Improving energy efficiency in WSN through adaptive memetic-based clustering and routing for resource management

Abstract

Efficient resource allocation in Wireless Sensor Networks (WSNs) is essential due to the constrained energy resources of sensor nodes and complex network dynamics. Existing clustering and routing methods often fail to optimize energy usage and ensure network stability under varying conditions. This research article introduces the Hybrid Memetic Evolutionary Algorithm (HMEA), which combines adaptive memetic-based clustering and evolutionary optimization to address energy-efficient clustering and routing. The HMEA dynamically selects cluster heads and optimizes transmission paths considering node energy levels and network topology, minimizing energy consumption and extending network lifetime. Simulation results demonstrate that the HMEA outperforms conventional methods, including Particle Swarm Optimization and Genetic Algorithm, in terms of energy efficiency, network throughput, and packet delivery ratio, particularly in large-scale networks. This approach advances robust resource allocation mechanisms for sustainable WSN operations.