Secure Task Offloading for MEC-Aided-UAV System
Peipei Chen, Luo Xue-shan, Deke Guo, Yuchen Sun, Junjie Xie, Yawei Zhao, Rui Zhou
Abstract
The promise of unmanned aerial vehicles (UAVs) combined with the mobile edge computing (MEC), named MEC-aided-UAV extends the MEC application to offer new flexible, low-latency computing services and considerable utilities for pervasive sensing of the world. In MEC, the UAV with limited on-board energy and computation resources needs to offload its tasks to resource-rich ground base stations (GBSs) servers. Because of the broadcast nature of line-of-sight (LoS) channels, one of the key challenges in task offloading is to guarantee that confidential data is offloaded safely to the GBSs without being intercepted by eavesdroppers (Eves). In this MEC-aided-UAV system, the GBSs help the UAV compute the offloaded tasks and transmit the artificial noise (AN) to suppress the vicious Eves. We make the first attempt to study the maximum-minimum average secrecy capacity problem, including joint optimization of the trajectory and transmit power of the UAV, the transmit power of AN, the local computation ratio, and the selection of GBSs with consideration of the practical constraints of completion delay of the tasks, maximum velocity, and the power consumption. The optimization issue is confirmed as a mixed-integer non-convex problem. Thereafter, a low-complexity iterative algorithm with the block coordinate descent method and successive convex approximation technique is put forward to get its suboptimal solution. In addition, the convergent solution can be achieved by solving the subproblems in turn. Evaluation results validate that the proposed secure offloading scheme significantly effectiveness the baselines by 17.4%-71.2% on the maximum-minimum average secrecy capacity.