AI-Based Wireless Sensor IoT Networks for Energy-Efficient Consumer Electronics Using Stochastic Optimization

Wireless Sensor Networks (WSNs) integration with the Internet of Things (IoT) expands its potential by providing ideal communication and data sharing across devices, allowing more considerable monitoring and management in Consumer Electronics (CE). WSNs have an essential limitation in terms of energy resources since sensor nodes frequently run on limited power from batteries. This limitation necessitates the consideration of energy-efficient techniques to extend the network’s lifetime. In this article, an integrated approach has been presented to improve the energy efficiency of Wireless Sensor IoT Networks (WSINs) by leveraging modern machine learning algorithms with stochastic optimization. Recursive Feature Elimination (RFE) is utilized for the feature selection thus optimizing the input features for various machine learning models. These models are rigorously evaluated for their aptness to predict and mitigate energy consumption concerns inside WSINs. Subsequently, the stochastic optimization technique utilizes the uniform and normal distributions to model energy consumption situations. The results show that RFE-driven feature selection has significant effects on model performance and that Random Forest is effective at reaching higher accuracy. This research provides valuable perspectives for the design and implementation of WSINs in CE, supporting sustainable smart devices, by addressing energy consumption concerns using an optimized approach.