Countering AI-Powered Threats in IoV: A Secure Task Offloading and Resource Allocation Scheme with RIS and Quantum Reinforcement Learning

The advanced Internet of Vehicles (IoV) integrates autonomous driving, intelligent navigation, and in-vehicle consumer electronics, relying on efficient and secure communications to ensure reliable services. While Reconfigurable Intelligent Surfaces (RIS) offer enhanced support for high-rate vehicular communication, existing RIS-aided scheduling schemes remain inefficient in delay-sensitive multi-task IoV scenarios and lack resilience against emerging AI-driven threats in consumer electronic systems. To overcome these limitations, this paper proposes a secure and communication-enhanced task offloading and resource allocation scheme for vehicular consumer electronic networks. First, we introduce an intelligent RIS-assisted vehicle model that jointly optimizes task dependencies, resource allocation, and RIS configuration under adversarial disturbances to improve transmission rate and communication quality. Second, to efficiently solve the resulting high-dimensional and non-convex optimization problem involving RIS phase shifts, we develop a Quantum-based Reinforcement Learning (QRL) framework. QRL encodes state spaces with quantum bits and embeds quantum circuits into neural networks, accelerating convergence and strengthening robustness against adversarial attacks. Experimental results show that QRL outperforms the Soft Actor-Critic (SAC) method in jointly optimizing offloading and allocation under security constraints. The proposed framework demonstrates strong generalization potential for various RIS-assisted mobile computing applications, especially in environments vulnerable to AI-powered threats.