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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8938706/ |
| _version_ | 1818353487396732928 |
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| author | Denghui Wang Qingmiao Zhang Jian Liu Dezhong Yao |
| author_facet | Denghui Wang Qingmiao Zhang Jian Liu Dezhong Yao |
| author_sort | Denghui Wang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-13T19:10:19Z |
| format | Article |
| id | doaj.art-68c4c3171e12477da8e662a21e4d81aa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T19:10:19Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-68c4c3171e12477da8e662a21e4d81aa2022-12-21T23:34:26ZengIEEEIEEE Access2169-35362019-01-01718573018573910.1109/ACCESS.2019.29613318938706A Novel QoS-Awared Grid Routing Protocol in the Sensing Layer of Internet of Vehicles Based on Reinforcement LearningDenghui Wang0https://orcid.org/0000-0002-2497-9036Qingmiao Zhang1https://orcid.org/0000-0002-6435-767XJian Liu2https://orcid.org/0000-0002-8367-7062Dezhong Yao3https://orcid.org/0000-0003-0336-0522Department of Information Engineering, East China Jiaotong University, Nanchang, ChinaDepartment of Information Engineering, East China Jiaotong University, Nanchang, ChinaDepartment of Information Engineering, East China Jiaotong University, Nanchang, ChinaRR@NTU Corporate Laboratory, School of Computer Science and Engineering, Nanyang Technological University, SingaporeThis 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.https://ieeexplore.ieee.org/document/8938706/WMSNgridreinforcement learningQoS |
| spellingShingle | Denghui Wang Qingmiao Zhang Jian Liu Dezhong Yao A Novel QoS-Awared Grid Routing Protocol in the Sensing Layer of Internet of Vehicles Based on Reinforcement Learning IEEE Access WMSN grid reinforcement learning QoS |
| title | 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_short | 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 |
| topic | WMSN grid reinforcement learning QoS |
| url | https://ieeexplore.ieee.org/document/8938706/ |
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