Physical Layer Security Assisted Computation Offloading in Intelligently Connected Vehicle Networks

In this paper, we propose a secure computationoffloading scheme (SCOS) in intelligently connected vehicle (ICV) networks, aiming to minimize overall latency of computing via offloading part of computational tasks to nearby servers in small cell base stations (SBSs), while securing the information delivered during offloading and feedback phases via physical layer security. Existing computation offloading schemes usually neglected time-varying characteristics of channels and their corresponding secrecy rates, resulting in an inappropriate task partition ratio and a large secrecy outage probability. To address these issues, we utilize an ergodic secrecy rate to determine how many tasks are offloaded to the edge, where ergodic secrecy rate represents the average secrecy rate over all realizations in a time-varying wireless channel. Adaptive wiretap code rates are proposed with a secrecy outage constraint to match time-varying wireless channels. In addition, the proposed secure beamforming and artificial noise (AN) schemes can improve the ergodic secrecy rates of uplink and downlink channels even without eavesdropper channel state information (CSI). Numerical results demonstrate that the proposed schemes have a shorter system delay than the strategies neglecting time-varying characteristics.

I. Introduction

The advancement in sensors, cloud computing, artificial intelligence (AI), and 5G technologies has pushed the evolution of traditional vehicle-to-vehicle (V2V) networks toward intelligently connected vehicle (ICV) networks [1]. Compared to safety or value-added services in V2V networks supported by IEEE 802.11p [2], 5G-enabled ICVs equipped with advanced computing modules can realize autonomous driving, driver-supervised driving, cooperative driving, high definition and three-dimensional (3D) map services, on-board working and entertainment, augmented reality (AR), etc. [3]–[5]. In addition to local computing, enabled by mobile edge computing (MEC) technologies, ICVs can offload part of computational tasks to nearby servers in SBSs to cut down vehicle cost. For example, in Tesla store [6], consumers should pay an extra 8,000~10,000 US dollar for full self-driving hardware (Nvidia drive PX 2 platform), whose price could probably be reduced substantially if dedicated MEC services are available [1], [7]. Also, MEC can reduce computing and network latency when on-board computing capacity is insufficient [8]–[14].