Hardware implementation of A Reinforcement learning based energy efficient protocol for wireless sensor network

Wireless Sensor Networks (WSNs) play a pivotal role in various applications, including environmental monitoring, industrial automation, and healthcare. However, the limited energy resources of sensor nodes pose a significant challenge in extending the network's lifetime while maintaining reliable data communication. In this paper, we propose a novel approach to address this challenge through a reinforcement learning-based energy efficient protocol. We have done its hardware implementation hence the results obtained are all experimental results. Traditional energy-efficient techniques for WSNs typically rely on fixed policies and heuristics, which may not adapt optimally to dynamic environmental conditions. In contrast, our approach leverages the capabilities of reinforcement learning to develop adaptive and context-aware energy management strategies. It shows 18% improvement in Packet Delivery Ratio and 29% improvement in Network lifetime in comparison to the recent similar protocol.