Empirical Analysis of Developing EC Based Algorithms for Various WSN Network Applications

Wi-Fi Sensor Networks (WSNs) have emerged as a transformative era for several applications, from environmental monitoring to protection systems, due to improvements in wi-fi verbal exchange and sensor technologies. The deployment of Mobile Wireless Sensor Networks (MWSNs) introduces the task of maximizing community lifetime at the equal time as making sure green facts series and processing. This test proposes a novel technique to extend the lifetime of MWSNs by using way of leveraging Evolutionary Computing (EC) techniques. Through a comparative assessment of five EC algorithms-Genetic Algorithms (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Neighborhood Field Optimization (NFO)-the research highlights the strengths and limitations of every method in optimizing the energy performance of MWSNs. The results show the functionality of EC strategies in enhancing the sustainability and operational basic overall performance of WSNs, presenting treasured insights for destiny studies and realistic programs in the subject.