Enhancing energy efficiency using metaheuristic methods for adaptive clustering and routing protocols in wireless sensor network

Abstract

A Wireless Sensor Network (WSN) is a group of sensors with restricted amount of battery capacity and processing ability deployed over a region for a specific purpose. As WSN nodes are powered by battery, their lifespan is shortened as they run out of battery after certain time duration. Lifetime of sensor node can be enhanced through optimizing energy usage. Efficient routing and clustering approaches can help in energy optimization of WSN. Even though various techniques are adopted for optimal resource utilization in WSN, they lack fault tolerance, and has storage and computational complexity. Moreover, routing and clustering approaches fail to meet the optimal QoS requirements. Through two modules for effective routing and clustering, the research proposed seeks to enhance network lifespan and simultaneously optimizing energy usage. Initially, a unique Energy Efficient Cluster based Adaptive Routing (ECAR) method is suggested for vast WSN deployment in the first module. Routing and Cluster Head (CH) choosing are performed in the suggested approach to maximize energy efficiency of WSN. The Genetic Bee Colony Algorithm (GBCA), that offers a useful method for choosing CHs according to node centralities, node degrees, distance to neighbors, and residual energy, is initially described. As a result, the Quantum Inspired African Vulture Optimization method (QIAVO) identifies the optimal path to use the CHs to guide the data packets from the initial node to the target node. To maximize system efficiency, QIAVO utilizes several elements, like residual energy, node degree, and distance. newline