In the Internet of Things (IoT), the mobile smart devices with powerful sensing capability help mobile crowdsourcing become an important paradigm to sense environment information. The social Internet of Things paradigm can be exploited for complex task crowdsourcing by forming a collaborative team of socially connected nodes (i.e., smart devices). Few existing team crowdsourcing studies have ever satisfied requirements of trustworthy sensing data and collaborative communication among team members.

In this paper, we design TAQ-Crowd (Trust-Aware sensing Quality estimation for team Crowdsourcing), a social team crowdsourcing framework for Social Internet of Things systems. Within TAQ-Crowd, we first incorporate the consideration of trustworthy relationships between nodes into sensing data quality evaluation for TAQ model design. Then, we design a task assignment algorithm CS-Selection, in which the sensing quality guides the participant selection to maximize the overall task valuation under a budget constraint. Meanwhile, we consider a variant of the classic Traveling Salesman Problem (TSP) to extract a tree-structured routing network for team communication. Solving the team crowdsourcing problem concerns participating device selection and task cooperation, which involves two coupling NP-hard problems. The two coupling problems can be transformed into an essentially submodular cost submodular knapsack problem to be solved by the greedy task assignment strategy. Finally, extensive simulation experiments are conducted. The results show that TAQ-Crowd significantly outperforms state-of-art approaches in team formation with at least $(1-1∕e)∕2$ approximation ratio. Furthermore, the achieved superior performance can validate our proposed trust-aware sensing quality estimation.

在物联网（IoT）中，具有强大感应功能的移动智能设备帮助移动众包成为感应环境信息的重要范例。通过组建社交连接节点（即智能设备）的协作团队，可以将社交物联网范式用于复杂的任务众包。现有的团队众包研究很少能够满足可信赖的感知数据和团队成员之间的协作交流的要求。

在本文中，我们设计了TAQ-Crowd（团队众包的信任感知质量评估），这是一种用于社交物联网系统的社交团队众包框架。在TAQ-Corwd中，我们首先将对节点之间可信赖关系的考虑纳入用于TAQ模型设计的传感数据质量评估中。然后，我们设计一个任务分配算法CS-Selection，其中感测质量指导参与者的选择，以在预算约束下最大化整体任务评估。同时，我们考虑了经典的旅行推销员问题（TSP）的一种变体，以提取树型路由网络进行团队沟通。解决团队众包问题涉及参与设备的选择和任务协作，这涉及两个耦合的NP难题。这两个耦合问题可以转化为本质上为子模块成本的子模块背包问题，可以通过贪婪的任务分配策略来解决。最后，进行了广泛的仿真实验。结果表明，在团队形成方面，TAQ-Crowd明显优于最新方法 $（\mathrm{1个}-\mathrm{1个}∕Ë）∕2$近似比率。此外，所取得的卓越性能可以验证我们提出的信任感知传感质量估计。