Bat Swarm Algorithm for Wireless Sensor Networks Lifetime Optimization

Challenges of wireless sensor networks under-optimization in field of research have been globally concerned. Generally, lifetime extension is still considered to be the most dominant challenge for WSNs. Clustering and routing protocols have been proposed as optimization solutions to extend WSNs lifetime. In this paper, we introduce a newly meta-heuristic population based soft computing algorithm as an optimization technique to extend the WSNs lifetime. The proposed technique applies the population-based meta-heuristic bat swarm optimization algorithm. It optimizes the network as a nonlinear problem to select the optimum cluster head nodes across number of generations. The objective; fitness function, employed to minimize the intra-cluster compactness with minimum distance between nodes in same cluster. The proposed technique is simulated and applied into four different wireless sensor networks deployments and compared with the LEACH hierarchal clustering and routing protocol. Results show that this proposed technique outperforms the classical LEACH. It efficiently optimizes the selection of cluster head nodes that ensure optimum coverage and connectivity based on intra-cluster distances. This reduces the energy consumption on each node level and hence increases the lifetime for each node, causing a significant extension in the wireless sensor network lifetime. The comparison between the hard or crisp LEACH routing and the soft or elastic proposed routing technique boasts the performance even more. The paper introduces a performance numerical analysis with the metrics of number of packets sent to sink, number of dead nodes, sum of WSN energy and the network lifetime.