为6G车联网设计轻量认证任务卸载方案，降低延迟提升效率
Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks
- 用基于身份的加密实现轻量认证，结合深度强化学习优化卸载决策
- 认证开销可使卸载效率下降50%，网络与任务规模增大时效率再降91.7%
- 提高数据传输速率可抵消认证开销，性能提升最高达63%，适合车联网研究者
6G车联网中的任务卸载管理对维持网络效率至关重要，尤其在车辆生成大量数据的背景下。通过认证实现安全通信会引入额外的计算和通信开销，显著影响卸载效率与延迟。本文提出一种统一框架，将轻量级基于身份的密码学（IBC）认证集成至基于云的6G车联孪生网络（VTNs）的任务卸载中。采用深度强化学习中的近端策略优化（PPO）算法，优化带认证的卸载决策，以最小化延迟并提升资源分配。在不同网络规模、任务大小和数据速率下的性能评估表明，IBC认证可导致卸载效率下降高达50%；网络规模和任务大小增加时，效率进一步下降最多达91.7%。作为应对措施，提高传输数据速率可在存在认证开销的情况下将卸载性能提升最高达63%。论文中详述的仿真与实验代码已公开于GitHub以供参考与复现。
原文摘要 · Abstract (English)
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].
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