Cloud-Blockchain Integrated Privacy-Preserving Distributed Computation Offloading Scheme in the Internet of Vehicles

INTRODUCTION: With the rapid development of the Internet of Vehicles technology, on-board applications in intelligent vehicles are increasingly facing the challenge of processing latency-sensitive and computationally intensive tasks. Although vehicle-mounted computing units possess certain computing capabilities, their limited resources cannot cope with complex latency-sensitive tasks. Offloading computations to resource-rich cloud servers is a feasible solution to this problem. However, transmission costs and communication delays on remote cloud servers become major bottlenecks for offloading latency-sensitive tasks. The emergence of Mobile Edge Computing (MEC) shifts computing power from centralized cloud servers to the network edge, reducing latency. Nevertheless, existing schemes still suffer from poor fault tolerance, privacy leakage, and low computational efficiency. OBJECTIVES: To address these limitations, this paper proposes a cloud-blockchain integrated privacy-preserving distributed computation offloading scheme (FCOS). METHODS: FCOS significantly improves fault tolerance via a redundant distributed computing mechanism, effectively mitigating the impact of single-point failures on computations. RESULTS: Smart contracts on the blockchain guarantee verifiability and fairness throughout the computation offloading process, enhancing overall offloading security. For privacy protection, the scheme combines homomorphic encryption and blind factor technology to protect data privacy with low computational overhead on intelligent vehicle terminals. CONCLUSION: Compared with existing methods, FCOS reduces the average computation time by 90.4% for polynomial calculations within 5000 iterations in the cloud computing phase. Under a single-point failure rate ranging from 0% to 60%, the average computation time for polynomial calculations within 10,000 iterations is reduced by 92.9% to 96.7%.