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HSCA: a novel harmony search based efficient clustering in heterogeneous WSNs

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Abstract

Clustering objective reasons scalability, fault tolerance, data aggregation or fusion, load balancing of cluster heads, stabilized network topology, maximal network lifetime, increased connectivity, reduced routing delay, collision avoidance and utilizing sleeping schemes in wireless sensor networks. Load balanced clustering effectively organize the network into a connected hierarchy. Clustering is a discrete problem that can have more than one solution under different operating constraints. In this scenario, meta-heuristic algorithms are found suitable because they give set of solutions in acceptable time constraints. In the literature, several analytical and meta-heuristic approaches have been developed for load balanced clustering. In this paper, a novel harmony search based energy efficient load balanced clustering algorithm is presented and it is tested on a large sample network. Results demonstrated that the proposed approach has faster convergence and gives reliable and efficient load balanced clustering as compared to conventional harmony search algorithm (HSA) and several other methods in the literature. Moreover, the robustness of the proposed approach is also verified for different cases of fixed and variable parameters of HSA.

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References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  2. Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67, 104–122.

    Article  Google Scholar 

  3. Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330. doi:10.1016/j.comnet.2008.04.002.

    Article  Google Scholar 

  4. Akkaya, K., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 3(3), 325–349.

    Article  Google Scholar 

  5. Khan, J. A., Qureshi, H. K., & Iqbal, A. (2015). Energy management in wireless sensor networks: A survey. Computers & Electrical Engineering, 41, 159–176.

    Article  Google Scholar 

  6. Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30(14), 2826–2841. doi:10.1016/j.comcom.2007.05.024.

    Article  Google Scholar 

  7. Afsar, M. M., & Tayarani-N, M. H. (2014). Clustering in sensor networks: A literature survey. Journal of Network and Computer Applications, 46(2014), 198–226. doi:10.1016/j.jnca.2014.09.005.

    Article  Google Scholar 

  8. Singh, S., & Sharma, R. M. (2016). Optimization techniques in wireless sensor networks. In ACM ICPS Proceedings of the 2016 international conference on information and communication technology for competitive strategies, ICTCS 2016 ACM (p. 7).

  9. Singh, S., & Sharma, R. M. (2016). Localization system optimization in wireless sensor networks (LSO-WSN). Handbook of research on wireless sensor network trends, technologies, and applications of AWTT Book Series (Eds.). IGI Global. doi:10.4018/978-1-5225-0501-3.ch001.

  10. Singh, S., & Sharma, R. M. (2015). Some aspects of coverage awareness in wireless sensor networks. Procedia Computer Science, 70, 160–165. doi:10.1016/j.procs.2015.10.065.

  11. Jiang, C., Yuan, D., & Zhao, Y. (2009). Towards clustering algorithms in wireless sensor networks—A survey. In Wireless communications and networking conference, 2009. WCNC 2009. IEEE (pp. 1–6). IEEE. doi:10.1109/WCNC.2009.4917996.

  12. Boyinbode, O., Le, H., & Takizawa, M. (2011). A survey on clustering algorithms for wireless sensor networks. International Journal of Space-Based and Situated Computing, 1(2–3), 130–136.

    Article  Google Scholar 

  13. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on System sciences, 2000, vol. 2 (pp. 3005–3014). IEEE. doi:10.1109/HICSS.2000.926982.

  14. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670. doi:10.1109/TWC.2002.804190.

    Article  Google Scholar 

  15. Kuila, P., & Jana, P. K. (2014). Approximation schemes for load balanced clustering in wireless sensor networks. The Journal of Supercomputing, 68(1), 87–105. doi:10.1007/s11227-013-1024-6.

    Article  Google Scholar 

  16. Tarachand, A., Kumar, V., Raj, A., Kumar, A., & Jana, P. K. (2012). An energy efficient load balancing algorithm for cluster-based wireless sensor networks. In India conference (INDICON), 2012 Annual IEEE. IEEE (pp. 1250–1254).

  17. Gupta, G., & Younis, M. (2003). Performance evaluation of load-balanced clustering of wireless sensor networks. In 10th International conference on Telecommunications, 2003. ICT 2003, IEEE (Vol. 2, pp. 1577–1583). doi:10.1109/ICTEL.2003.1191669.

  18. Bari, A., Jaekel, A., & Bandyopadhyay, S. (2008). Clustering strategies for improving the lifetime of two-tiered sensor networks. Computer Communications, 14(31), 3451–3459.

    Article  Google Scholar 

  19. Kuila, P., Gupta, S. K., & Jana, P. K. (2013). A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm and Evolutionary Computation, 12(2013), 48–56. doi:10.1016/j.swevo.2013.04.002.

    Article  Google Scholar 

  20. Hussain, S., Matin, A. W., & Islam, O. (2007). Genetic algorithm for hierarchical wireless sensor networks. Journal of Networks, 2(5), 87–97.

    Article  Google Scholar 

  21. Das, S., & Suganthan, P. N. (2011). Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 15(1), 4–31. doi:10.1109/TEVC.2010.2059031.

    Article  Google Scholar 

  22. Low, C. P., Fang, C., Ng, J. M., & Ang, Y. H. (2008). Efficient load-balanced clustering algorithms for wireless sensor networks. Computer Communications, 31(4), 750–759.

    Article  Google Scholar 

  23. Kuila, P., & Jana, P. K. (2012). Energy efficient load-balanced clustering algorithm for wireless sensor networks. Procedia Technology, 6, 771–777.

    Article  Google Scholar 

  24. Chiang, S. S., Huang, C. H., & Chang, K. C. (2007). A minimum hop routing protocol for home security systems using wireless sensor networks. IEEE Transactions on Consumer Electronics, 53(4), 1483–1489. doi:10.1109/TCE.2007.4429241.

    Article  Google Scholar 

  25. Gupta, S. K., Kuila, P., & Jana, P. K. (2013). GAR: An energy efficient GA-based routing for wireless sensor networks. In ICDCIT (pp. 267–277).

  26. Kuila, P., & Jana, P. K. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127–140.

    Article  Google Scholar 

  27. Gupta, S. K., & Jana, P. K. (2015). Energy efficient clustering and routing algorithms for wireless sensor networks: GA based approach. Wireless Personal Communications, 83(3), 2403–2423.

    Article  Google Scholar 

  28. Yang, X. S. (2009). Harmony search as a metaheuristic algorithm. In Music-inspired harmony search algorithm (Vol. 191, pp. 1–14). Berlin, Heidelberg: Springer.

  29. Kumar, P., & Singh, S. (2014). Reconfiguration of radial distribution system with static load models for loss minimization. In 2014 IEEE international conference on power electronics, drives and energy systems (PEDES) IEEE (pp. 1–5). doi:10.1109/PEDES.2014.7042011.

  30. Kumar, P., Ali, I., Thomas, M., & Singh, S. (2017). Imposing voltage security and network radiality for reconfiguration of distribution systems using efficient heuristic and meta-heuristic approach. IET Generation, Transmission & Distribution.,. doi:10.1049/iet-gtd.2016.0935.

    Google Scholar 

  31. Lee, K. S., & Geem, Z. W. (2004). A new structural optimization method based on the harmony search algorithm. Computers & Structures, 82, 781–798. doi:10.1016/j.compstruc.2004.01.002.

    Article  Google Scholar 

  32. Lee, K. S., & Geem, Z. W. (2005). A new meta-heuristic algorithm for continuous engineering optimization: Harmony search theory and practice. Computer Methods in Applied Mechanics and Engineering, 194, 3902–3933. doi:10.1016/j.cma.2004.09.007.

    Article  Google Scholar 

  33. Geem, Z. W. (2010). Recent advances in harmony search algorithm (Vol. 270). Springer.

  34. Das, S., Mukhopadhyay, A., Roy, A., Abraham, A., & Panigrahi, B. K. (2011). Exploratory power of the harmony search algorithm: Analysis and improvements for global numerical optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41, 89–106. doi:10.1109/TSMCB.2010.2046035.

    Article  Google Scholar 

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Correspondence to Surjit Singh.

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Singh, S., Sharma, R.M. HSCA: a novel harmony search based efficient clustering in heterogeneous WSNs. Telecommun Syst 67, 651–667 (2018). https://doi.org/10.1007/s11235-017-0365-5

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