Journal
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 68, Issue 11, Pages 11158-11168Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2019.2935450
Keywords
Vehicle edge computing; resource allocation; IoT; deep reinforcement learning
Categories
Funding
- National Natural Science Foundation of China [61773126, 61727810, 61701125, 61603099, 61973087]
- Pearl River S&T Nova Program of Guangzhou [201806010176]
- European Union [824019]
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Mobile Edge Computing (MEC) is a promising technology to extend the diverse services to the edge of Internet of Things (IoT) system. However, the static edge server deployment may cause service hole in IoT networks in which the location and service requests of the User Equipments (UEs) may be dynamically changing. In this paper, we firstly explore a vehicle edge computing network architecture in which the vehicles can act as the mobile edge servers to provide computation services for nearby UEs. Then, we propose as vehicle-assisted offloading scheme for UEs while considering the delay of the computation task. Accordingly, an optimization problem is formulated to maximize the long-term utility of the vehicle edge computing network. Considering the stochastic vehicle traffic, dynamic computation requests and time-varying communication conditions, the problem is further formulated as a semi-Markov process and two reinforcement learning methods: Q-learning based method and deep reinforcement learning (DRL) method, are proposed to obtain the optimal policies of computation offloading and resource allocation. Finally, we analyze the effectiveness of the proposed scheme in the vehicular edge computing network by giving numerical results.
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