With the development of Internet of Thing (IoT) joint 5G and Beyond Networks, Mobile Edge Users (MEUs) can act as mobile data collectors to collect data for various applications. However, some malicious MEUs reporting false or malicious data can cause serious harm to applications, especially for ultra-reliable applications. A novel Verifiable Trust Evaluation joint UAV (VTE-UAV) mechanism is proposed to select trustworthy MEUs to conduct the task for ultra-reliable applications. The VTE-UAV strategy adopts two novel trust evaluation methods, one is the aggregation-based MEU trust evaluation mechanism, when malicious evaluation objects are in the minority, the mechanism takes most evaluation results as baseline data. The other is an active trust acquisition mechanism, it takes the data obtained by Unmanned Aerial Vehicles (UAVs) as baseline data to actively validate the authenticity of the data. Through these two cross evaluation strategies, we obtain more accurate trust evaluation results. Finally, this paper transforms the trust evaluation optimization problem into the optimization of the accuracy of trust evaluation with reducing the cloud payment and the dispatch cost of UAVs. Extensive experiments have verified the validity of the VTE-UAV strategy. Compared with the previous strategies, the VTE-UAV improves the cloud recruitment performance by 7.74%-25.91%, increases the accuracy of trust evaluation of IoT devices by 2.24%-11.72%, and reduces the cloud payment and the cost of UAVs by 3.11%-10.20% and 58.23%, respectively.

随着物联网（IoT）联合5G和超越网络的发展，移动边缘用户（MEU）可以充当移动数据收集器，以收集各种应用程序的数据。但是，某些恶意MEU报告虚假或恶意数据会严重损害应用程序，尤其是对于超可靠的应用程序。提出了一种新颖的可验证信任评估联合无人机（VTE-UAV）机制，以选择可信任的MEU来执行超可靠应用程序的任务。VTE-UAV策略采用两种新颖的信任评估方法，一种是基于聚集的MEU信任评估机制，当恶意评估对象占少数时，该机制将大多数评估结果作为基准数据。另一个是主动的信任获取机制，它以无人飞行器（UAV）获得的数据为基线数据，以主动验证数据的真实性。通过这两种交叉评估策略，我们可以获得更准确的信任评估结果。最后，将信任评估优化问题转化为信任评估精度的优化，同时降低了无人机的云支付和调度成本。大量实验证明了VTE-UAV策略的有效性。与以前的策略相比，VTE-UAV将云招聘绩效提高了7.74％-25.91％，将IoT设备的信任评估准确性提高了2.24％-11.72％，并将云支付和无人机成本降低了3.11 ％-10.20％和58.23％。通过这两种交叉评估策略，我们可以获得更准确的信任评估结果。最后，将信任评估优化问题转化为信任评估精度的优化，同时降低了无人机的云支付和调度成本。大量实验证明了VTE-UAV策略的有效性。与以前的策略相比，VTE-UAV将云招聘绩效提高了7.74％-25.91％，将IoT设备的信任评估准确性提高了2.24％-11.72％，并将云支付和无人机成本降低了3.11％ ％-10.20％和58.23％。通过这两种交叉评估策略，我们可以获得更准确的信任评估结果。最后，将信任评估优化问题转化为信任评估精度的优化，同时降低了无人机的云支付和调度成本。大量实验证明了VTE-UAV策略的有效性。与以前的策略相比，VTE-UAV将云招聘绩效提高了7.74％-25.91％，将IoT设备的信任评估准确性提高了2.24％-11.72％，并将云支付和无人机成本降低了3.11％ ％-10.20％和58.23％。将信任评估优化问题转化为信任评估精度的优化，同时降低了无人机的云支付和调度成本。大量实验证明了VTE-UAV策略的有效性。与以前的策略相比，VTE-UAV将云招聘绩效提高了7.74％-25.91％，将IoT设备的信任评估准确性提高了2.24％-11.72％，并将云支付和无人机成本降低了3.11 ％-10.20％和58.23％。将信任评估优化问题转化为信任评估精度的优化，同时降低了云支付和无人机的调度成本。大量实验证明了VTE-UAV策略的有效性。与以前的策略相比，VTE-UAV将云招聘绩效提高了7.74％-25.91％，将IoT设备的信任评估准确性提高了2.24％-11.72％，并将云支付和无人机成本降低了3.11 ％-10.20％和58.23％。