Improving Both Quantity and Quality: Incentive Mechanism for Social Mobile Crowdsensing Architecture

Mobile crowdsensing has emerged as an efficient paradigm for performing large-scale sensing tasks. Improving both the quantity and quality of users is still the pivotal problem for mobile crowdsensing system. This paper gives a comprehensive solution to improve the quantity and quality of users simultaneously through the social mobile crowdsensing architecture. To incentive the users based on the novel architecture, we first propose a universal initial diffuser selection algorithm to accommodate two widely studied diffusion models and, then a lightweight, multi-metric comprehensive, and parameter-free user quality evaluation method is presented. Finally, we propose a reverse auction to optimize the new criterion, which takes both social cost and user quality into consideration. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed incentive mechanisms achieve computational efficiency, individual rationality, truthfulness, and guaranteed approximation. Meanwhile, the proposed incentive mechanisms show prominent advantage in total unit quality cost and running time.