Optimizing Resource Allocation and Energy Efficiency in Vehicle Mobile-Edge Computing With Blockchain Integration
Yongsheng Cao, Caiping Zhao, Yihong Zhang, Yaohui Jin · IEEE Internet of Things Journal · 2025
The availability of conventional mobile edge computing (MEC) for vehicles is often hindered by signal interference and attenuation, limiting its efficiency in supporting computationally intensive and latency-sensitive applications. To address these challenges, we propose a novel blockchainenabled vehicular mobile edge computing (VMEC) system that enhances resource sharing and energy efficiency in electric vehicle (EV)-centric services. The system employs an improved RAFT-based consensus mechanism (mRAFT), which dynamically evaluates the reputation of access point (AP) nodes based on their available resources, ensuring fair leader election and enhancing consensus reliability and efficiency. Furthermore, a probabilistic model is introduced to describe AP behaviors, improving the security of the consensus process. To minimize overall energy consumption, we develop a decentralized optimization framework using the Alternating Direction Method of Multipliers (ADMM). This framework jointly optimizes AP clustering, computation resource allocation, and bandwidth scheduling to achieve energy-efficient task offloading and consensus. Simulation results demonstrate that the proposed VMEC system reduces latency by 29.53 and energy consumption by 43.43 schemes, showcasing its effectiveness in delivering low-latency, energy-efficient services for advanced vehicular applications.