Online Edge Learning Offloading and Resource Management for UAV-Assisted MEC Secure Communications

The mobile and flexible unmanned aerial vehicle (UAV) with mobile edge computing (MEC) can effectively relieve the computing pressure of the massive data traffic in 5G Internet of Things. In this paper, we propose a novel online edge learning offloading (OELO) scheme for UAV-assisted MEC secure communications, which can improve the secure computation performance. Moreover, the problem of information security is further considered since the offloading information of terminal users (TUs) may be eavesdropped due to the light-of-sight characteristic of UAV transmission. In the OELO scheme, we maximize the secure computation efficiency by optimizing TUs' binary offloading decision and resource management while guaranteeing dynamic task data queue stability and minimum secure computing requirement. Since the optimization problem is fractionally structured, binary constrained and multi-variable coupled, we first utilize the Dinkelbach method to transform the fractionally structured problem into a tractable form. Then, OELO generates the offloading decision based on deep reinforcement learning (DRL) and optimizes the resource management in an iterative manner through successive convex approximation (SCA). Simulation results show that the proposed scheme achieves better computing performance and enhances the stability and security compared with benchmarks.

I. Introduction

The developing evolution of 5G Internet of Things (5GIoT) has brought great convenience to human life and production, which also causes tremendously data traffic [1], [2], [3]. To quickly process the massive data of 5GIoT, mobile edge computing (MEC) is an efficient method to offload the data traffic to the edge for computation, which can effectively alleviate the pressure of data growth and improve the data computing efficiency [4], [5], [6], [7], [8], [9], [10]. Mao et al. investigated that MEC can assist terminal users (TUs) with limited resource to enable latency-critical and computation-intensive applications, and promote the achievement of 5GIoT [7]. Li et al. studied the online trusted collaboration offloading method for MEC systems by considering the cooperative trust risk and the variation in completion delays [8]. A model-free configuration scheme to achieve the MEC system's quality of service (QoS) was proposed by in Zhao et al. [9]. Guo et al. investigated that MEC can overcome the limitation of the multi-user system and increase the computation capability [10].