An Energy Efficient Evolutionary Algorithm based Clustering Protocol for WSNs

Wireless sensor networks (WSNs) is an emerging area for the reserchers for its easiness in deployment. WSNs are adhoc networks in which small sensor nodes are scattered to form network. These sensor nodes having limited resources in terms of energy, memeoy and power etc. WSNs are rapidly growing technology which is very useful for numerous environment applications. In this research, the main target is on escalassions of network lifetime. The technique of clustering in which aggregation of data is being taken place at the end of cluster head (CH) inclines specific eloquent factors to enlarge life-span of the network. In this paper, a hybrid biogeography based optimization (BBO) with differential evolution (DE), namely BBO/DE is introduced for optimal selection of CHs. BBO is a latest biogeography inspired method, in which migration operator is used for exchanging the data/information among different solutions. On the other hand DE is best known for its global optimization. The hybrid combination of BBO and DE effectively counterbalances the exploitation of BBO followed by exploration of DE. This approach generates the number of best candidate solutions. Further, the simulation results demonstrate that the schemed algorithm outperforms as compared to existing algorithms in terms of system lifetime and energy dissipation. Keywords— Wireless Sensor Network, Differential Evolution, Biogeography Based Optimization, clustering, Network Lifetime, Residual energy.