A Secure Data Collection Strategy Using Mobile Vehicles Joint UAVs in Smart City

Evidence shows that using Mobility Vehicles (MVs) to gather data from smart city Sensing Devices (SDs) is efficient and cost-effective. Data security is usually not considered when attempting to use MVs as a data mule. A Consistent confidence Verification for MVs (CTV-MV) based on relationship-based proposals was previously introduced. A collusion attack, however, may exploit this method by having multiple MVs work together to feed the system consistently false data. Consequently, Unmanned Aerial Vehicles (UAVs) collect data from specific SDs, which serve as baseline data for the trust reasoning process. This technique is called Cross Trust Verification for MV joint UAVs (CTV-MVU). UAVs might improve data-gathering ratios by spotting SDs that MVs would miss due to coverage gaps. We introduce CirCo, a state-of-the-art algorithm that optimizes the speed and trajectory of UAVs to optimize their energy consumption. To simplify optimization, CirCo maps a three-dimensional problem onto a two-dimensional plane. This plane contains the GN positions, transmission ranges, and the minimum transmission time requirements for the temporal dimensions. This could allow for the immediate identification of the possible UAV crossing window.