Abstract
Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of cluster-based routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. Spider monkey optimization (SMO) is a relatively new nature inspired evolutionary algorithm based on the foraging behaviour of spider monkeys. It has proved its worth for benchmark functions optimization and antenna design problems. In this paper, SMO based threshold-sensitive energy-efficient clustering protocol is proposed to prolong network lifetime with an intend to extend the stability period of the network. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.
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References
Afsar, M. M., & Tayarani-N, M. (2014). Clustering in sensor networks: A literature survey. Journal of Network and Computer Applications, 46, 198–226. doi:10.1016/j.jnca.2014.09.005.
Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-Efficient routing protocols in wireless sensor networks: A survey. IEEE Communications, Surveys and Tutorials, 15(2), 551–591. doi:10.1109/SURV.2012.062612.00084.
Heinzelman, W. B., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of 33rd annual Hawaii international conference on system sciences (HICSS-33), IEEE (p. 223). doi: 10.1109/HICSS.2000.926982.
Smaragdakis, G., Matta, I., & Bestavros, A. (2004). SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. In Proceedings of international workshop on SANPA. http://open.bu.edu/xmlui/bitstream/handle/2144/1548/2004-022-sep.pdf?sequence=1.
Qing, L., Zhu, Q., & Wang, M. (2006). Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor network. Computer Communications, 29, 2230–2237. doi:10.1016/j.comcom.2006.02.017.
Kumar, D., Aseri, T. C., & Patel, R. B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications, 32, 662–667. doi:10.1016/j.comcom.2008.11.025.
Kumar, D. (2014). Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks. IET Wireless Sensor Systems, 4(1), 9–16. doi:10.1049/iet-wss.2012.0150.
Kang, S. H., & Nguyen, T. (2012). Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Communications Letters, 16(9), 1396–1399. doi:10.1109/LCOMM.2012.073112.120450.
Tarhani, M., Kavian, Y. S., & Siavoshi, S. (2014). SEECH: Scalable energy efficient clustering hierarchy protocol in wireless sensor networks. IEEE Sensors Journal, 14(11), 3944–3954. doi:10.1109/JSEN.2014.2358567.
Tyagi, S., Gupta, S. K., Tanwar, S., & Kumar, N. (2013). EHE-LEACH: Enhanced heterogeneous LEACH protocol for lifetime enhancement of wireless SNs. In Proceedings of international conference on advances in computing, communications and informatics (ICACCI), August 22–25, 2013, Mysore, India (pp. 1485–1490). doi:10.1109/ICACCI.2013.6637399.
Aderohunmu, F. A., Deng, J. D., & Purvis, M. K. (2011). A deterministic energy-efficient clustering protocol for wireless sensor networks. In Proceedings of 7th international conference on intelligent sensors, sensor networks and information processing (ISSNIP ‘11), IEEE (pp. 341–346). doi: 10.1109/ISSNIP.2011.6146592.
Manjeshwar, A. & Agrawal, D. P. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In Proceedings international parallel and distributed processing symposium (IPDPS’01) workshops, 2001 (pp. 2009–2015), San Francisco, CA, USA. doi: 10.1109/IPDPS.2001.925197.
Mittal, N., & Singh, U. (2015). Distance-based residual energy-efficient stable election protocol for WSNs. Arabian Journal of Science and Engineering, 40(6), 1637–1646. doi:10.1007/s13369-015-1641-x.
Mittal, N., Singh, U., & Sohi, B. S. (2016). A stable energy efficient clustering protocol for wireless sensor networks. Wireless Networks. doi:10.1007/s11276-016-1255-6.
Adnan, Md A, Razzaque, M. A., Ahmed, I., & Isnin, I. F. (2014). Bio-Mimic optimization strategies in wireless sensor networks: A survey. Sensors, 14, 299–345. doi:10.3390/s140100299.
Jin, S., Zhou, M., & Wu, A. S. (2003). Sensor network optimization using a genetic algorithm. In 7th World multi-conference on systemics, cybernetics and informatics, Orlando, FL, USA (pp. 1–6).
Hussain, S. & Matin, A. W. (2006). Hierarchical cluster-based routing in wireless sensor networks. In IEEE/ACM international conference on information processing in sensor networks, IPSN.
Khalil, E. A., & Attea, B. A. (2011). Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm and Evolutionary Computation. doi:10.1016/j.swevo.2011.06.004.
Attea, B. A., & Khalil, E. A. (2012). A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Applied Soft Computing, 12, 1950–1957. doi:10.1016/j.asoc.2011.04.007.
Rao, P. C., & Banka, H. (2015). Energy efficient clustering algorithms for wireless sensor networks: Novel chemical reaction optimization approach. Wireless Networks. doi:10.1007/s11276-015-1156-0.
Rao, P. C., & Banka, H. (2016). Novel chemical reaction optimization based unequal clustering and routing algorithms for wireless sensor networks. Wireless Networks. doi:10.1007/s11276-015-1148-0.
Rao, P. C., Jana, P. K., & Banka, H. (2016). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Networks. doi:10.1007/s11276-016-1270-7.
Shokouhifar, M., & Jalali, A. (2015). A new evolutionary based application specific routing protocol for clustered wireless sensor networks. International Journal of Electronics and Communications, 69, 432–441.
Bansal, J. C., Sharma, H., Jadon, S. S., & Clerc, M. (2014). Spider monkey optimization algorithm for numerical Optimization. Memetic Computing, 6, 31–47.
Singh, U., & Salgotra, R. (2016). Optimal synthesis of linear antenna arrays using modified spider monkey optimization. Arabian Journal for Science and Engineering. doi:10.1007/s13369-016-2053-2.
Singh, U., Salgotra, R., & Rattan, M. (2016). A novel binary spider monkey optimization algorithm for thinning of concentric circular antenna arrays. IETE Journal of Research. doi:10.1080/03772063.2015.1135086.
Al-Azza, A. A., Al-Jodah, A. A., & Harackiewicz, F. J. (2015). Spider monkey optimization: A novel technique for antenna optimization. IEEE Antennas and Wireless Propagation Letters. doi:10.1109/LAWP.2015.2490103.
Karl, H., & Willig, A. (2005). Protocols and architectures for wireless sensor networks. New York: Wiley.
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Mittal, N., Singh, U., Salgotra, R. et al. A boolean spider monkey optimization based energy efficient clustering approach for WSNs. Wireless Netw 24, 2093–2109 (2018). https://doi.org/10.1007/s11276-017-1459-4
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DOI: https://doi.org/10.1007/s11276-017-1459-4