed Solomon code and Convolutional code perform better characteristic than the Golay and Hamming codes. Its clear that a Convolutional code provides the highest gain, but it results in a higher bit error rate (BER ≈ 100 ). By contrast, the Reed Solomon technique gives the second highest gain and results in a better bit error rate (BER ≈ 10−1 ). On the other hand, Convolution code requires high power consumption due to its encoding and decoding complexity. As a result, it is not suitable for energy constraint wireless sensors [11]. Hence, Reed Solomon approach is considered as the best choice for WSNs. In this work, a packet length of 512 bytes is considered for these simulations and tested over a range of 4 to 16 correction bytes. Through extensive simulations, we analyzed various ECC to find energy optimal code for a particular choice of BER. Table 1 summarizes principal characteristics of coding techniques simulated. TABLE I C ARACTERISTIC OF RS C ODES S IMULATED Parameters of various RS codes Codeword length: n
Information length: k
Check bytes: n-k
Correcting Capability: (n-k)/2
Rate of Code: R=k/n
512
508
4
2
0.994
512
504
8
4
0.986
512
496
16
8
0.970
512
480
32
16
0.939
Figure 4 presents the performance of Reed Solomon code with various code rates. Not surprisingly, the coding gains of different Reed Solomon codes evaluated are not the same for all the bit-error-rates. Moreover, these coding
codes perform better than the uncoded system with lower BER. From these curves, we also observe that RS (511, 480, 32), with correcting capability of 16 error bits, provides a coding gain of approximately 1.7 dB for BER = 10−4 whilst, RS (511, 496, 16) and RS (511, 504, 8) provide respectively 1.2 dB and 0.8 dB for the same BER. In addition, using RS (511,480, 32) code, more redundant bits will be appended and hence more energy is required for transmitting, encoding and decoding of these additional bits [12]. From table II we remark that, RS (511, 496, 16) offers low power consumption compared to RS (511, 480, 32). Thereby, we choose RS (511,496, 16) code, which have the second highest gain, as optimized coding codes for the rest of our simulations. TABLE II R S C ODE P OWER C ONSUMPTION
V.
RS code
Power consumption (mw)
RS(511,504,8)
23mw
RS(511,496,16)
38mw
RS(511,504,32)
66mw
E NERGY M ODEL OF MSK M ODULATION
In this work, we performed the transmitter and receiver hardware model as introduced in our earlier work [4]. The energy consumed by both the transmitter and the receiver blocks was evaluated for calculating the total energy consumption in the network. Case of frequency modulation schemes (MSK), power consumption of both the DAC and the mixer at the transmitter were not included in the calculation of the total power consumption [13] [17]. The transmission period T is given by [4]: T = Tstart + Ton
time
+ Tstby
(1)
Where: -
Tstart is the time of the transient mode. Ton time represents the time spent to transmit L bits. Tstby is the duration of the standby mode.
Since the base station is not power constrained, the energy consumption by the decoding process was ignored. Only the encoding energy for the first node was taken into account. Powers consumption associated to the described modes are denoted as: -
Pstart : Power consumed for mode changing, which is mainly equal to the power of frequency synthesizer. Pon time : Power consumed during the active mode. Tstby : Power consumed during standby mode (assumed to be null for simplification).
Correspondingly, we can derive the equation of the energy consumed data as follows: Etotal = (Penc + Pon
time ) Ton time
Etotal = (Penc + PT x + Ptx +PP A )Ton
time
+ (Ptx
circuit
circuit
+ Pstart Tstart
+ Prx
+ Prx
circuit
(2)
-
PT x represents the power of data transmission. Ptx circuit and Prx circuit are respectively circuit powers for transmitter and receiver block without considering the amplifier. Penc represents the power consumption of the RS encoder at the first sensor node.
We denote: -
Px : the power consumption of device x.
Expressing each term: Ptx Prx
circuit
= Pf ilt + Psyn
Pe ≈ e−SN R
(3)
= PADC + Pf ilt + Pmixer + Psyn + PLN A + PIF A (4) The power of the amplifier is expressed as [14] [15] : ξ PP A = − 1 PT x (5) η circuit
Next the energy per information bit at the receiver is: 1 Erxb = N0 Nf SN R ≈ 2 σ 2 Nf ln Pe
The power of the signal in the output of the transmitter is calculated by the equation of k t h path loss model [14]. We can state that: PT x = Prx Gd (10) Or, Gd = G1 dk M1 represents the power gain factor, G1 is the gain factor at 1 m, M1 is the link margin and d (meters) is the distance that separate two communicating nodes . The exponent order k is between 2 and 4. In this study k = 3 is selected.
ET x−M SK = 2Nf σ 2 ln(
η represents the drain efficiency of the amplifier. ξ is the peak to average ratio that depends on the modulation technique and ξ = 1 for MSK modulation.
circuit
+ Prx
circuit )Ton−time
(6) A bound on the probability of error for MSK is written as [4] [14]: √ 1 Pe ≈ erf c SN R (7) 2
(11)
As well, the energy consumption per information bit is calculated as follows: Etotal−M SK Einf Bit = (13) L Let us recall that, optimal RS code previously chosen permits to minimize the transmitter signal energy due to its coding gain Gcode . Therefore, the required transmission energy is minimized by Gcode at a price of increased transmission time that will be expressed as follows: Ton−time R
(14)
However, the total energy consumption per bit information with coded system is given as: 2 Einf Bit−M SK−code = Gcode 1 + η1 − 1 Nf σ 2 ln( P1e ) Gd + +
+ 2Psyn Tstart
1 )Gd L Pe
Using (2), (6) and (11), the total energy consumption is written as: Etotal−M SK = 2 1 + ( η1 − 1) Nf σ 2 ln( P1e ) Gd L+ (12) (Penc + Pcircuit )Ton−time + 2Psyn Tstart
Ton−time−code =
The total energy expression for MSK modulation techniques is derived as: Etotal−M SK = 1 + η1 − 1 PT x Ton time + (Penc + Ptx
(9)
with N0 = 2σ 2
Where: -
(8)
By following the same process used in our previous study Erxb [4] and knowing that Prx = (L Ton−time ) and ET x = PT x Ton−time , we obtain the expression of the transmission energy as follows:
circuit )Tstart
Where: -
Therefore, we can deduce:
2 Psyn Tstart L
(Penc +Pcircuit )Ton−time−code L
(15)
VI.
S IMULATION R ESULTS
We consider a small scale network based on 10 nodes capable of collecting and transmitting information to a central node. These sensors are deployed in in a spread manner over an area of about 150 square meters and can have different sensitivities as shown in Figure 5 The communication link between two sensor nodes will be modeled by an additive white Gaussian noise (AWGN) channel. The setting parameters considered in the simulation are reported in Table II [4] [16].
Fig. 6: Total energy consumption as a function of transmission distance for coded system, (AWGN).
It is clear that the more the transmission distance increases the more the transmission energy is important. From the above figure, we note that for small transmission distance the uncoded MSK outperforms the coded system. Therefore, at small distances (d < 50meters), using coding techniques is less efficient. But for longer distances −100 m and above, using error correcting codes is energy efficient.
Fig. 5: Example of random distributed sensors network.
TABLE III S IMULATION PARAMETERS Parameter
Value
Tstart T L σ2 k η B Carrierf requency Pe G1 Mt PADC Pf ilt Psyn PLN A PIF A Pmixer Penc Gcode Nf
−
510 6 s 1.07 s 103 bit 3.981 10−21 3 0.75 104 Hz 2.45 GHz 10−4 103 104 6.70 mw 2.5 mw 50 mw 20 mw 3 mw 30.3 mw 48 mw 1.2 dB 10 dB
By using equation (15), the total energy consumption per information bit for MSK modulation with selected RS code over different transmission distances is plotted in Figure 6.
We suppose that a source node S1 send L bits of data to a destination node S5 in a deadline T seconds. We can derive the expression of total energy consumption for both uncoded and coded MSK modulation for inter-nodes communication through relays from following equation [18]:
ER = Eenc +
n X i=1
Ptx (i) Ton−time +
n−1 X
Prx (i) Ton−time
i=1
(16)
Where, -
Ptx (i) is the transmission power of node Si . Prx (i) is the receiving power of node Si n is the number of nodes.
Fig. 7: Total energy consumption for MSK modulation with and without coding technique, (AWGN).
Figure 7 depicts the comparison of energy consumption during an inter-nodes communication for different uncoded and coded system.
[4] Rajoua Anane, kosai Raoof, and Ridha Bouallegue, Optimal modulation scheme for energy efficient wireless sensor networks , 10th International Conference on Wireless Communications, Networking and Mobile Computing (WICOM’14), pp. 500-507, Beijing, September 26-28, 2014.
In this scenario, we consider an inter-nodes communication through relay. Distance between nodes is randomly taken within a range of 50-150m. We assume that there are about 4 intermediate nodes for routing information. The simulation results confirm that MSK modulation with RS code is most energy efficient than the uncoded system.
[5] A. Angelin, B. Revathi, T. Gayathri and Mr. D. Balakumaran, Channel coding in WSN for energy optimization, International Journal of advanced research in electrical electronics and Instrumentation Engineering, vol3, Issue 3, March 2014, pp 7873-7878.
VII.
C ONCLUSION
This paper analysed the performance of Minimum shift keying scheme with Error Correcting approach through AWGN channel to improve the energy efficiency of wireless sensor network. The results disclose that MSK modulation with Reed Solomon code is a suitable combination to minimize the power consumption of sensor nodes and to increase the lifetime of network. Indeed, the use of RS codes permits to minimize the power consumption for large transmission distance without a significant increase in transceiver power. The receiver node is less susceptible to false data detection errors. This makes the use of RS coding technique very attractive in low power wireless networks. The obtained results can be used as a design guideline for coded MSK scheme in various channels conditions with other energy efficient techniques in wireless sensor network. ACKNOWLEDGMENT This work was supported in part by Laboratory Innov’COM of Higher School of Telecommunication, University of Carthage Tunisia and Laboratory of Acoustics at University of Maine, LAUM UMR CNRS n 6613, France. R EFERENCES [1] [2]
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