The content discusses the importance of worker recruitment in collaborative mobile crowd sensing, focusing on trust relationships and abilities. It introduces the TSR algorithm to optimize worker selection, demonstrating superior performance compared to other strategies through extensive simulations.
Collaborative Mobile Crowd Sensing (CMCS) aims to improve data quality by enhancing teamwork among workers. Existing strategies overlook trust relationships between workers, impacting task utility evaluation. The paper introduces the TSR algorithm to recruit optimal worker sets based on abilities and trust values.
Privacy concerns in CMCS are addressed through high trust values between workers. The proposed strategy outperforms other methods in terms of effectiveness and efficiency, as demonstrated in simulation experiments on real-world datasets.
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