Journal
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 71, Issue 9, Pages 10044-10058Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2022.3182048
Keywords
Delays; Control systems; Reliability; Load management; Vehicle dynamics; Servers; Optimization; Internet of vehicles (IoV); mobile edge computing (MEC); software defined network (SDN); controller placement; multi-agent deep Q-learning networks (MADQN)
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Funding
- National Natural Science Foundation of China Project [62172441, 62172449, 61772553]
- Local Science and Technology Developing Fundation by Central Goverment through Free Exploration Project [2021Szvup166]
- Opening Project of State Key Laboratory of Nickel and Cobalt Resources Comprehensive Utilization [GZSYSKY-2020-033]
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This paper focuses on controller placement problem in Internet of Vehicles (IoV) using software-defined networks (SDN) and mobile-edge computing (MEC). A multi-objective optimization problem is investigated, and an algorithm based on multi-agent deep Q-learning networks (MADQN) is developed to efficiently solve the problem. The algorithm is accelerated using multi-process technology.
The flow fluctuations in the highly dynamic Internet of Vehicles (IoV) make the IoV difficult to provide reliable and scalable wireless network services for the emerging applications in the 5 G and beyond era. The software-defined networks (SDN) could feasibly manage and optimize the network according to the network load. Controller placement is a critical problem in SDN to achieve its robustness and flexibility with the changes of network status. Motivated by the advantages of SDN and Mobile-edge computing (MEC), this paper aims at enhancing the performance of IoV with the assistance of these two. Specifically, we consider a three-layer hierarchical SDN control plane for the IoV, where the SDN controllers are placed at the edge of networks. Under this framework, we investigate a multi-objective optimization problem on controller placement problem including delay, load balancing, and path reliability. To efficiently solve the formulated NP-hard problems, we develop an algorithm based on multi-agent deep Q-learning networks (MADQN) because of its advantages for large-scale combinatorial optimization. At last, we use multi-process technology to accelerate the operation of the algorithm, so as to complete the algorithm iteration faster. Numerical results show that the proposed methods achieve better performances than three baselines.
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