Journal
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 72, Issue 2, Pages 1449-1463Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2022.3210570
Keywords
Internet of Vehicles; mobile edge computing; blockchain; resource allocation; asynchronous advantage actor-critic
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This paper introduces the application of blockchain technology in IoT vehicle's mobile edge computing system to solve the issues of data transmission privacy and authenticity, and designs an intelligent resource allocation framework to reduce energy consumption and improve the transactional throughput of the blockchain. The asynchronous advantage actor-critic approach is introduced to deal with the optimization problem. Simulation results show significant advantages of the proposed scheme over other comparison schemes.
Recently, the rise of the Internet of Vehicles (IoV) has driven the broad development of intelligent transportation and smart cities. In order to promote the computing power of mobile vehicles and decrease the content delivery latency of suppliers, mobile edge computing (MEC) is recognized as a promising computational paradigm and used in vehicular networks. However, there are some essential issues to be considered: 1) privacy and authenticity of data transmission in IoV, and 2) reasonable resource allocation for collaborative computing and caching. In this paper, to solve above issues, blockchain technology is introduced and adopted to ensure the accuracy and reliability of data transmission and interaction. Meanwhile, we develop an intelligent framework of resource allocation about computing and caching for blockchain-enabled MEC systems in IoV. Through jointly considering and optimizing offloading decision of computation task carried by vehicle, caching decision, the number of offloaded consensus nodes, block interval and block size, the weighted consumption costs of energy consumption and computation overheads can be decreased, and the transactional throughput of the blockchain can be increased. Moreover, due to the continuity and dynamic of the available resources of mobile vehicles and computing servers, the optimization problem is modeled as a Markov decision process (MDP). Facing the large-scale and dynamic characteristics of the system, the asynchronous advantage actor-critic (A3C) approach is introduced to deal with the optimization problem. Simulation results show that our proposed scheme achieves significant advantages over other comparison schemes, such as the total reward of the proposed scheme is about 14% higher than that of the deep Q-network based scheme.
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