Distributed Task Selection for Crowdsensing: A Game-Theoretical Approach

Mobile CrowdSensing (MCS) is a promising sensing paradigm that leverages users’ mobile devices to collect and share data for various applications. A key challenge in MCS is task allocation, which aims to assign sensing tasks to suitable users efficiently and effectively. Existing task allocation approaches are mostly centralized, requiring users to disclose their private information and facing high computational complexity. Moreover, centralized approaches may not satisfy users’ preferences or incentives. To address these issues, we propose a novel distributed task allocation scheme based on route navigation systems. We consider two scenarios: time-tolerant tasks and time-sensitive tasks, and formulate them as potential games. We design distributed algorithms to achieve Nash equilibria while considering users’ individual preferences and the platform’s task allocation objectives. We also analyze the convergence and performance of our algorithm theoretically. In the time-sensitive task scenario, the problem becomes even more intricate due to temporal conflicts among tasks. We prove the task selection problem is NP-hard and propose a distributed task selection algorithm. In contrast to existing distributed approaches that require users to deviate from their regular routes, our method ensures task completion while minimizing disruption to users. Trace-based simulation results validate that the proposed algorithm attains a Nash equilibrium and offers a total user profit performance closely aligned with that of the optimal solution.