Optimizing Energy Efficiency in Wireless Sensor Networks Using AI-Driven Algorithms

Abstract

Wireless Sensor Networks (WSNs) are increasingly being used in various applications such as environmental monitoring, healthcare, and smart cities, where energy efficiency plays a crucial role in ensuring long-term functionality. This research aims to optimize energy consumption in WSNs by leveraging Artificial Intelligence (AI)-driven algorithms. The study explores the integration of machine learning and optimization techniques to enhance energy efficiency while maintaining the network’s reliability and performance. The research utilizes a combination of AI algorithms, such as reinforcement learning and neural networks, to predict energy usage patterns and adjust the network's operation accordingly. The results demonstrate significant improvements in energy efficiency, with a reduction in energy consumption and an extension in the network’s lifetime. The findings highlight the potential of AI-driven solutions to address the challenges of energy consumption in WSNs, offering practical implications for designing more sustainable and efficient networks in various real-world applications.