Hybrid Metaheuristic Approach based Bio-Inspired Framework for Energy-Efficient Clustering and Routing in Next-Generation Wireless Sensor Networks

Abstract

Researchers currently face significant challenges in developing long-lived and energy-efficient clustering and routing protocols for wireless sensor networks (WSNs) in various domains, including military, agriculture, edu cation, and environmental monitoring. WSNs have profoundly affected various aspects of human existence. Ex isting routing protocols have mainly focused on cluster head selection but have ignored critical elements of routing such as clustering, data aggregation, and security. Although cluster-based routing has played a significant role in solving these problems, there is definitely area for improvement in the cluster head selection process (Cluster Head) by integrating key features. Biologically inspired algorithms are gaining recognition as a practical approach to addressing key issues in WSNs, including sensor lifetime and transmission range. Nevertheless, the wireless nature of sensor node batteries presents a challenge in replacing them when deployed in remote or unattended regions. Consequently, significant research efforts are underway to enhance the longevity of these nodes. The performance of a WSN relies on both device design and the architecture of its nodes. Maximizing device lifespan and range, minimizing energy consumption, and achieving optimal connectivity and high transmission rates are currently imperative goals. Metaheuristic algorithms, including DE (Differential Evolution), GA (Genetic Algo rithm), PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization), SFO (Social Fish Optimization), and GWO (Grey Wolf Optimization), offer numerous benefits such as simplicity, versatility, and independence from derivative calculations. These algorithms effectively utilize the energy resources of wireless sensor networks (WSNs) by clustering nodes, leading to an increased overall network lifespan. This research paper focuses on the exploration of hybridization techniques, such as DE-GA, GA-PSO, PSO-ACO, PSO-ABC, PSO-GWO, and oth ers, to enhance the energy efficiency of WSNs using bioinspired algorithms. It also addresses critical issues by accelerating the implementation process, enabling more efficient data transmission, and reducing energy con sumption through the application of bioinspired hybrid optimization algorithms.