Journal
2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
Volume -, Issue -, Pages -Publisher
IEEE
DOI: 10.1109/GLOBECOM46510.2021.9685402
Keywords
Internet of Vehicles; blockchain; mobile edge computing (MEC); resource allocation; asynchronous advantage actor-critic (A3C) approach
Categories
Funding
- National Natural Science Foundation of China [61901011]
- Foundation of Beijing Municipal Commission of Education [KM202110005021]
- Beijing Natural Science Foundation [L202016]
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This paper investigates the blockchain-enabled MEC system in the Internet of Vehicles, utilizing blockchain technology to ensure secure data transmission and developing an intelligent resource framework for computing and caching to optimize resource allocation.
Nowadays, the rise of the Internet of Vehicles (IoV) has led to the rapid development of smart transportation. To increase the computing capacity of mobile vehicles and decrease the content delivery latency of suppliers, mobile edge computing (MEC) is considered as an indispensable solution. However, there are some essential issues to be considered: 1) security and privacy of data transmission, and 2) reasonable resource allocation for collaborative computing and caching. In this paper, to solve above issues, blockchain technology is adopted to ensure reliable transmission and interaction of data. Meanwhile, we develop an intelligent resource framework 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 energy consumption and computation overheads can be decreased, and the data throughput of the blockchain can be increased significantly. Moreover, the proposed optimization problem is modeled and formulated as a Markov decision process. Facing the complexity and dynamic of resource allocation, the asynchronous advantage actor-critic approach is considered and applied to solve the optimization problem. Experiment results demonstrate that the advantages of the proposed optimization scheme are obvious compared with other existing schemes.
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