Acquiring Secure Data in Smart Cities with Mobile Vehicles and Coordinated UAVs

Abstract

There is proof that it is both economical and efficient to use Mobility Vehicles (MVs) to collect data from smart city Sensing Devices (SDs). When trying to employ MVs as a data mule, data security is typically overlooked. Prior work provided a Consistent confidence Verification for MVs (CTV-MV) based on relationship-based propositions. However, a collusion attack may take advantage of this technique by having several MVs cooperate to continuously provide the system with erroneous data. In order to provide baseline data for the trust reasoning process, Unmanned Aerial Vehicles (UAVs) gather data from particular SDs. Cross Trust Verification for MV joint UAVs (CTV-MVU) is the name of this method. By identifying SDs that MVs would overlook because of coverage gaps, UAVs could increase data-gathering ratios. In order to minimize the energy consumption of UAVs, we provide CirCo, a cutting-edge algorithm that optimizes their speed and trajectory. A three-dimensional problem is mapped onto a two-dimensional plane by CirCo to make optimization easier. For the temporal dimensions, this plane includes the GN placements, transmission ranges, and the minimum transmission time requirements. This might make it possible to quickly identify the potential window for UAV crossing.