Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks

Jinyu Yuan, Jingyi Peng, Qing Yan, Gang He, Honglin Xiang, Zili Liu

The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum and the power limitations seriously constrains the improvement of WSN. Therefore, in this paper, we proposed a deep reinforcement learning-based energy consumption optimization for P2P communication in WSN. Specifically, P2P sensors (PUs) are considered agents to share the spectrum of authorized sensors (AUs). An authorized sensor has permission to access specific data or systems, while a P2P sensor directly communicates with other sensors without needing a central server. One involves permission, the other is direct communication between sensors. Each agent can control the power and select the resources to avoid interference. Moreover, we use a double deep Q network (DDQN) algorithm to help the agent learn more detailed features of the interference. Simulation results show that the proposed algorithm can obtain a higher performance than the deep Q network scheme and the traditional algorithm, which can effectively lower the energy consumption for P2P communication in WSN.