UAV-Aided Energy-Efficient Edge Computing Networks: Security Offloading Optimization

Abstract

Unmanned aerial vehicles (UAVs) are widely applied for service provisioning in many domains, such as topographic mapping and traffic monitoring. These applications are complicated with huge computational resources and extremely low-latency requirements. However, the moderate computational capability and limited energy restrict the local data processing for the UAV. Fortunately, this impediment may be mitigated by utilizing wireless power transfer (WPT) and employing the multiaccess edge computing (MEC) paradigm for offloading demanding computational tasks from the UAV via wireless communications. Particularly, the offloaded information may become compromising by the eavesdropper (Eve) when UAVs offload the computational tasks to MEC servers. To address this issue, a UAV-MEC (UMEC) system with energy harvesting (EH) is studied, where the full-duplex protocol is considered to realize simultaneously receiving confidential data from the UAV and broadcasting the control instructions. It is worth noting that in our proposed scheme, these control instructions also serve as the artificial interference to confuse the Eve. To improve the energy efficiency for offloading, the computational communication resource allocation is optimized to minimize the energy consumption for UAV with the consumed and harvested energy. Specially, the worst case secrecy offloading rate and computation-latency constraint are considered, to further enhance the reliability and security of the proposed system. Since the objective optimization problem is nonconvex, we convert it into a convex one by analytical means. The semiclosed form expressions of the offloading time, offloading data size, and transmit power are, respectively, derived. Moreover, the conditions of nonoffloading, partial, and full offloading are also discussed from a physical perspective. With the specific conditions of activating the above-mentioned three offloading options, numerical results verify the performance of our proposed offloading strategy in various scenarios and show the superiority of our offloading strategy with the existing works in terms of the offloading capacity and energy efficiency.