Blockchain-Based Multi-Access Edge Computing for Future Vehicular Networks: A Deep Compressed Neural Network Approach

Abstract

Vehicular ad hoc networks (VANETs) have become an important branch of future 6G smart wireless communications. As an emerging key technology, multi-access edge computing (MEC) provides low-latency, high-speed, and high-capacity network services for the VANETs. In this paper, we propose a novel framework for blockchain-based, hierarchical multi-access edge computing for the future VANET ecosystem (BMEC-FV). In the underlying VANET environment, we propose a trust model to ensure the security of the communication link between vehicles. Multiple MEC servers calculate the trust between vehicles through computing offloading. Meanwhile, the blockchain system plays an important role to manage the entire BMEC-FV architecture. We aim to optimize the throughput and the quality of services (QoS) for MEC users in the lower layer of the system architecture. In this framework, the main challenge is how to effectively reach consensus among blockchain nodes while ensuring the performance of MEC systems and blockchains. The blocksize of blockchain nodes, the number of consensus nodes, reliable features of each vehicle, and the number of producing blocks for each block producer are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Since it is difficult for this to be solved by traditional methods, we propose a novel deep compressed neural network scheme. Simulation results illustrate the superiority of the BMEC-FV ecosystem.