TY - GEN
T1 - Task Selection and Route Planning for Mobile Crowd Sensing Using Multi-Population Mean-Field Games
AU - Kang, Yuhan
AU - Liu, Siting
AU - Zhang, Hongliang
AU - Han, Zhu
AU - Osher, Stanley
AU - Poor, H. Vincent
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - With the increasing deployment of mobile vehicles, such as mobile robots and unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in mobile crowd sensing (MCS). Specifically, mobile vehicles equipped with sensors and computing devices are able to collect massive data due to their fast and flexible mobility in MCS systems. In this paper, we consider a mobile vehicle-based MCS system where vehicles owned by different operators or individuals compete against others for limited sensing resources. We investigate the joint task selection and route planning problem for such an MCS system. However, since the structural complexity and computational complexity of the original problem is very high, we propose a multi-population Mean-Field Game (MFG) problem by simplifying the interaction between vehicles as a distribution over their strategy space, known as the mean-field term. To solve the multi-population MFG problem efficiently, we propose a G-prox primal-dual hybrid gradient method (PDHG) algorithm whose computational complexity is independent of the number of vehicles. Numerical results show that the proposed multi-population MFG scheme and algorithm are of effectiveness and efficiency.
AB - With the increasing deployment of mobile vehicles, such as mobile robots and unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in mobile crowd sensing (MCS). Specifically, mobile vehicles equipped with sensors and computing devices are able to collect massive data due to their fast and flexible mobility in MCS systems. In this paper, we consider a mobile vehicle-based MCS system where vehicles owned by different operators or individuals compete against others for limited sensing resources. We investigate the joint task selection and route planning problem for such an MCS system. However, since the structural complexity and computational complexity of the original problem is very high, we propose a multi-population Mean-Field Game (MFG) problem by simplifying the interaction between vehicles as a distribution over their strategy space, known as the mean-field term. To solve the multi-population MFG problem efficiently, we propose a G-prox primal-dual hybrid gradient method (PDHG) algorithm whose computational complexity is independent of the number of vehicles. Numerical results show that the proposed multi-population MFG scheme and algorithm are of effectiveness and efficiency.
KW - Multi-population mean-field game
KW - mobile crowd sensing
KW - mobile vehicles
KW - route planning
UR - http://www.scopus.com/inward/record.url?scp=85115672541&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115672541&partnerID=8YFLogxK
U2 - 10.1109/ICC42927.2021.9500261
DO - 10.1109/ICC42927.2021.9500261
M3 - Conference contribution
AN - SCOPUS:85115672541
T3 - IEEE International Conference on Communications
BT - ICC 2021 - IEEE International Conference on Communications, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Communications, ICC 2021
Y2 - 14 June 2021 through 23 June 2021
ER -