TY - GEN
T1 - Intelligent Scheduling of UAVs and Sensors for Information Age Minimization at Wireless Powered Internet of Things
AU - Li, Jiameng
AU - Wang, Xiaojie
AU - Wu, Jun
AU - Ning, Zhaolong
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Age of Information (AoI) has received much attention from researchers as the latest metric to quantify the freshness of data. It is necessary to jointly schedule Unmanned Aerial Vehicles (UAVs) and sensors to reduce the system AoI in wireless powered Internet of things. However, constraints on UAV flight time, charging time, and data collection time, as well as constraints of half-duplex hardware for sensors make it difficult to efficiently jointly schedule UAVs and sensors by traditional methodes. Thus, we design a multi-agent Deep Reinforcement Learning (DRL)-based UAV cooperative scheduling algorithm that jointly optimizes sensor charging time, UAV trajectories and sensor update scheduling with AoI as the optimization objective. Initially, we define the AoI minimization problem, portraying it as a Markov decision process. Then, we design a multi-agent DRL algorithm founded on factorizing value functions to address this issue. Finally, experiments demonstrate that the MAPLE algorithm can effectively coordinate the scheduling of UAVs and sensors.
AB - Age of Information (AoI) has received much attention from researchers as the latest metric to quantify the freshness of data. It is necessary to jointly schedule Unmanned Aerial Vehicles (UAVs) and sensors to reduce the system AoI in wireless powered Internet of things. However, constraints on UAV flight time, charging time, and data collection time, as well as constraints of half-duplex hardware for sensors make it difficult to efficiently jointly schedule UAVs and sensors by traditional methodes. Thus, we design a multi-agent Deep Reinforcement Learning (DRL)-based UAV cooperative scheduling algorithm that jointly optimizes sensor charging time, UAV trajectories and sensor update scheduling with AoI as the optimization objective. Initially, we define the AoI minimization problem, portraying it as a Markov decision process. Then, we design a multi-agent DRL algorithm founded on factorizing value functions to address this issue. Finally, experiments demonstrate that the MAPLE algorithm can effectively coordinate the scheduling of UAVs and sensors.
KW - Age of information
KW - Internet of things
KW - multi-agent deep reinforcement learning
KW - unmanned aerial vehicle
KW - wireless power transfer
UR - http://www.scopus.com/inward/record.url?scp=85199103434&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85199103434&partnerID=8YFLogxK
U2 - 10.1109/CSCWD61410.2024.10580720
DO - 10.1109/CSCWD61410.2024.10580720
M3 - Conference contribution
AN - SCOPUS:85199103434
T3 - Proceedings of the 2024 27th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2024
SP - 3243
EP - 3248
BT - Proceedings of the 2024 27th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2024
A2 - Shen, Weiming
A2 - Shen, Weiming
A2 - Barthes, Jean-Paul
A2 - Luo, Junzhou
A2 - Qiu, Tie
A2 - Zhou, Xiaobo
A2 - Zhang, Jinghui
A2 - Zhu, Haibin
A2 - Peng, Kunkun
A2 - Xu, Tianyi
A2 - Chen, Ning
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2024
Y2 - 8 May 2024 through 10 May 2024
ER -