Maximising node coverage in WSNs using adaptive crossover mutation differential evolution

Wireless sensor networks (WSNs) are essential in environmental monitoring, surveillance systems, and smart cities. Achieving optimal coverage in WSNs is a fundamental challenge for effective event detection and monitoring. This research introduces an adaptive crossover mutation differential evolution (ADE) approach to address the node coverage planning problem in WSNs. The ADE method optimises sensor placement to maximise coverage while minimising the number of required sensors. It incorporates enhanced adaptive strategies for crossover, mutation, and re-initialisation to improve coverage efficiency. The ADE approach is evaluated using test suite functions and compared against existing optimisation strategies using various metrics for validation. Experimental results demonstrate the superior performance of the proposed approach in maximising node coverage in WSNs. The ADE approach presents a novel and effective solution for optimising coverage in WSNs with potential practical applications.