Privacy-Preserving Integrity Assurance for Optimized Vehicular Network Performance in Edge Computing
Keywords:
IoV VEC, PPIA-SARLG, Cyberattack mitigation, Reinforcement LearningAbstract
The rapid evolution of the Internet of Vehicles (IoV) and Vehicular Edge Computing (VEC) has led to increased data traffic and communication challenges, necessitating efficient and secure models for seamless connectivity. Traditional models struggle with maintaining high communication efficiency and reliability in dynamic vehicular environments. This study addresses these challenges by introducing the Privacy-Preserving Integrity Assurance (PPIA) State-Action Reinforcement Learning Game (SARLG) (PPIA-SARLG) model. The primary objective of PPIA-SARLG is to enhance communication efficiency, reduce failure rates, and improve throughput in urban and highway scenarios. The proposed model was implemented using the SIMITS simulator, integrated with NS3, and evaluated under realistic conditions using the CICIoV2024 dataset for cyberattack simulation. Experimental results demonstrated that PPIA-SARLG achieved an average improvement of 19.71% in communication efficiency, reduced communication failures by 18.36%, and enhanced throughput by 15.79% compared to Two-Factor Privacy-Preserving Protocol Authentication (TF3PA). The novelty of PPIA-SARLG lies in its adaptive learning mechanism, which dynamically optimises packet transmission in high-mobility networks.
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