Pruning and Validation Techniques Enhanced Genetic Algorithm for Energy Efficiency in Wireless Sensor Networks

Designing energy-efficient systems in Wireless Sensor Networks (WSNs) is challenging as each sensor has limited energy.This research paper suggests a combined method that merges a Genetic Algorithm (GA) with pruning and validation strategies to enhance sensor network routing paths to minimize energy usage.The GA uses variable-length chromosomes to depict paths from a source sensor node to a sink node.Initial populations are created randomly and genetic mechanisms such as selection, crossover, and mutation are applied to refine these paths for efficiency.Pruning methods are then used to remove redundant nodes in the obtained paths ensuring energy-efficient routing.Path validation in the GA processes ensures that each path adheres to the transmission range limits of sensors.The experiments use setups with 20, 50, 100, and 150 sensor nodes.Results have shown that this approach chooses the best paths with minimal energy consumption and it is superior to the Ant Colony Optimization (ACO) algorithm.