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Collaborative Mobile Crowd Sensing (CMCS) enhances data quality and coverageby promoting teamwork in task sensing, with worker recruitment representing acomplex multi-objective optimization problem. Existing strategies mainly focuson the characteristics of workers themselves, neglecting the asymmetric trustrelationships between them, which affects the rationality of task utilityevaluation. To address this, this paper first employs the Mini-Batch K-Meansclustering algorithm and deploys edge servers to enable efficient distributedworker recruitment. Historical data and task requirements are utilized toobtain workers' ability types and distances. A trust-directed graph in theworker's social network is input into the Graph Convolutional Network (GCN)framework for training, capturing asymmetric trustworthiness between workerpairs. Privacy leakage is prevented in CMCS scenarios through high trust valuesbetween workers. Ultimately, an undirected recruitment graph is constructedusing workers' abilities, trust values, and distance weights, transforming theworker recruitment problem into a Maximum Weight Average Subgraph Problem(MWASP). A Tabu Search Recruitment (TSR) algorithm is proposed to rationallyrecruit a balanced multi-objective optimal task utility worker set for eachtask. Extensive simulation experiments on four real-world datasets demonstratethe effectiveness of the proposed strategy, outperforming other strategies.

Efficient Recruitment Strategy for Collaborative Mobile Crowd Sensing Based on GCN Trustworthiness Prediction