Nature-Inspired Energy Enhancement Technique for Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSN) play a major role in various applications, yet maintaining energy efficiency remains a critical challenge due to their limited energy availability. Network lifetime is one of the primary parameters for analyzing the performance of a WSN. This proposed work aims to improve the network lifetime of a WSN by enhancing its energy utilization through the Enhanced Monkey Search Algorithm (E-MSA). The E-MSA provides an optimum solution for this issue by finding a better routing decision by analyzing the available energy on the nodes and the distance between the source and destination. Additionally, a Class Topper Optimization (CTO) algorithm is also included in the work for determining an efficient node to be the cluster head and lead cluster head. In this technique, the data packets are collected by the lead cluster head from the other cluster heads for sending the information in a sequential manner to the base station for reducing data loss. A simulation model is implemented in the NS2 platform with 700 nodes in a 300 × 300 square meter area with 0.5 J of energy to each node for finding the efficiency of the proposed E-MSA with CTO algorithm over the traditional On-Demand Distance Vector (ODV) and Destination-Sequenced Distance Vector (DSDV) approaches. The experimental outcome indicates that the proposed work can reach a maximum lifetime of 1579 s which is comparatively better than the ODV and DSDV approaches by 212 and 358 s, respectively. Similarly, a packet delivery ratio of 79% is achieved with a throughput of 0.85 Mbps along with a delay of 0.48 s for the operation of all 700 nodes.