Mobile crowd sensing utilizes smartphones' sensors for efficient data collection, yet existing incentive mechanisms overlook cooperative compatibility among users for multiple tasks. This paper presents truthful incentive mechanisms to minimize social costs by forming compatible user groups for each task. It introduces the Social Optimization Compatible User Selection (SOCUS) problem, considering bid models and compatibility relationships derived from social networks. Two reverse auction-based mechanisms, MCT-M and MCT-S, are designed, demonstrating computational efficiency, individual rationality, and truthfulness through theoretical analysis and simulations, with MCT-M offering optimal solutions.
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Xu, Jia, et al. "Mobile crowd sensing via online communities: Incentive mechanisms for multiple cooperative tasks." 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 2017.
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