Hybrid Coopetitive Mechanism for Multiplatform Mobile Crowdsensing: A Two-Stage Approach to Pricing and Matching

In multi-platform mobile crowdsensing (MCS), platforms attract mobile workers to participate in sensing tasks and collect data through incentive mechanisms to provide data-driven services. However, existing studies often focus exclusively on either competition or cooperation mechanisms between platforms, overlooking scenarios where both coexist. Additionally, most studies prioritize optimizing social welfare or market fairness, neglecting the core role of platforms as data service providers in a free market and the impact of their dominant market position, which limits the utility of platform benefit optimization. In addition, the issue of privacy protection has not received sufficient attention. To this end, this paper proposes a two-stage hybrid mechanism (TS-HM) with the goal of maximizing the utility of the platform, including a decentralized multi-agent reinforcement learning pricing mechanism (DC-PM) and a two-substage cooperative matching mechanism (CMM). In the first stage, the DC-PM mechanism is used to help the platform learn the optimal pricing strategy under privacy-preserving conditions by modeling the pricing and worker contribution problem as a multi-leader-multi-follower stackelberg game; and in the second stage, the CMM mechanism is used to guarantee the matching stability and further enhance the platform utility. Experimental simulations demonstrate that the DC-PM mechanism effectively achieves rapid convergence of pricing strategies while ensuring privacy protection, and the CMM mechanism excels in matching stability and platform performance improvement. In general, the TS-HM mechanism outperforms existing approaches by increasing the total utility of the platform by an average of approximately 12. 65%, significantly increasing the effectiveness of the platform and showcasing its strong advantages.