Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning

Vehicle tracking has become one of the key applications of wireless sensor networks in the fields of rescue, surveillance, traffic monitoring, etc. However, the increased tracking accuracy requires more energy consumption. In this letter, a decentralized vehicle tracking strategy is conceived for improving both tracking accuracy and energy saving, which is based on adjusting the intersection area between the fixed sensing area and the dynamic activation area. Then, two deep reinforcement learning (DRL) aided solutions are proposed, relying on the dynamic selection of the activation area radius. Finally, simulation results show the superiority of our DRL-aided design.