Due to the limited transmitting power of sensors in the Wireless Sensor Network (WSN) and high probability of large distance between sensors and their associated Base Station(BS), the sensor data may not be received in time. This will reduce the data freshness of sensor data and affect the quality of decision for delay sensitive service. Therefore, the use of Unmanned Aerial Vehicles (UAVs) to assist in collecting sensor data has become an effective solution to decrease the data freshness, measured by Age of Information (AoI), in wireless sensor networks. A UAV trajectory optimization algorithm based on the Multi-Agent Proximal Policy Optimization (MAPPO) method is developed in this paper, which employs a centralized-training and distributed-execution framework. By jointly optimizing the flight trajectories of all UAVs, the average AoI of all ground nodes is minimized. The simulation results verify the effectiveness of our proposed UAV trajectory optimization algorithm on minimizing the AoI in the WSN.
关键词
无人机辅助通信/信息年龄/轨迹规划/多智能体强化学习
Key words
Unmanned Aerial Vehicles(UAV)-assisted communication/Age of Information (AoI)/Trajectory planning/Multi-agent reinforcement learning
维普
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