Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be influenced by trust relationships among workers. To obtain the asymmetric trust values among all workers in the social network, the Trust Reinforcement Evaluation Framework (TREF) based on Graph Convolutional Neural Networks (GCNs) is proposed in this paper. The task completion effect is comprehensively calculated by considering the workers' ability benefits, distance benefits, and trust benefits in this paper. The worker recruitment problem is modeled as an Undirected Complete Recruitment Graph (UCRG), for which a specific Tabu Search Recruitment (TSR) algorithm solution is proposed. An optimal execution team is recruited for each task by the TSR algorithm, and the collaboration team for the task is obtained under the constraint of privacy loss. To enhance the efficiency of the recruitment algorithm on a large scale and scope, the Mini-Batch K-Means clustering algorithm and edge computing technology are introduced, enabling distributed worker recruitment. Lastly, extensive experiments conducted on five real datasets validate that the recruitment algorithm proposed in this paper outperforms other baselines. Additionally, TREF proposed herein surpasses the performance of state-of-the-art trust evaluation methods in the literature.

翻译：协作移动群智感知（CMCS）允许平台招募工人团队协同执行复杂感知任务。此类协作效率可能受工人间信任关系影响。为获取社交网络中所有工人间的非对称信任值，本文提出基于图卷积神经网络（GCNs）的信任强化评估框架（TREF）。通过综合计算工人的能力收益、距离收益与信任收益，全面评估任务完成效果。将工人招募问题建模为无向完全招募图（UCRG），并针对该模型提出特定禁忌搜索招募（TSR）算法。TSR算法为每个任务招募最优执行团队，并在隐私损失约束下获取任务协作团队。为提升大规模多场景下招募算法的效率，引入小批量K均值聚类算法与边缘计算技术，实现分布式工人招募。最后，基于五个真实数据集的大量实验表明，本文提出的招募算法优于其他基线方法。此外，本文提出的TREF在性能上超越了文献中最先进的信任评估方法。