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dc.contributor.author Oyewobi, SS
dc.contributor.author Hancke, G
dc.contributor.author Abu-Mahfouz, Adnan MI
dc.contributor.author Onumanyi, AJ
dc.date.accessioned 2019-11-08T10:18:07Z
dc.date.available 2019-11-08T10:18:07Z
dc.date.issued 2019-03
dc.identifier.citation Oyewobi, S.S. 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, pp. 1-21 en_US
dc.identifier.isbn 978-1-7281-3666-0
dc.identifier.isbn 978-1-7281-3667-7
dc.identifier.issn 1424-8220
dc.identifier.uri https://www.mdpi.com/1424-8220/19/6/1395
dc.identifier.uri DOI: 10.3390/s19061395
dc.identifier.uri http://hdl.handle.net/10204/11208
dc.description Copyright 2019 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. en_US
dc.description.abstract The 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_US
dc.language.iso en en_US
dc.publisher MDPI en_US
dc.relation.ispartofseries Workflow;22665
dc.subject Channel selection strategy en_US
dc.subject Cognitive radio en_US
dc.subject Dynamic spectrum access en_US
dc.subject Industrial-internet of Things en_US
dc.subject Reinforcement learning en_US
dc.subject Spectrum handoff en_US
dc.title An effective spectrum handoff based on reinforcement learning for target channel selection in the industrial Internet of Things en_US


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