JOURNAL OF TELECOMMUNICATIONS, VOLUME 29, ISSUE 2, FEBRUARY 20154 Error Correction Scheme for Wireless Sensor Networks Abdulkareem A. Kadhim, Aya K. Al-Joudi, and Hamed Al-Raweshidy Abstract— Wireless Sensor Networks (WSNs) have gained high importance in recent years. Because they are very small and can easily be implemented in any place, they invoke a wide range of applications. In the last years, improvements of wireless sensor networks have been made by applying Error Control Coding (ECC) schemes. Usually two different error control schemes are used for WSNs which are Forward Error Correction (FEC) and Automatic Repeat on reQuest (ARQ). These codes work either separately or in a hybrid manner known as Hybrid Automatic Repeat on reQuest (HARQ) schemes. A proposed coding arrangement is presented here and tested, aiming to provide further performance improvement for different applications of WSNs. The arrangement is based on HARQ scheme which consists of two concatenated FEC codes together with ARQ. The concatenation here reduces errors and hence unnecessary retransmissions by ARQ are avoided, thus energy saving is obtained. WSN simulator is built and used to test the proposed coding arrangement performance. The proposed coding arrangement shows better error rate performance when tested over models of AWGN, flat fading and multipath fading channels. Improvements were gained also in throughput (packets/s) and energy saving as compared to other coding schemes normally used with WSNs. Index Terms— Automatic Repeat on reQuest, Energy Saving in Wireless Sensor Networks, Forward Error Correction, Hybrid Automatic Repeat on reQuest, Wireless Sensor Networks. —————————— u —————————— 1 INTRODUCTION T HE importance of using Wireless Sensor Networks performance measures such as Packet Error Rate (PER), (WSNs) in many applications stems from the fact that it can be easily and effectively deployed. Sensors can reach places where it is difficult to place wires. The fact that WSNs is relatively has lower cost than other wired networks give them more importance [1]. Transmissions over wireless channels affect the transmitted data. Data transmitted over wireless channels will suffer from corruption due to noise and fading. Thus, in recent years the focusing is on improving the overall transmission for these channels [2]. The most effective way to protect transmitted data is the cooperation be-‐‑ tween the transmitter and receiver through the communi-‐‑ cation. This can be done using Error Control Coding (ECC) schemes [3]. ECC schemes for WSNs received considerable attention in recent years to improve their performance. Bit Error Rate (BER) performance shows that using FEC codes, especially Reed Solomon (RS) codes, can significantly improve performance and packet loss [4], [5], [6], [7], [8]. ARQ codes can also be used to improve the perfor-‐‑ mance of WSNs but on the expense of energy consump-‐‑ tion [9]. Using FEC technique combined with ARQ is a promising alternative. Even a simple repetition is more efficient than an ARQ scheme without FEC for different ———————————————— • A. Kadhim is with the Department of Networks Engineering, College of Information Engineering, Al-Nahrain University, Baghdad, Iraq. • A. Al-Joudi is with the Department of Information and Communication Engineering, College of Information Engineering, Al-Nahrain University, Baghdad, Iraq. • H. Al-Raweshidy is with the Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University London, U.K. BER, throughput and energy consumption [1], [10], [11], [12], [13], [14], [15], 16], [17], [18]. In the present work, an arrangement of FEC and ARQ codes is proposed to improve the performance of WSNs, while trying to gain an advantage in the con-‐‑ sumed energy. The proposed coding scheme is Hybrid Automatic Repeat on request (HARQ) where two differ-‐‑ ent FEC codes are serially concatenated followed by ARQ scheme. The two concatenated FEC codes are RS code and convolutional code. Such arrangement may work in a way to reserve the advantages of both the FEC and ARQ. WSN simulator is also built and used to test the proposed coding arrangement. The remaining sections of the paper are organized as follows; in next section the built simulator and the model parameters are described, while Section-‐‑3 gives the de-‐‑ tails of the proposed coding arrangement. Section-‐‑4 rep-‐‑ resents the simulation tests and results of the proposed coding scheme. Assessment of the results is given in Sec-‐‑ tion-‐‑5 followed by the conclusion in Section-‐‑6. 2 WSN SIMULATOR AND MODEL PARAMETERS The nature of WSNs and their applications make them vulnerable to different channel impairments. These in-‐‑ clude whether and other factors such as security, cover-‐‑ age and unreliable communication which make the in-‐‑ formation sent more susceptible to errors. In the built WSNs simulator, there is a need to specify; network to-‐‑ pology, network area dimensions, channel type and pa-‐‑ rameters, number of sensor nodes and number of packets to be transmitted through the network, the presence of 5 node mobility…etc. All these parameters together with transmission and channel specifications have made the need for a universal WSNs simulator an important issue. The simulator here deals with the network performance measures that cover error rates, throughput, and energy consumption. The simulator is built using Matlab and is now subject to patent application. For more details of the simulator, its main stages and flow chart can be found elsewhere [19]. The following summarizes the main features of the used WSNs simulator; a-‐‑ Variety of network area dimensions. b-‐‑ Three types of transmission channel models namely; i-‐‑Additive White Gaussian Noise (AWGN) Channel. ii-‐‑Flat Fading Channel. iii-‐‑Multipath Frequency Selective Fading Channel. c-‐‑ Different number of clusters d-‐‑ Possible mobility of sensor nodes e-‐‑ Different size and number of packets f-‐‑ Varaiety in coding parameters g-‐‑ Different performance measures. Signal-‐‑to-‐‑noise power ratio (SNR) is varied within some ranges and the corresponding performance results are measured. The definition of SNR is given by ; SNR = Eb / N o (dB) (1) Where Eb is the average signal energy per data bit and N o is the single sided power spectral density (PSD) of noise in W/Hz. Binary Phase Shift Keying (BPSK) modulation scheme is considered. The performace results can be seen in the form of Packet Error Rate (PER), Bit Error Rate (BER), Throughput (Thru) in terms of packet per seconds and bit per seconds, and the total remaining energy. PER is determinde by the ratio of the number of incorrect packets to the total number transmitted packets. BER is the ratio of the total number of incorrect bits to the number of transmitted bits. The packet based throughput is defined as the number of correct received packets divided by the interval of the whole transmission. Similar division will go for the bit based througput. The total remaining energy for the over all network can be calculated by the difference between the initial energy set for all network nodes and the total energy consumed by transmission. The packet size and the distance between nodes are taken into account when calculating the energy consumption after each transmission as in the following equation [21]; (J/bit), Emp is the amplifier energy (J/bit/m4), and d nb is the distance between the sending and receiving nodes. The above equation is for the transmission, similar equation can be used for receiption as well with ETX is replaced by E RX (the energy consumed per received bit). The adopted parameters considered in the model for simulation tests are; 1. Number of packets (Np ) = 10000 packets 2. Packet size: 10000 bits 3. Transmission bit rate( R) = 1 Mbps 4. Number of sensor nodes (NN) = 250 nodes 5. Area dimension (DX,DY) = (100,1000) m 6. Number of clusters (NC) = 16 clusters 7. Mobility percentage (Mob%)= 25% of nodes are mobile 3 THE PROPOSED CODING ARRANGEMENT In order to obtain better performance in WSN environ-‐‑ ment, a new coding arrangement is proposed and tested here. The proposed scheme here combines three different coding techniques which are RS, Convolutional and ARQ codes. RS and convolutional codes are presented as seri-‐‑ ally concatenated codes. This arrangement provides bet-‐‑ ter error performance when combined with ARQ. The RS code is used as the outer code and the convolutional code as the inner code along with the ARQ scheme. The latter provides better correction capability on the expense of more consumed energy, thus it is believed that using the three codes together will provide better error perfor-‐‑ mance and energy tradeoff. The simulator used provides different parameter settings for the three different types of coding schemes mentioned. 1-‐‑ The Outer Code Parameters Different combinations of n (codeword length) and k (the data block length) for RS code are provided by the simulator. For each combination there is certain error correction cabability t determined by the relation [20]; n − k = 2t (3) 2-‐‑ The Inner Code Parameters Three parameters are needed for the inner convolutional code. These are : the number of output bits n , the number of input bits k , and the number of memory stages D . 3-‐‑ The ARQ Parameter The only parameter needed for ARQ scheme is the number of retransmissions N. 4 SIMULATION TESTS AND RESULTS Erem = EI − ((ETX + Emp ) × Packet size × (d nb ) 4 ) (2) Three different RS codes are used here with three differ-‐‑ where E I is the initial energy (Joule) for the network nodes, ETX is the energy consumed per transmitted bit ent codewords length (n) ; 255, 511, and 1023. These are also tested with different error correction capabilities (and hence with different number of check symbols) to investi-‐‑ gate the effect of such parameters on the system perfor-‐‑ 6 mance. Three different values are used for the error cor-‐‑ rection capabilities; 8, 16, and 32, resulting in three differ-‐‑ ent lengths for check symbols of 16, 32, and 64, respective-‐‑ ly. ARQ used here is with 4 maximum number of re-‐‑ transmission, while the convolutional code parameters (n, k, D) are (3,1,3). Simulation test results are shown according to given channel. The first is the performance of different coding schemes over AWGN channel in terms of PER, BER, Throughput, and the remaining energy. Similarly, the second and third parts are for Flat fading and Multipath selective fading channels, respectively. These perfor-‐‑ mances are shown for three different error correction schemes with different error capabilities. The performance of the proposed coding arrangement is shown in Figs.1-‐‑5 for AWGN channel with the coding and network parameters as described in the previous sec-‐‑ tion. Figs.6-‐‑10 show the performance of the proposed coding arrangement over flat fading channel with differ-‐‑ ent coding and network parameters. Similar performance is also shown in Figs.11-‐‑15 for frequency selective fading channel with different coding and network parameters as described in the previous section. Fig. 2. Different coding schemes performance over AWGN Channel Fig.1. Different coding schemes performance over AWGN Channel Fig. 3. Different coding schemes performance over AWGN Channel 7 Fig. 6. Different coding schemes performance over flat fading channel Fig. 4. Different coding schemes performance over AWGN Channel Fig. 5.Remaining energy of coding schemes over AWGN Channel Fig. 7. Different coding schemes performance over flat fading channel 8 Fig. 10. Remaining energy of coding schemes over flat fading channel Fig. 8. Different coding schemes performance over flat fading channel Fig. 11. Different coding schemes performance over SUI-3 channel Fig. 9. Different coding schemes performance over flat fading channel 9 Fig. 12. Different coding schemes performance over SUI-3 channel Fig. 14. Different coding schemes performance over SUI-3 channel Fig. 15. Remaining energy of coding schemes over SUI-3 channel Fig. 13. Different coding schemes performance over SUI-3 channel 10 5 ASSESSMENT OF RESULTS Considering the test results of the previous section for AWGN, flat fading, and frequency selective multipath fading channels encourage the use of the proposed cod-‐‑ ing scheme for WSNs. The performance over the three channels considered in the work shows that the coding arrangement with RS code having codeword length (n) of 255 outperforms oth-‐‑ er codeword length selections. This is the least length tested in the work. This means that RS code with small codeword length is a preferred selection and more suita-‐‑ ble for WSNs applications. Packet throughput over the three channels shows that the proposed coding arrangement is more efficient for use with WSNs, where the real applications of WSNs usually rely on transmission of large data units in the form of packets rather than serial bits. Thus the most im-‐‑ portant factor here is to obtain better throughput in terms of packets/sec. Also, the results of BER and throughput in terms of bits per second show that codes with higher n perform better than others. Clearly, this is achieved on the expense of more processing time and complexity. Looking at the performance with remaining energy using different RS codes for the proposed arrangement (Figs 5, 10, and 15) shows that as long as the codeword length is the same, the remaining energy is unaffected. In general, the results show that the proposed coding ar-‐‑ rangement of Hybrid-‐‑ARQ gives an improved perfor-‐‑ mance for WSNs together with noticeable energy saving. 6 CONCLUSIONS Error correction schemes can improve the performance of WSNs transmission in terms of PER, BER and through-‐‑ put. Using ARQ code alone in WSNs consumes more energy due to the extra transmissions required. Thus more energy is required and hence powerful coding schemes are needed for WSNs applications. The proposed concatenated and hybrid coding arrangement for WSNs reduces the number of retransmissions of ARQ compo-‐‑ nent by improving the correction capability of the FEC. This is reflected in the form of improved throughput measured over models of wireless fading channels tested in the work. Thus a better performance/ energy trade-‐‑off is provided by the proposed arrangement REFERENCES [1] O. Eriksson, “Error Control in Wireless Sensor Networks A Pro-‐‑ cess Control Perspective”, Master Thesis, Faculty of Science and Technology, Uppsala University, Sweden, 2011. [2] M. Radi , B. Dezfouli, K. Abu Bakar, and M. Lee, “Multipath Routing in Wireless Sensor Networks: Survey and Research Challenges”, Sensors Journal, Vol.12, January 2012. [3] S. Howard, C. Schlegel, and K. Iniewski, “Error Control Coding in Low-‐‑Power Wireless Sensor Networks:When Is ECC Energy-‐‑ Efficient?”, Eurasip Journal on Wireless Communications and Net-‐‑ working, Vol.2006, Hindawi Publishing Corporation, March 2006. [4] J. Jeong and C. Ee, “Forward Error Correction in Sensor Net-‐‑ works”, International Workshop on Wireless Sensor Networks (IEEE Sponsorship), Morocco, 2003. [5] A. Willig and R. Mitschke, “Results of Bit Error Measurements with Sensor Nodes and Casuistic Consequences for Design of Energy-‐‑Efficient Error Control Schemes”, In Proceeding of the 3rd European Workshop on Wireless Sensor Networks (EWSN), Switzer-‐‑ land, January 2006. [6] G. Balakrishnan, M. Yang, Y. Jiang, and Y. Kim, “Performance Analysis of Error Control Codes for Wireless Sensor Net-‐‑ works”, Fourth International Conference on Information Technology (ITNG 'ʹ07), Nevada, Las Vegas, USA, April 2007. [7] M. Islam, “Error Correction Codes in Wireless Sensor Network: An Energy Aware Approach”, International Journal of Computer and Information Engineering, Vol.4, 2010. [8] A. Angelin, B. Revathi, T. Gayathri and D. Balakumaran, “Channel Coding in WSN for Energy Optimization”, Interna-‐‑ tional Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol.3, March 2014. [9] A. Babiker, M. Nordin and B. Zakaria, “Energy Efficiency Analysis of Error Correction Techniques in Underwater Wire-‐‑ less Sensor Networks”, Journal of Engineering Science and Tech-‐‑ nology, Vol. 6, 2011. [10] D. Schmidt, M. Berning, and N. Wehn, “Error Correction in Single Hop Wireless Sensor Networks -‐‑ A Case Study”, Proceed-‐‑ ing of the 9th International Conference of Design, Automation and Test in Europe (DATE), France, April 2009. [11] M. Vuran and I. Akyildiz, “Error Control in Wireless Sensor Networks: A Cross Layer Analysis”, IEEE/ACM Transactions on Networking, Vol.17, August 2009. [12] M. Jebarani and T. Jayanthy, “An Analysis of Various Parame-‐‑ ters in Wireless Sensor Networks Using Adaptive FEC Tech-‐‑ nique”, International Journal of Ad hoc, Sensor & Ubiquitous Com-‐‑ puting (IJASUC) Vol.1, September 2010. [13] Y. Jin, J. Chang and D. Le, “A High Energy Efficiency Link Layer Adaptive Error Control Mechanism for Wireless Sensor Networks”, International Conference on computational Intelligence and Software Engineering (CiSE), Wuhan, December 2010. [14] U. Datta, D. Kumar, A. Ball and S. Kundu, “Performance of a Hybrid ARQ Scheme in CDMA Wireless Sensor Network”, In-‐‑ ternational Journal of Energy, Information and Communications, Vol. 2, August 2011. [15] G. AL-‐‑Suhail, K. Louis and T. Abdallah, “Energy Efficiency Analysis of Adaptive Error Correction in Wireless Sensor Net-‐‑ works”, International Journal of Computer Science Issues (IJCSI), Vol. 9, July 2012. [16] H. Saraswat, G. Sharma, S. Mishra and Vishwajeet, “Perfor-‐‑ mance Evaluation and Comparative Analysis of Various Con-‐‑ catenated Error Correcting Codes Using BPSK Modulation for AWGN Channel”, International Journal of Electronics and Com-‐‑ munication Engineering, Vol. 5, 2012. [17] B. Manzoor, N. Javaid, O. Rehman, S. Bouk, S. Ahmed and D. Kim, “Energy Aware Error Control in Cooperative Communi-‐‑ cation in Wireless Sensor Networks”, International Conference of ACM Research in Adaptive and Convergent Systems (RACS), Mon-‐‑ treal, Canada, September 2013. [18] C. Cheah, P. Tan, and C. Ho, “Experimental Investigation of Reed-‐‑Solomon Error Correction Technique for Wireless Sensor Network”, International Journal of Information and Electronics En-‐‑ gineering (IJIEE), Vol.4, No.2, March 2014. 11 [19] A. K. Al-‐‑Joudi, "ʺ Error Correction Schemes for Wireless Sensor Networks"ʺ, M.Sc. Thesis submitted to the Department of Infor-‐‑ mation and Communications Engineering, Al-‐‑Nahrain University, Baghdad, Iraq, Jan. 2015. [20] S. Wicker and V. Bhargava, “Reed-‐‑Solomon Codes and Their Applications”, John Wiley and Sons, New York, October 1999. [21] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “An Application-‐‑Specific Protocol Architecture for Wireless Mi-‐‑ crosensor Networks”, IEEE Transactions on Wireless Communica-‐‑ tions, Vol.1, No.4, October 2002.
Report "Error Correction Scheme for Wireless Sensor Networks"