Model for enhancing wireless sensor network energy efficiency through genetic algorithm-driven optimization

Abstract

Wireless Sensor Networks (WSNs) are important and key applications in modern monitoring and data acquisition tasks. Their finite energy sources are a key challenge towards network lifetime extension and optimal operation in WSNs. This research puts forward a model enhancing energy efficiency in WSNs based on a Genetic Algorithm-driven optimization technique. The model makes intelligent node and routing-path selection in order to achieve energy minimization while providing accurate data and coverage. The performance of the GA-based model is compared as against random and all-node selection methods with different network size and blocked nodes' probabilities in the network. Simulation demonstrates the GA-based method outcompeting random and all-node selection methods in terms of energy savings and network lifetime extension. This research benefits the design and development towards more sustainable and energy-efficient WSNs, especially in dynamic constraint and energy constrained environments.