Yin, M, Li, K and Zheng, M (2018) Spectrum Utilization of Cognitive Radio in Industrial Wireless Sensor Networks - A Review. In: ICSEE 2018, IMIOT 2018: Intelligent Computing and Internet of Things. 2018 International Conference on Intelligent Manufacturing and Internet of Things & International Conference on Intelligent Computing for Sustainable Energy and Environment, 21-23 Sep 2018, Eilat, Israel. Springer, Singapore , pp. 419-428. ISBN 978-981-13-2384-3
Abstract
The increasing demand for intelligent control and automation in industry requires better use of the radio spectrum due to the use of industrial wireless sensor networks (IWSNs). Cognitive Radio (CR) is a promising technology to improve the spectrum utilization by sensing spectrum holes. Research in this area is still in its infancy, but it is progressing rapidly. In this paper, industrial environment with different wireless technology, such as WirelessHART and ISA 100.11a is investigated. Various sensing schemes and the challenges associated for the cognitive radio are reviewed. In addition, the paper discussed the methods relevant to industrial applications, covering architecture, spectrum access, interference management, spectrum sensing and spectrum sharing.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Singapore Pte Ltd. 2018. This is a post-peer-review, pre-copyedit version of an article published in CCIS Vol 924. The final authenticated version is available online at: https://doi.org/10.1007/978-981-13-2384-3_39. |
Keywords: | IWSN; cognitive radio; spectrum sensing; spectrum utilization |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 31 Aug 2018 11:23 |
Last Modified: | 04 Sep 2019 00:42 |
Status: | Published |
Publisher: | Springer, Singapore |
Identification Number: | 10.1007/978-981-13-2384-3_39 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134481 |