An effective spectrum handoff based on reinforcement learning for target channel selection in the industrial internet of things

dc.contributor.authorOyewobi, Stephen S.
dc.contributor.authorHancke, Gerhard P.
dc.contributor.authorAbu-Mahfouz, Adnan Mohammed
dc.contributor.authorOnumanyi, A.J. (Adeiza)
dc.date.accessioned2019-09-18T07:50:58Z
dc.date.available2019-09-18T07:50:58Z
dc.date.issued2019-03-21
dc.description.abstractThe overcrowding of the wireless space has triggered a strict competition for scare network resources. Therefore, there is a need for a dynamic spectrum access (DSA) technique that will ensure fair allocation of the available network resources for diverse network elements competing for the network resources. Spectrum handoff (SH) is a DSA technique through which cognitive radio (CR) promises to provide effective channel utilization, fair resource allocation, as well as reliable and uninterrupted real-time connection. However, SH may consume extra network resources, increase latency, and degrade network performance if the spectrum sensing technique used is ineffective and the channel selection strategy (CSS) is poorly implemented. Therefore, it is necessary to develop an SH policy that holistically considers the implementation of effective CSS, and spectrum sensing technique, as well as minimizes communication delays. In this work, two reinforcement learning (RL) algorithms are integrated into the CSS to perform channel selection. The first algorithm is used to evaluate the channel future occupancy, whereas the second algorithm is used to determine the channel quality in order to sort and rank the channels in candidate channel list (CCL). A method of masking linearly dependent and useless state elements is implemented to improve the convergence of the learning. Our approach showed a significant reduction in terms of latency and a remarkable improvement in throughput performance in comparison to conventional approaches.en_ZA
dc.description.departmentElectrical, Electronic and Computer Engineeringen_ZA
dc.description.librarianam2019en_ZA
dc.description.sponsorshipThe Council for Scientific and Industrial Research, South Africa (CSIR)en_ZA
dc.description.urihttp://www.mdpi.com/journal/sensorsen_ZA
dc.identifier.citationOyewobi, S.S., Hancke, G.P., Abu-Mahfouz, A.M. et al. 2019, 'An effective spectrum handoff based on reinforcement learning for target channel selection in the industrial internet of things', Sensors, vol. 19, no. 6, art. 1395, pp. 1-21.en_ZA
dc.identifier.issn1424-8220 (online)
dc.identifier.other10.3390/s19061395
dc.identifier.urihttp://hdl.handle.net/2263/71394
dc.language.isoenen_ZA
dc.publisherMDPI Publishingen_ZA
dc.rights© 2019 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.en_ZA
dc.subjectSpectrum handoffen_ZA
dc.subjectIndustrial-Internet of Things (IIoT)en_ZA
dc.subjectCognitive radio (CR)en_ZA
dc.subjectReinforcement learningen_ZA
dc.subjectInternet of Things (IoT)en_ZA
dc.subjectDynamic spectrum access (DSA)en_ZA
dc.subjectChannel selection strategy (CSS)en_ZA
dc.subjectCandidate channel list (CCL)en_ZA
dc.subjectWireless sensor network (WSN)en_ZA
dc.subjectLink qualityen_ZA
dc.subjectAllocationen_ZA
dc.subjectChallengesen_ZA
dc.titleAn effective spectrum handoff based on reinforcement learning for target channel selection in the industrial internet of thingsen_ZA
dc.typeArticleen_ZA

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