Physical Layer Security Assisted Computation Offloading in Intelligently Connected Vehicle Networks

Abstract

In this paper, we propose a secure computation offloading 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.

Satisfaction-Maximized Secure Computation Offloading in Multi-Eavesdropper MEC Networks

“…We can linearize the processing delay term in R den i using z i . Based on z i and Theorem 1, problem (P2) can be reformulated as (23), shown at the bottom of this page.…”

“…In other words, the secrecy level provided by PLS will not be compromised by the mobile devices' 0000-0000/00$00.00 © 2021 IEEE limited computation resources [19]. Recently, many works on MEC-based computation offloading consider eavesdropping attacks and exploit the nature of wireless channels in PLS to enhance security [4,5,[20][21][22][23][24][25][26]. Most of these works have focused on the eavesdropping scenario with only a single eavesdropper in MEC networks [4,5,[20][21][22][23][24].…”

“…Recently, many works on MEC-based computation offloading consider eavesdropping attacks and exploit the nature of wireless channels in PLS to enhance security [4,5,[20][21][22][23][24][25][26]. Most of these works have focused on the eavesdropping scenario with only a single eavesdropper in MEC networks [4,5,[20][21][22][23][24]. The authors in these works aimed at minimizing processing delay or energy consumption in a single-eavesdropper system, and ensure the security of offloaded tasks using PLS technologies.…”

“…The authors in [20] and [21] defined secrecy energy efficiency (SEE) as the total number of secure computation offloading bits per Joule, which aims to maximize secrecy capacity and minimize devices' energy consumption. Moreover, to achieve excellent delay performance in IoT applications, the authors in [4] and [23] formulated the overall delay minimizing problem with security provisioning. We note that the degrees and urgencies of delay and energy consumption requirements may vary greatly in different scenarios and situations.…”

5G Security Challenges and Solutions: A Review by OSI Layers

“…Artificial noise is also used in intelligent transportation systems. For instance, it can be exploited to guarantee a secure communication while offloading part of the data from increased network quality of service [141]. Furthermore, multiple vehicles can be exploited to cooperatively generate noise schemes to secure the communication [142].…”