Paper Info
Title
AoI-minimal UAV Crowdsensing by Model-based Graph Convolutional Reinforcement Learning
Abstract
Mobile Crowdsensing (MCS) with smart devices has become an appealing paradigm for urban sensing. With the development of 5G-and-beyond technologies, unmanned aerial vehicles (UAVs) become possible for real-time applications, including wireless coverage, search and even disaster response. In this paper, we consider to use a group of UAVs as aerial base stations (BSs) to move around and collect data from multiple MCS users, forming a UAV crowdsensing campaign (UCS). Our goal is to maximize the collected data, geographical coverage whiling minimizing the age-of-information (AoI) of all mobile users simultaneously, with efficient use of constrained energy reserve. We propose a model-based deep reinforcement learning (DRL) framework called "GCRL-min(AoI)", which mainly consists of a novel model-based Monte Carlo tree search (MCTS) structure based on state-of-the-art approach MCTS (AlphaZero). We further improve it by adding a spatial UAV-user correlation extraction mechanism by a relational graph convolutional network (RGCN), and a next state prediction module to reduce the dependance of experience data. Extensive results and trajectory visualization on three real human mobility datasets in Purdue University, KAIST and NCSU show that GCRL-min(AoI) consistently outperforms five baselines, when varying different number of UAVs and maximum coupling loss in terms of four metrics.
Year
DOI
Venue
2022
10.1109/INFOCOM48880.2022.9796732
IEEE INFOCOM 2022 - IEEE Conference on Computer Communications
Keywords
DocType
ISSN
Mobile crowdsensing,Unmanned aerial vehicles,Age of Information,Graph convolutional reinforcement learning
Conference
0743-166X
ISBN
Citations 
PageRank 
978-1-6654-5823-8
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Z Dai100.34
Chi Harold Liu2109172.90
Ye Yuan311724.40
Ye Yuan411724.40
Rui Han511710.88
Guoyin Wang62144202.16
Jin Tang732262.02