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Prime goal of WSN deployment in large specific area aims to sense the environment and execute the defined application with the help of essential location information of the devices. Through localization technique, location information is assigned to the unknown devices within the area of interest. Due to its definite solution capabilities with fast convergence rate, bio inspired application become popular to solve numerous applications in the field of wireless sensor network (WSN) applications. In this paper, a newly developed meta- heuristic algorithm based on the social behavior of chickens named as chicken swarm optimization (CSO) is proposed to solve the WSN node localization problem. Two performance metrics which are node precision and computation time are investigated using three different bio inspired algorithms that are particle swarm optimization (PSO), binary particle swarm optimization (BPSO) and penguin search optimization algorithm (PeSOA) respectively. Results are demonstrated using simulation graph where CSO performs more precise accuracy having a ratio of 55% over PSO and BPSO and 10% over PeSOA. For computation time, proposed algorithm performs a computation time that is shorter by 30% than PeSOA as well as 50 and 40% than PSO and BPSO, respectively.
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Boukerche, A., Oliveira, H. A. B. F., Nakamura, E. F., & Loureiro, A. A. F. (2007). Localization systems for wireless sensor networks. Wireless Communications, IEEE, 14(6), 6–12. CrossRef
Doherty, L., Pister, K. S. J., & El Ghaoui, L. (2001). Convex position estimation in wireless sensor networks. In INFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE (Vol 3, pp. 1655–1663). IEEE, Piscataway.
Pottie, G. J., & Kaiser, W. J. (2000). Wireless integrated network sensors. Communications of the ACM, 43(5), 51–58. CrossRef
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422. CrossRef
Zhang, Q., Huang, J., Wang, J., Jin, C., Ye, J., Zhang, W., & Hu, J. (2008). A two-phase localization algorithm for wireless sensor network. In International Conference on Information and Automation, 2008. ICIA 2008. (pp. 59–64). IEEE, Piscataway.
Niculescu, D., & Nath, B. (2003). Ad hoc positioning system (aps) using aoa. In INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies (Vol 3, pp. 1734–1743). IEEE, Piscataway.
Bulusu, N., Estrin, D., Girod, L., & Heidemann, J. (2001). Scalable coordination for wireless sensor networks: Self-configuring localization systems. In International Symposium on Communication Theory and Applications (ISCTA 2001) Ambleside, UK.
Savvides, A., Park, H., & Srivastava, M. B. (2002). The bits and flops of the n-hop multilateration primitive for node localization problems. In Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications (pp. 112–121). ACM, London.
Kannan, A. A., Mao, G., & Vucetic, B. (2005). Simulated annealing based localization in wireless sensor network. In The IEEE Conference on Local Computer Networks, 2005. 30th Anniversary, (p. 2). IEEE, Piscataway.
Kulkarni, R. V., Venayagamoorthy, G. K., & Cheng, M. X. (2009). Bio-inspired node localization in wireless sensor networks. In IEEE International Conference on Systems, Man and Cybernetics, 2009. SMC 2009 (pp. 205–210). IEEE, Piscataway.
Yun, S., Lee, J., Chung, W., Kim, E., & Kim, Soohan. (2009). A soft computing approach to localization in wireless sensor networks. Expert Systems with Applications, 36(4), 7552–7561. CrossRef
Gopakumar, A., & Jacob, L. (2008). Localization in wireless sensor networks using particle swarm optimization. In IET International Conference on Wireless, Mobile and Multimedia Networks, 2008 (pp. 227–230). IET.
Kumar, A., Khosla, A., Saini, J. S., & Singh, S. (2012) Computational intelligence based algorithm for node localization in wireless sensor networks. In 2012 6th IEEE International Conference on Intelligent Systems (IS) (pp. 431–438). IEEE, Piscataway.
Stoleru, R., & Stankovic, J. A. (2004). Probability grid: A location estimation scheme for wireless sensor networks. In 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004 (pp. 430–438). IEEE, Piscataway.
Chuang, P.-J., & Wu, C.-P. (2008). An effective pso-based node localization scheme for wireless sensor networks. In Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies, 2008. PDCAT 2008 (pp. 187–194). IEEE, Piscataway.
Rencheng, J., Hongbin, W., Bo, P., & Ning, P. (2008). Research on rssi-based localization in wireless sensor networks. In 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008. WiCOM’08 (pp. 1–4). IEEE, Piscataway.
Kang, J., Kim, D., & Kim, Y. (2007). RSS self-calibration protocol for WSN localization. In 2nd international symposium on wireless pervasive computing, ISWPC'07. IEEE.
Meng, X., Liu, Y., Gao, X., & Zhang, H. (2014). A new bio-inspired algorithm: Chicken swarm optimization. In Advances in swarm intelligence (pp. 86–94). Springer, Berlin.
- Bio Inspired Distributed WSN Localization Based on Chicken Swarm Optimization
Md Al Shayokh
Soo Young Shin
- Publication date
- Springer US