计算机科学
强化学习
拥挤感测
符号
变压器
人工智能
实时计算
数学
电气工程
工程类
计算机安全
算术
电压
作者
Hao Wang,Chi Harold Liu,Haijun Yang,Guoren Wang,Kin K. Leung
出处
期刊:IEEE ACM Transactions on Networking
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-16
标识
DOI:10.1109/tnet.2023.3289172
摘要
Unmanned aerial vehicle (UAV) crowdsensing (UCS) is an emerging data collection paradigm to provide reliable and high quality urban sensing services, with age-of-information (AoI) requirement to measure data freshness in real-time applications. In this paper, we explicitly consider the case to ensure that the attained AoI always stay within a specific threshold. The goal is to maximize the total amount of collected data from diverse Point-of-Interests (PoIs) while minimizing AoI and AoI threshold violation ratio under limited energy supplement. To this end, we propose a decentralized multi-agent deep reinforcement learning framework called “DRL-UCS( $\text{AoI}_{th}$ )” for multi-UAV trajectory planning, which consists of a novel transformer-enhanced distributed architecture and an adaptive intrinsic reward mechanism for spatial cooperation and exploration. Extensive results and trajectory visualization on two real-world datasets in Beijing and San Francisco show that, DRL-UCS( $\text{AoI}_{th}$ ) consistently outperforms all nine baselines when varying the number of UAVs, AoI threshold and generated data amount in a timeslot.
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