A boolean spider monkey optimization based energy efficient clustering approach for WSNs
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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.
KeywordsSMO binSMO WSN Network lifetime Stability period
- 3.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.
- 4.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.
- 10.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.
- 11.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.
- 12.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.
- 16.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).Google Scholar
- 17.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.Google Scholar