Energy Aware Metaheuristics based Path Planning Technique with Mobile Sinks for Wireless Sensor Networks

Abstract

In recent years, wireless sensor networks (WSN) becomes a vital part of the emerging Internet of Things (IoT) due to their applicability in several real time applications. But a crucial challenge that exists in the WSN is due to maximum energy dissipation of the nodes and minimum network lifetime. Some of the possible solutions to accomplish reduced energy utilization and increased lifetime of WSN are clustering, routing, data aggregation, etc. With this motivation, this article introduces an energy aware metaheuristics based path planning with mobile sinks (EAM-PPMS) technique for WSN. The goal of the EAM-PPMS technique is to choose cluster heads (CHs) and optimal paths for MS. The EAM-PPMS technique initially performs chicken swarm optimization (CSO) based clustering process to pick out a set of CHs and organize the network into a set of clusters. Besides, the water strider algorithm (WSA) based path planning technique for MS is derived to reach the destination in an optimal way. The path planning technique is mainly based on the derivation of objective function with the minimization of cost, distance, and delay. The extensive simulation analysis of the EAM-PPMS technique is carried out and the results are inspected in terms of different measures like NODN Analysis, Loss packet and NRE Analysis for both Homogeneous and Heterogeneous Environments. The simulation results portrayed the betterment of the EAM-PPMS technique compared to the recent approaches.