학술논문
Ensuring Threshold AoI for UAV-Assisted Mobile Crowdsensing by Multi-Agent Deep Reinforcement Learning With Transformer
Document Type
Periodical
Author
Source
IEEE/ACM Transactions on Networking IEEE/ACM Trans. Networking Networking, IEEE/ACM Transactions on. 32(1):566-581 Feb, 2024
Subject
Language
ISSN
1063-6692
1558-2566
1558-2566
Abstract
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.