论文标题
无线传感器网络中的车辆跟踪通过深度加固学习
Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning
论文作者
论文摘要
车辆跟踪已成为无线传感器网络（WSN）在救援，监视，交通监控等领域的关键应用之一。但是，提高的跟踪精度需要更多的能耗。在这封信中，为了提高跟踪准确性和节能的构想，这是一个分散的车辆跟踪策略，该策略基于调整固定感应区域和动态激活区域之间的交叉区域。然后，提出了两个深钢筋学习（DRL）辅助溶液，依靠激活区域半径的动态选择。最后，仿真结果显示了我们的DRL辅助设计的优越性。
Vehicle tracking has become one of the key applications of wireless sensor networks (WSNs) 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.
