An Intelligent Fusion of Machine Learning and Optimization for Performance Improvisation in Wireless Sensor Networks

In present days, Wireless Sensor Networks (WSNs) have become a significant scientific advancement with a wide range of applications, spanning from environmental monitoring to industrial automation. However, the obstacles presented by restricted resources of energy, ever-changing settings, and fluctuating communication situations require inventive strategies to optimize their effectiveness. This work introduces a novel integration of machine learning and optimization methodologies with the objective of enhancing the efficiency of WSNs. Here, we used the concept of a Clustering-based routing mechanism with the Cuckoo Search Algorithm (CSA) and Neural Network (NN) as a machine learning approach. This work aims to examine the incorporation of machine learning techniques and optimization methodologies in order to tackle the complex issues encountered by WSNs. The evaluation of the suggested methodology in comparison to standard methodologies involves the utilization of performance measures such as energy consumption, network lifetime, and packet delivery ratio. The findings indicate significant improvements in energy efficiency, network stability, and data throughput, hence confirming the effectiveness of the integrated approach using machine learning and optimization.