Dependable Task Offloading for CAVs in Web3: An Intelligence-Based Active Inference Approach

Abstract

Connected and autonomous vehicles (CAVs) enhance traffic efficiency and safety via massive data-driven computation and decision-making. The computational demands of massive data challenge centralized cloud networks, leading to a novel CAV paradigm supported by mobile edge computing (MEC) and built on Web3. CAVs in Web3 can efficiently and securely offload compute-intensive tasks to edge devices in a decentralized and self-controlled manner, necessitating dependable task offloading schemes. However, existing deep reinforcement learning (DRL)-based offloading schemes face two challenges: overlooking security risks like privacy exposure in dependability definitions, while being constrained by reward function formulation, resulting in poor generalization. In this paper, we propose a dependable offloading scheme based on intelligence and active inference for CAVs in Web3. First, we introduce a dependable offloading framework utilizing double-layer blockchain and decentralized identifiers to ensure offloading source dependability. Then, by introducing a security-measuring dependability metric called cost from energy consumption, delay, and privacy exposure risk (cEDP), we formulate the dependable offloading optimization problem from an intelligence and active inference perspective, enabling higher-level environmental cognition without rewards. The problem is solved by the proposed intelligence-based active inference (INAI) algorithm. Experimental results demonstrate that reward-free INAI outperforms mainstream DRL and heuristic approaches in convergence, efficiency, and generalization capabilities.