A novel QoS-awared grid routing protocol in the sensing layer of internet of vehicles based on reinforcement learning

This paper proposes a novel Quality of Service (QoS) grid routing protocol in Wireless Multimedia Sensor Networks (WMSN) based on reinforcement learning to guarantee Quality of Service in WMSN based on the sensing layer of the Internet of Vehicles (IoV). The sensing layer of IoV acquires abundant in...

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Main Authors: Wang, Denghui, Zhang, Qingmiao, Liu, Jian, Yao, Dezhong
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/146687
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1466872021-03-04T09:22:40Z A novel QoS-awared grid routing protocol in the sensing layer of internet of vehicles based on reinforcement learning Wang, Denghui Zhang, Qingmiao Liu, Jian Yao, Dezhong School of Computer Science and Engineering Rolls-Royce@NTU Corporate Lab Engineering::Computer science and engineering WMSN Grid This paper proposes a novel Quality of Service (QoS) grid routing protocol in Wireless Multimedia Sensor Networks (WMSN) based on reinforcement learning to guarantee Quality of Service in WMSN based on the sensing layer of the Internet of Vehicles (IoV). The sensing layer of IoV acquires abundant information to handle complex road traffic problems. Moreover, WMSN is rich in perceptual data. This suggests a need for complex acquisition, processing, storage, transfer of text and video data. These issues are elevated due, impart, increased requirements for QoS in WMSN. However, WMSN is heterogeneous, and its network topology is changing dynamically. Therefore, ensuring high QoS in a complex environment has become a challenge. This research suggests that least delay can be accomplished by calculating the distance among the nodes through grid identification number (GID) to acquire the nearest path from the source to the sink. Additionally, optimal grid coordinators with the highest reliability can be elected by making all the nodes in the grid for reinforcement learning to acquire their performance knowledge of reliability and delay. This enables high QoS performance in terms of reliability and end-to-end delay. The results indicate that the QoS of QoS-awared grid routing (QAGR) protocol is higher compared with the traditional grid-based clustering routing protocol. Published version 2021-03-04T09:22:40Z 2021-03-04T09:22:40Z 2019 Journal Article Wang, D., Zhang, Q., Liu, J., & Yao, D. (2019). A novel QoS-awared grid routing protocol in the sensing layer of internet of vehicles based on reinforcement learning. IEEE Access, 7, 185730-185739. doi:10.1109/ACCESS.2019.2961331 2169-3536 0000-0002-2497-9036 0000-0002-6435-767X 0000-0002-8367-7062 0000-0003-0336-0522 https://hdl.handle.net/10356/146687 10.1109/ACCESS.2019.2961331 2-s2.0-85077954523 7 185730 185739 en IEEE Access © 2020 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
WMSN
Grid
spellingShingle Engineering::Computer science and engineering
WMSN
Grid
Wang, Denghui
Zhang, Qingmiao
Liu, Jian
Yao, Dezhong
A novel QoS-awared grid routing protocol in the sensing layer of internet of vehicles based on reinforcement learning
description This paper proposes a novel Quality of Service (QoS) grid routing protocol in Wireless Multimedia Sensor Networks (WMSN) based on reinforcement learning to guarantee Quality of Service in WMSN based on the sensing layer of the Internet of Vehicles (IoV). The sensing layer of IoV acquires abundant information to handle complex road traffic problems. Moreover, WMSN is rich in perceptual data. This suggests a need for complex acquisition, processing, storage, transfer of text and video data. These issues are elevated due, impart, increased requirements for QoS in WMSN. However, WMSN is heterogeneous, and its network topology is changing dynamically. Therefore, ensuring high QoS in a complex environment has become a challenge. This research suggests that least delay can be accomplished by calculating the distance among the nodes through grid identification number (GID) to acquire the nearest path from the source to the sink. Additionally, optimal grid coordinators with the highest reliability can be elected by making all the nodes in the grid for reinforcement learning to acquire their performance knowledge of reliability and delay. This enables high QoS performance in terms of reliability and end-to-end delay. The results indicate that the QoS of QoS-awared grid routing (QAGR) protocol is higher compared with the traditional grid-based clustering routing protocol.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Wang, Denghui
Zhang, Qingmiao
Liu, Jian
Yao, Dezhong
format Article
author Wang, Denghui
Zhang, Qingmiao
Liu, Jian
Yao, Dezhong
author_sort Wang, Denghui
title A novel QoS-awared grid routing protocol in the sensing layer of internet of vehicles based on reinforcement learning
title_short A novel QoS-awared grid routing protocol in the sensing layer of internet of vehicles based on reinforcement learning
title_full A novel QoS-awared grid routing protocol in the sensing layer of internet of vehicles based on reinforcement learning
title_fullStr A novel QoS-awared grid routing protocol in the sensing layer of internet of vehicles based on reinforcement learning
title_full_unstemmed A novel QoS-awared grid routing protocol in the sensing layer of internet of vehicles based on reinforcement learning
title_sort novel qos-awared grid routing protocol in the sensing layer of internet of vehicles based on reinforcement learning
publishDate 2021
url https://hdl.handle.net/10356/146687
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