PSO-Least Squares SVM for Clustering in Cognitive Radio Sensor Networks

Abstract : In this paper, a solution for a cluster formation in cognitive radio networks is presented. The solution features a network-wide energy consumption model for these networks. The particle swarm optimisation (PSO) and least squares support vector machines (LS-SVMs) have been transformed into our clustering problem. The obtained results show that the given hybrid AI system provides a good estimate of a cluster formation. Through extensive simulations, we observed that the PSO-LS-SVM method can be effectively used under various spectrum characteristics. Moreover, the formed clusters are reliable and stable in a dynamic frequency environment.
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Richard Chbeir; Yannis Manolopoulos; Ilias Maglogiannis; Reda Alhajj. 11th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2015), Sep 2015, Bayonne, France. IFIP Advances in Information and Communication Technology, AICT-458, pp.91-102, 2015, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-23868-5_7〉
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Jerzy Martyna. PSO-Least Squares SVM for Clustering in Cognitive Radio Sensor Networks. Richard Chbeir; Yannis Manolopoulos; Ilias Maglogiannis; Reda Alhajj. 11th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2015), Sep 2015, Bayonne, France. IFIP Advances in Information and Communication Technology, AICT-458, pp.91-102, 2015, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-23868-5_7〉. 〈hal-01385347〉

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