Xu, Jia, et al. "Incentive mechanism for multiple cooperative tasks with compatible users in mobile crowd sensing via online communities." IEEE Transactions on Mobile Computing 19.7 (2019): 1618-1633.

This paper introduces innovative incentive mechanisms for mobile crowd sensing, a paradigm leveraging smartphones' ubiquitous sensors to gather data. Addressing a gap in existing models, it emphasizes the importance of cooperative compatibility among users for executing multiple tasks efficiently. The study presents the Social Optimization Compatible User Selection (SOCUS) problem within two bid models, incorporating real-life social network data to define user compatibility. Two mechanisms, MCT - M and MCT - S, are developed, focusing on grouping compatible users and utilizing reverse auctions for task allocation. A combination of neural networks and clustering algorithms aids in optimizing user grouping, enhancing the mechanisms' performance. The proposed solutions are validated through theoretical analysis and simulations, demonstrating efficiency, fairness, truthfulness, and the ability to minimize social costs and overpayments while optimizing task completion.