Recruiting mobile vehicles (MVs) has been proved as an effective and low-cost strategy to collect data from sensing devices (SDs) in the smart city. However, few works consider data security when using MVs as data mules. In our previous work, we have proposed a Consistent Trust Verification for MVs (CTV-MV), which builts trust through recommendation relationships, but this method was vulnerable to collusion attack, that is, multiple MVs provided consistent fake data to deceive the system. Therefore, a Cross Trust Verification for MVs joint UAVs (CTV-MVU) data collection strategy is proposed in this paper, where the Unmanned Aerial Vehicles (UAVs) are deployed to collect data from specific SDs which are used as baseline data to realize trust reasoning mechanism. Besides, the UAVs can also sense the SDs that are difficult to be collected by MVs due to their limitations of coverage, and thus improve the data collection ratio. Furthermore, a Trust Priority Recruitment (TPR) strategy for CTV-MVU is also proposed to prioritize the recruitment of high-trust MVs. Experiment results show that the proposed CTV-MVU strategy outperforms the CTV-MV one in terms of the excellent ratio, trust, data collection ratio, and robustness.

招募移动车辆 (MV) 已被证明是从智能城市中的传感设备 (SD) 收集数据的有效且低成本的策略。然而，很少有作品在使用 MV 作为数据骡子时考虑数据安全性。在我们之前的工作中，我们提出了一种 MV 一致性信任验证（CTV-MV），它通过推荐关系建立信任，但这种方法容易受到共谋攻击，即多个 MV 提供一致的假数据来欺骗系统。因此，本文提出了一种MVs联合无人机（CTV-MVU）数据收集策略的交叉信任验证，其中部署无人驾驶飞行器（UAV）从特定的SDs收集数据作为基线数据以实现信任推理机制。除了，无人机还可以感知由于覆盖范围限制而难以被MV采集的SD，从而提高数据采集率。此外，还提出了 CTV-MVU 的信任优先招募 (TPR) 策略，以优先招募高信任 MV。实验结果表明，所提出的 CTV-MVU 策略在优良率、信任度、数据收集率和鲁棒性方面优于 CTV-MV 策略。