Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

TL;DR

This paper proposes a lightweight authentication framework for task offloading in 6G vehicular networks, optimizing offloading decisions with deep reinforcement learning to balance security and efficiency.

Contribution

It introduces a unified IBC authentication scheme integrated with DRL-based offloading optimization in 6G-VTN, addressing security overhead challenges.

Findings

IBC authentication reduces offloading efficiency by up to 50%.

Increasing network and task size further decreases efficiency, up to 91.7%.

Higher data rates can improve performance by up to 63% despite overhead.

Abstract

Task offloading management in 6G vehicular networks is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50% due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1].