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基于 FORESAM-FOG 范例的车联网资源分配机制。

FORESAM-FOG Paradigm-Based Resource Allocation Mechanism for Vehicular Clouds.

机构信息

Department of Computer Science, São Paulo State University-UNESP, São José do Rio Preto 15054-000, Brazil.

School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada.

出版信息

Sensors (Basel). 2021 Jul 24;21(15):5028. doi: 10.3390/s21155028.


DOI:10.3390/s21155028
PMID:34372265
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8348640/
Abstract

The Intelligent Transport Systems (ITS) has the objective quality of transportation improvement through transportation system monitoring and management and makes the trip more comfortable and safer for drivers and passengers. The mobile clouds can assist the ITS in handling the resource management problem. However, resource allocation management in an ITS is challenging due to vehicular network characteristics, such as high mobility and dynamic topology. With that in mind, we propose the FORESAM, a mechanism for resources management and allocation based on a set of FOGs which control vehicular cloud resources in the urban environment. The mechanism is based on a more accurate mathematical model (Multiple Attribute Decision), which aims to assist the allocation decision of resources set that meets the period requested service. The simulation results have shown that the proposed solution allows a higher number of services, reducing the number of locks of services with its accuracy. Furthermore, its resource allocation is more balanced the provided a smaller amount of discarded services.

摘要

智能交通系统(ITS)通过交通系统监控和管理具有提高交通质量的客观质量,使驾驶员和乘客的出行更加舒适和安全。移动云可以协助 ITS 处理资源管理问题。然而,由于车辆网络的特点,如高移动性和动态拓扑,ITS 中的资源分配管理具有挑战性。考虑到这一点,我们提出了 FORESAM,这是一种基于一组 FOG 的资源管理和分配机制,用于控制城市环境中的车辆云资源。该机制基于更精确的数学模型(多属性决策),旨在协助分配满足请求服务期限的资源集的决策。仿真结果表明,所提出的解决方案允许更多的服务,减少了具有其准确性的服务锁定数量。此外,它的资源分配更加平衡,提供了更少的丢弃服务。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/7c4dd1dc8f7f/sensors-21-05028-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/08833cb80fa4/sensors-21-05028-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/3721ca70e273/sensors-21-05028-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/c68976667164/sensors-21-05028-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/7b02b0571886/sensors-21-05028-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/ac34aadca3e2/sensors-21-05028-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/5b0fe93c92a5/sensors-21-05028-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/f67d3b2bab7c/sensors-21-05028-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/7c4dd1dc8f7f/sensors-21-05028-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/08833cb80fa4/sensors-21-05028-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/3721ca70e273/sensors-21-05028-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/c68976667164/sensors-21-05028-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/7b02b0571886/sensors-21-05028-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/ac34aadca3e2/sensors-21-05028-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/5b0fe93c92a5/sensors-21-05028-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/f67d3b2bab7c/sensors-21-05028-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8d1/8348640/7c4dd1dc8f7f/sensors-21-05028-g008.jpg

相似文献

[1]
FORESAM-FOG Paradigm-Based Resource Allocation Mechanism for Vehicular Clouds.

Sensors (Basel). 2021-7-24

[2]
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[3]
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Neural Netw. 2024-11

[4]
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Sensors (Basel). 2024-3-28

[5]
Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey.

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[6]
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PLoS One. 2023

[7]
A Low-Cost Resource Re-Allocation Scheme for Increasing the Number of Guaranteed Services in Resource-Limited Vehicular Networks.

Sensors (Basel). 2018-11-9

[8]
QoS-oriented high dynamic resource allocation in vehicular communication networks.

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[9]
A Micro-Level Compensation-Based Cost Model for Resource Allocation in a Fog Environment.

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[10]
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引用本文的文献

[1]
Extended DEMATEL method with intuitionistic fuzzy information: A case of electric vehicles.

PLoS One. 2024-12-19

[2]
Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles.

Sensors (Basel). 2021-11-4

本文引用的文献

[1]
RELIABLE: Resource Allocation Mechanism for 5G Network using Mobile Edge Computing.

Sensors (Basel). 2020-9-23

[2]
A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS.

Sensors (Basel). 2019-12-20

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