Details of Research Outputs

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.