Hybridized Fuzzy Based Clustering For Wireless Sensor Networks Based On Cognitive Internet Of Things
S.Suganthi Devi
S.Suganthi Devi, Lecturer, Department of Computer Engineering, Srinivasa Subbaraya Polytechnic College, puthur, Nagappatinam, Tamilnadu, India. [Deputed from Annamalai University].
Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 7876-7881 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7684118419/2019©BEIESP | DOI: 10.35940/ijrte.D7684.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Wireless sensor network explosive growth has increased demand for radio spectrum and has created problems with spectrum shortage since different wireless services and technologies have already been assigned the full range of wireless sensor networks. Cognitive radio has become a promising solution for resource-controlled wireless sensor network to access the reserved under-used frequency bands resourcefully. Artificial intelligence algorithms allow sensor nodes to avoid crowded congested bands by detecting under utilized licensed bands and to decide to adapt their transmission parameters. However, clusters are based on fixed spectrum distribution and cannot deal with the dynamic spectrum allocation required for future generation networks. Clusters are used to reduce power usage and support scalability of sensor networks. This article proposes an Hybridized Fuzzy Clustering (HFC), which groups adjacent nodes with comparable sets of idle channels and optimally forming power-efficient clusters based on three fuzzy energy parameters, proximity to the base station, and the level of the node to determine the possibility of each node being a cluster head.
Keywords: Wireless Sensor Network, Artificial Intelligence Algorithms.
Scope of the Article: Energy Harvesting and Transfer for Wireless Sensor Networks.