Distributed Game-Theoretical Route Navigation for Vehicular Crowdsensing

Abstract

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.