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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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 |
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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 |
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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. |
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School of Computer Science and Engineering |
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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 |
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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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1695706149750308864 |