A Novel QoS-Awared Grid Routing Protocol in the Sensing Layer of Internet of Vehicles Based on Reinforcement Learning Article Swipe
Denghui Wang
,
Qingmiao Zhang
,
Jian Liu
,
Dezhong Yao
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2961331
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2961331
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.
Related Topics
Concepts
Computer science
Quality of service
Computer network
Reinforcement learning
Routing protocol
Reliability (semiconductor)
Grid
Distributed computing
The Internet
Routing (electronic design automation)
Artificial intelligence
World Wide Web
Physics
Quantum mechanics
Mathematics
Geometry
Power (physics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2019.2961331
- https://ieeexplore.ieee.org/ielx7/6287639/8600701/08938706.pdf
- OA Status
- gold
- Cited By
- 12
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2998566475
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2998566475Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2019.2961331Digital Object Identifier
- Title
-
A Novel QoS-Awared Grid Routing Protocol in the Sensing Layer of Internet of Vehicles Based on Reinforcement LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-01Full publication date if available
- Authors
-
Denghui Wang, Qingmiao Zhang, Jian Liu, Dezhong YaoList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2019.2961331Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8600701/08938706.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8600701/08938706.pdfDirect OA link when available
- Concepts
-
Computer science, Quality of service, Computer network, Reinforcement learning, Routing protocol, Reliability (semiconductor), Grid, Distributed computing, The Internet, Routing (electronic design automation), Artificial intelligence, World Wide Web, Physics, Quantum mechanics, Mathematics, Geometry, Power (physics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1, 2021: 3, 2020: 8Per-year citation counts (last 5 years)
- References (count)
-
30Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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