Group-Signature Authentication to Secure Task Offloading in Vehicular Edge Twin Networks

Al-Shareeda, Sarah; Ozguner, Fusun; Canberk, Berk

Abstract

This study delves into the integration of Group Signature (GS)-based authentication within Vehicular Edge Twin Networks (VETNs), a critical component in ensuring secure and efficient vehicular communication. By leveraging Proximal Policy Optimization with Deep Reinforcement Learning (PPO-DRL), we explore the impact of GS authentication on system performance, particularly in terms of latency and scalability during task offloading scenarios. Our findings reveal that while GS authentication enhances security, it also introduces overheads that can be mitigated by strategically optimizing edge data rates. Remarkably, we observe up to a 43% reduction in latency in less dense networks and a 35% reduction in latency in medium/dense networks, underscoring the potential of carefully calibrated GS-secure offloading strategies to maintain high performance even as network conditions fluctuate.

Citation

Al-Shareeda, S., Ozguner, F., & Canberk, B. (2024, December). Group-Signature Authentication to Secure Task Offloading in Vehicular Edge Twin Networks. Paper presented at 2024 IEEE Global Communications Conference (GLOBECOM), Cape Town, South Africa

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: 2024 IEEE Global Communications Conference (GLOBECOM)
• Start Date: Dec 8, 2024
• End Date: Dec 12, 2024
• Acceptance Date: Oct 31, 2024
• Deposit Date: Oct 11, 2024
• Peer Reviewed: Peer Reviewed
• Keywords: Index Terms-Vehicular Edge Twin Networks; Authentication; Group Signatures; Task Offloading; Deep Reinforcement Learn- ing; Proximal Policy Optimization
• External URL: https://globecom2024.ieee-globecom.org/