Bayesian fuzzy clustering‐based routing protocol in heterogeneous wireless sensor network using fractional Border Collie optimization

Abstract

SummaryThe major complex issues of routing protocols in heterogeneous wireless sensor network (WSN) are energy balancing and energy efficiency. Though numerous protocols exist in WSN for routing, increasing the system lifetime and to balance the energy consumption still pose a difficult task in the networking scenario. To solve the energy balancing issues and to prolong the network lifetime, an effective routing protocol is designed using the proposed fractional Border Collie optimization (FBCO) algorithm. The proposed FBCO is derived by incorporating fractional calculus (FC) with Border Collie Optimization (BCO), respectively. To make the routing process more efficient, it is significant to group the nodes in the form of cluster such that the formation of cluster is made using Bayesian fuzzy clustering (BFC). The fitness function is designed by taking into account the multiobjective constrictions, like distance, delay, latency, route link time (RLT), and energy. With these objective factors, the optimal value is computed for each solution in the search space such that the computation of the optimal value enables the routing protocol to enhance energy consumption and network lifespan. The performance achieved by the introduced FBCO‐based routing procedure based on delay is 0.3470 s, latency is 0.3142 s, throughput is 97.58%, and residual energy is 0.1726 J by considering 100 numbers of nodes.