A vehicular delay-tolerant network (VDTN) allows mobile vehicles (MVs) to collect data from widely deployed delay-tolerant sensors in a smart city through opportunistic routing, which has proven to be an efficient and low-cost data collection method. However, malicious MVs may report false data to obtain rewards, which will compromise applications. In this paper, the Active Trust Verification Data Collection (ATVDC) scheme is proposed for efficient, cheap, and secure data collection. In this scheme, an unmanned aerial vehicle (UAV) is adopted to collect baseline data from sensors to evaluate the trust of MVs, and a high-trust MV priority recruitment (HTMPR) strategy is proposed to recruit credible MVs at a low cost. In addition, a genetic-algorithm-based trajectory planning (GATP) algorithm is proposed to allow the UAV to collect more baseline data at the minimum flight cost. After sufficient experiments, the strategy proposed in this paper is seen to greatly improve performance in terms of the error-free ratio EF, the symbol error ratio ES, and the data coverage ratio ϑ.

车载延迟容忍网络（VDTN）允许移动车辆（MV）通过机会路由从智能城市中广泛部署的延迟容忍传感器收集数据，这已被证明是一种高效且低成本的数据收集方法。但是，恶意MV可能会举报虚假数据以获得报酬，这将损害应用程序。本文提出了主动信任验证数据收集（ATVDC）方案，以实现高效，廉价和安全的数据收集。在该方案中，采用无人飞行器（UAV）从传感器收集基线数据以评估MV的信任度，并提出了一种高信任度MV优先招募（HTMPR）策略，以低成本招募可信的MV。此外，提出了一种基于遗传算法的轨迹规划（GATP）算法，以使无人机能够以最低的飞行成本收集更多的基线数据。经过充分的实验，可以证明本文提出的策略在无误码率EF，符号误码率ES和数据覆盖率方面都大大提高了性能。ϑ。