Abstract
Energy consumption of sensor nodes is one of the major challenges in wireless sensor networks (WSNs). Therefore, to defeat this challenge clustering technique is used. In cluster based WSN, the leader of cluster, called cluster head (CH) collects, aggregates, and sends data to the base station. Hence, data load balancing is also one of the crucial tasks in WSN. To overcome this problem, we use two bio-inspired algorithms for clustering namely Grey Wolf Optimization (GWO) and Genetic Algorithm (GA). The best fitted solutions from GWO and GA undergo the crossover and mutation operations to produce healthy off-springs. The clustering solution obtained from GWO-GA is well load balanced and energy efficient. We compare GWO-GA approach with some of the existing algorithms over fitness values and different network parameters namely first sensor node dies and half of the sensor nodes are alive in the network. We observe GWO-GA outperforms existing algorithms.
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Lipare, A., Reddy Edla, D., Cheruku, R., Tripathi, D. (2020). GWO-GA Based Load Balanced and Energy Efficient Clustering Approach for WSN. In: Zhang, YD., Mandal, J., So-In, C., Thakur, N. (eds) Smart Trends in Computing and Communications. Smart Innovation, Systems and Technologies, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-15-0077-0_29
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DOI: https://doi.org/10.1007/978-981-15-0077-0_29
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