Design of energy-aware sensor networks for climate and pollution monitoring

Abstract

The growing concern over climate change and Pollution has driven the development of energy-efficient sensor networks for environmental monitoring. This research proposes an energy-aware sensor network using Spanning Tree-Reinforcement Learning (ST-RL) to optimize data accuracy, minimize energy consumption, and extend the network's lifetime. The proposed method achieves significant performance improvements compared to existing approaches. Experimental results demonstrate that ST-RL enhances network lifetime by 28.57 %, reduces energy consumption by 41.24 %, improves packet delivery ratio by 3.7 %, and reduces transmission delay by 10 % over traditional methods such as EDAL, FT-EEC, and EAEDAR. The data is collected from multiple environmental sensors, processed using spanning tree algorithms for optimized connectivity and refined with reinforcement learning to suppress unnecessary transmissions. The results confirm that the proposed ST-RL technique significantly enhances energy efficiency and network reliability, making it a promising solution for large-scale climate and pollution monitoring applications.