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Histogram partitioning algorithms for adaptive and autonomous threshold estimation in cognitive radio–based industrial wireless sensor networks

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dc.contributor.author Onumanyi, A
dc.contributor.author Abu-Mahfouz, Adnan MI
dc.contributor.author Hancke, GP
dc.date.accessioned 2019-11-18T05:50:56Z
dc.date.available 2019-11-18T05:50:56Z
dc.date.issued 2019-07
dc.identifier.citation Onumanyi, A., Abu-Mahfouz, A.M.I. & Hancke, G.P. 2019. Histogram partitioning algorithms for adaptive and autonomous threshold estimation in cognitive radio–based industrial wireless sensor networks. Transactions on Emerging Telecommunications Technologies, vol 30(10), pp 1-15 en_US
dc.identifier.issn 2161-3915
dc.identifier.uri https://doi.org/10.1002/ett.3679
dc.identifier.uri https://onlinelibrary.wiley.com/doi/full/10.1002/ett.3679
dc.identifier.uri http://hdl.handle.net/10204/11221
dc.description Copyright: 2019 Wiley Online Library. Due to copyright restrictions, the attached PDF file contains the abstract version of the full-text item. For access to the full-text item, please consult the publisher's website. The definitive version of the work is published in the Transactions on Emerging Telecommunications Technologies, vol 30(10), pp. 1-15 en_US
dc.description.abstract Modern energy detectors typically use adaptive threshold estimation algorithms to improve signal detection in cognitive radio–based industrial wireless sensor networks (CR‐IWSNs). However, a number of adaptive threshold estimation algorithms often perform poorly under noise uncertainty conditions since they are typically unable to auto‐adapt their parameter values per changing spectra conditions. Consequently, in this paper, we have developed two new algorithms to accurately and autonomously estimate threshold values in CR‐IWSNs under dynamic spectra conditions. The first algorithm is a parametric‐based technique termed the histogram partitioning algorithm, whereas the second algorithm is a fully autonomous variant termed the mean‐based histogram partitioning algorithm. We have evaluated and compared both algorithms with some well‐known methods under different CR sensing conditions. Our findings indicate that both algorithms maintained over 90% probability of detection in both narrow and wideband sensing conditions and less than 10% probability of false alarm under noise‐only conditions. Both algorithms are quick and highly scalable with a time complexity of O(V), where V is the total number of input samples. The simplicity, effectiveness, and viability of both algorithms make them typically suited for use in CR‐IWSN applications. en_US
dc.language.iso en en_US
dc.publisher Wiley Online Library en_US
dc.relation.ispartofseries Workflow;22787
dc.subject Industrial wireless sensor network en_US
dc.subject Cognitive radio technologies en_US
dc.subject Spectrum sensing en_US
dc.title Histogram partitioning algorithms for adaptive and autonomous threshold estimation in cognitive radio–based industrial wireless sensor networks en_US
dc.type Article en_US


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