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

2025-02-05

AI Summary

To address the high overhead and compromised real-time performance and resource efficiency caused by security authentication in task offloading within 6G vehicular cloud environments, this paper proposes an authentication-aware deep reinforcement learning offloading framework that integrates lightweight Identity-Based Cryptography (IBC) with Proximal Policy Optimization (PPO). It is the first work to natively embed IBC into the 6G vehicular digital twin network (VTN), establishing a joint optimization mechanism for authentication and offloading decision-making. The proposed approach overcomes the longstanding technical trade-off between security overhead and ultra-low latency. Extensive multi-scenario evaluations demonstrate a 63% improvement in offloading efficiency and a 91.7% reduction in efficiency degradation under heavy-load conditions, significantly outperforming conventional schemes.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Security and Privacy: Authentication, authorization, and access control
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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].

Problem

• Lightweight authentication in 6G networks
• Optimizing task offloading efficiency
• Reducing latency with DRL and IBC

Innovation

• Lightweight IBC authentication
• PPO in DRL optimization
• Enhanced offloading performance