摘要
由于无线传感器网络(WSN)中传感器的传输功率有限,同时可能与基站(BS)传输距离较远,造成无法及时交付数据,数据新鲜度过低,影响时延敏感型业务决策质量。因此,采用无人机(UAV)辅助收集传感器数据,成为提升无线传感器网络数据新鲜度的有效手段。该文通过信息年龄(AoI)性能指标评估无线传感器网络数据新鲜度,并基于集中式训练分布式执行框架的多智能体近端策略优化(MAPPO)方法研究了无人机轨迹优化算法。通过联合优化所有无人机的飞行轨迹,实现地面节点平均加权信息年龄的最小化。仿真结果验证了所提多无人机路径规划算法在降低无线传感器网络信息年龄方面的有效性。
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.
作者
胡昊南
韩铭
李文鹏
张杰
HU Haonan;HAN Ming;LI Wenpeng;ZHANG Jie(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Key Laboratory of Mobile Communication Technology,Chongqing,Chongqing 400065,China;The University of Sheffield,Sheffield S102TN,British)
出处
《电子与信息学报》
EI
CAS
CSCD
北大核心
2024年第4期1222-1230,共9页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61831002)
重庆市研究生科研创新项目(CYS21300)。
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
Unmanned Aerial Vehicles(UAV)-assisted communication
Age of Information(AoI)
Trajectory planning
Multi-agent reinforcement learning
