Adaptive Weight-based Dollmaker Optimization Algorithm for Energy Efficient clustering and routing in Wireless Sensor Network

Wireless sensor network (WSN) is a self-optimizing network composed of many small sensor nodes used to monitor a wide range of applications. The main objective of this clustering and routing is to obtain high network lifetime with less energy usage. Therefore, this research proposes an Adaptive Weight-based Dollmaker Optimization Algorithm (AWDOA) for Cluster Head (CH) and route selection in Internet of Things (IoT) based Wireless Sensor Network (WSN). The AWDOA enhances both efficiency and precision of CH and route thereby enhancing overall performance. It enables to dynamically adopts exploration and exploitation by enabling search space in complex solutions. Its flexibility enhances convergence speed and avoid local optima issues thereby ensuring optimal network lifetime. The residual energy, node degree, communication cost and distance are measured as fitness function for AWDOA. The Energy Consumption (EC), Network Lifetime (NL) and Packet Delivery Ratio (PDR) metrics are considered to show effectiveness of AWDOA. The AWDOA achieves 31.63mJ of EC, 6850rounds of NL and 99.86% of PDR for 200 no. of nodes.