BioWSN: A Bio-Inspired Method for Optimization of Routing in Wireless Sensor Networks

Wireless sensor networks (WSN) have been recently gaining traction for many applications in monitoring and surveillance systems the physical world specifically agriculture, healthcare, smart cities. Many clustering routing approaches introduced to reduce consumption of energy WSNs increase lifetime network. In this study, we propose an improved version grey wolf optimizer (GWO), a nature-inspired metaheuristic optimization algorithm, perform cluster head selection WSN while maximizing WSN. GWO has propensity converge local optima. To overcome drawback conventional GWO, introduce balancing factor between exploration exploitation phases algorithm addition mapping scheme. Comparative simulation analysis proposed show significant improvement compared frequently used well-known namely LEACH PSO.