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Article type: Research Article
Authors: Carmalatta, J.a | Diwakaran, S.b | Uma Maheswari, P.c | Raja, S.d | Robinson, Y. Harolde | Julie, E. Goldenf | Kumar, Raghvendrag | Son, Le Hoangh; * | Le, Chungi; * | Tung, Nguyen Thanhj | Long, Hoang Vietk
Affiliations: [a] Department of Electronics and Communication Engineering, SCAD College of Engineering and Technology, Tirunelveli, India | [b] Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, India | [c] Department of Electronics and Communication Engineering, PMR Engineering College, India | [d] Department of Mathematics, Amrita College of Engineering and Technology, Nagarcoil, India | [e] Department of Computer Science and Engineering, Francis Xavier Engineering College, Tirunelveli, India | [f] Department of Computer Science and Engineering, Anna University Regional Campus, Tirunelveli, India | [g] Department of Computer Science and Engineering, GIET University, India | [h] VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam | [i] Institute of Research and Development and School of Computer Science, Duy Tan University, Da Nang, Vietnam | [j] VNU International University, Vietnam National University, Hanoi | [k] Faculty of Information Technology, University of Technology-Logistics of Public Security, Bac Ninh, Vietnam
Correspondence: [*] Corresponding author. Le Hoang Son, VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam. E-mail: [email protected]; Chung Le, Institute of Research and Technology and School of Computer Science, Duy Tan University, Da Nang 550000, Vietnam, E-mail: [email protected].
Abstract: In Passive Clustered Wireless Sensor Networks (WSNs), energy is lost in a sensor node during the data transmission. In order to avoid the energy loss due to data transmission, a data prediction technique is implemented. In this paper, we present a new multi-point data prediction technique, in which the prediction algorithm is initially implemented at both member nodes and cluster heads. The algorithm is updated to cluster head by member nodes by tracking temporal correlation of data. Neuro-Fuzzy model is used as a predictor in both member nodes and cluster heads. The simulation is performed using MATLAB and the overall energy in nodes seems to increase. The mean square error (MSE) value is reduced to greater extend.
Keywords: Neuro-fuzzy, wireless sensor networks, clustering, cluster head, mean square error value, energy consumption.
DOI: 10.3233/JIFS-212214
Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1213-1228, 2023
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