MP-Coopetition: Competitive and Cooperative Mechanism for Multiple Platforms in Mobile Crowd Sensing

Abstract

Mobile Crowd Sensing (MCS) enables the platform to offer data-based service by incentivizing mobile users to perform sensing task and collecting sensing data from them. Most of the existing works on MCS only consider designing incentive mechanisms for a single MCS platform. In this paper, we study the incentive mechanism in MCS with multiple platforms under two scenarios: competitive platform and cooperative platform. We correspondingly propose new competitive and cooperative mechanisms for each scenario. In the competitive platform scenario, platforms decide their prices on rewards to attract more participants, while the users choose which platform to work for. We model such a competitive platform scenario as a two-stage Stackelberg game. In the cooperative platform scenario, platforms cooperate to share sensing data with each other. We model it as many-to-many bargaining. Moreover, we first prove the NP-hardness of exact bargaining and then propose heuristic bargaining. Finally, numerical results show that (1) platforms in the competitive platform scenario can guarantee their payoff by optimally pricing on rewards and participants can select the best platform to contribute; (2) platforms in the cooperative platform scenario can further improve their payoff by bargaining with other platforms for cooperatively sharing collected sensing data.