TY - GEN
T1 - Distributed Game-Theoretical Route Navigation for Vehicular Crowdsensing
AU - Wang, En
AU - Luan, Dongming
AU - Yang, Yongjian
AU - Wang, Zihe
AU - Dong, Pengmin
AU - Li, Dawei
AU - Liu, Wenbin
AU - Wu, Jie
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/8/9
Y1 - 2021/8/9
N2 - Vehicular CrowdSensing (VCS) has become a powerful sensing paradigm by selecting users driving vehicles to perform tasks. In most existing research, the platform centrally allocates tasks according to the collected user information. We argue that the information collection process results in user privacy leakage, and the centralized allocation leads to a heavy computation complexity. We propose to apply a distributed task allocation method in the widely-used route navigation system. The navigation system recommends several routes to a user and each route may cover some tasks. Then, the user distributively selects a route according to the route profit (task reward minus route cost). Since the task reward is shared by users, the route selections of users may influence each other. Hence, it remains unclear how to design a distributed route navigation approach to reach an equilibrium state (i.e., each user is satisfied with the selected route), while guaranteeing a good total profit. To this end, we formulate the problem as a multi-user potential game and propose a distributed route navigation algorithm. The trace-based simulation results verify that the proposed algorithm achieves a Nash equilibrium, while achieving a total user profit performance close to that of the optimal solution.
AB - Vehicular CrowdSensing (VCS) has become a powerful sensing paradigm by selecting users driving vehicles to perform tasks. In most existing research, the platform centrally allocates tasks according to the collected user information. We argue that the information collection process results in user privacy leakage, and the centralized allocation leads to a heavy computation complexity. We propose to apply a distributed task allocation method in the widely-used route navigation system. The navigation system recommends several routes to a user and each route may cover some tasks. Then, the user distributively selects a route according to the route profit (task reward minus route cost). Since the task reward is shared by users, the route selections of users may influence each other. Hence, it remains unclear how to design a distributed route navigation approach to reach an equilibrium state (i.e., each user is satisfied with the selected route), while guaranteeing a good total profit. To this end, we formulate the problem as a multi-user potential game and propose a distributed route navigation algorithm. The trace-based simulation results verify that the proposed algorithm achieves a Nash equilibrium, while achieving a total user profit performance close to that of the optimal solution.
KW - Nash equilibrium.
KW - Vehicular CrowdSensing
KW - potential game
KW - route navigation
UR - http://www.scopus.com/inward/record.url?scp=85117256071&partnerID=8YFLogxK
U2 - 10.1145/3472456.3472498
DO - 10.1145/3472456.3472498
M3 - Conference contribution
AN - SCOPUS:85117256071
T3 - ACM International Conference Proceeding Series
BT - 50th International Conference on Parallel Processing, ICPP 2021 - Main Conference Proceedings
PB - Association for Computing Machinery
T2 - 50th International Conference on Parallel Processing, ICPP 2021
Y2 - 9 August 2021 through 12 August 2021
ER -