Abstract
With the emergence and development of 5G technology, Mobile Edge Computing (MEC) has been closely integrated with Internet of Vehicles (IoV) technology, which can effectively support and improve network performance in IoV. However, the high-speed mobility of vehicles and diversity of communication quality make computing task offloading strategies more complex. To solve the problem, this paper proposes a computing resource allocation scheme based on deep reinforcement learning network for mobile edge computing scenarios in IoV. Firstly, the task resource allocation model for IoV in corresponding edge computing scenario is determined regarding the computing capacity of service nodes and vehicle moving speed as constraints. Besides, the mathematical model for task offloading and resource allocation is established with the minimum total computing cost as objective function. Then, deep Q-learning network based on deep reinforcement learning network is proposed to solve the mathematical model of resource allocation. Moreover, experience replay method is used to solve the instability of nonlinear approximate function neural network, which can avoid falling into dimension disaster and ensure the low-overhead and low-latency operation requirements of resource allocation. Finally, simulation results show that proposed scheme can effectively allocate the computing resources of IoV in edge computing environment. When the number of user uploaded data is 10K bits and the number of terminals is 15, it still shows the excellent network performance of low-overhead and low-latency.
Highlights
In recent years, the automobile industry has brought tremendous changes to people's lives under the impetus transformation of information and communication technology
In this paper, oriented to the precise needs of mobility characteristics and task allocation for Internet of Vehicles (IoV) users, drawing on the existing task management research of Mobile Edge Computing (MEC), this paper proposes a computing resource allocation scheme using deep reinforcement learning in edge computing environment
The main contributions of this paper are as follows: 1) In order to clarify the mathematical model of MEC task distribution algorithm proposed in this paper, this paper considers the computing power of service nodes and vehicle speed on the basis of determining system network model, computing model and communication model of task offloading and resource allocation
Summary
The automobile industry has brought tremendous changes to people's lives under the impetus transformation of information and communication technology. The applications equipped on vehicles can provide drivers and passengers with more useful help information, such as safety information, surrounding environmental conditions and traffic information [1,2,3]. The emergence of Internet of Vehicles (IoV) can integrate information provided by multiple applications to solve many problems in transportation [4,5,6]. The mobile network is connected to the backup network, which serves applications such as vehicle safety, traffic control, information services and user network access. It aims to establish an intelligent comprehensive network system that improves traffic conditions and travel efficiency, and expands information interaction forms
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