Onumanyi, Adeiza JAbu-Mahfouz, Adnan MIHancke, GP2019-11-182019-11-182019-07Onumanyi, 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 1-152161-3915https://doi.org/10.1002/ett.3679https://onlinelibrary.wiley.com/doi/full/10.1002/ett.3679http://hdl.handle.net/10204/11221Copyright: 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. 1-15Modern 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.enIndustrial wireless sensor networkCognitive radio technologiesSpectrum sensingArticleOnumanyi, A., Abu-Mahfouz, A. M., & Hancke, G. (2019). Histogram partitioning algorithms for adaptive and autonomous threshold estimation in cognitive radio–based industrial wireless sensor networks. http://hdl.handle.net/10204/11221Onumanyi, A, Adnan MI Abu-Mahfouz, and GP Hancke "Histogram partitioning algorithms for adaptive and autonomous threshold estimation in cognitive radio–based industrial wireless sensor networks." (2019) http://hdl.handle.net/10204/11221Onumanyi A, Abu-Mahfouz AM, Hancke G. Histogram partitioning algorithms for adaptive and autonomous threshold estimation in cognitive radio–based industrial wireless sensor networks. 2019; http://hdl.handle.net/10204/11221.TY - Article AU - Onumanyi, A AU - Abu-Mahfouz, Adnan MI AU - Hancke, GP AB - 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. DA - 2019-07 DB - ResearchSpace DP - CSIR KW - Industrial wireless sensor network KW - Cognitive radio technologies KW - Spectrum sensing LK - https://researchspace.csir.co.za PY - 2019 SM - 2161-3915 T1 - Histogram partitioning algorithms for adaptive and autonomous threshold estimation in cognitive radio–based industrial wireless sensor networks TI - Histogram partitioning algorithms for adaptive and autonomous threshold estimation in cognitive radio–based industrial wireless sensor networks UR - http://hdl.handle.net/10204/11221 ER -