Increasing Energy Efficiency in Wireless Sensor Networks Using GA-ANFIS to Choose a Cluster Head and Assess Routing and Weighted Trusts to Demodulate Attacker Nodes

• Published in: Foundations of science Vol. 25; no. 4; pp. 1227 - 1246
• Main Authors: Al Hayali, Shaymaa, Rahebi, Javad, Ucan, Osman N., Bayat, Oguz
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.12.2020; Springer; Springer Nature B.V
• Subjects: Adaptive algorithms; Adaptive systems; Algorithms; Artificial neural networks; Clustering; Education; Energy; Energy efficiency; Energy management systems; Energy use; Fuzzy logic; Genetic algorithms; Life span; Mathematical Logic and Foundations; Methodology of the Social Sciences; Nodes; Packets (communication); Philosophy; Philosophy of Science; Sensors; Trustworthiness; Wireless networks; Wireless sensor networks; Canada; Wireless sensor network; LEACH; Fuzzy; Adaptive neuro-fuzzy inference system; Genetic algorithm
• ISSN: 1233-1821; 1572-8471
• DOI: 10.1007/s10699-019-09593-9

Demodulating harmful nodes and diminishing the energy waste in sensor nodes can prolong the lifespan of wireless sensor networks (WSNs). In this study, a genetic algorithm (GA) and an adaptive neuro fuzzy inference system were used to diminish the energy waste of sensors. Weighted trust evaluation was applied to search for harmful nodes in the network to prolong the lifespan of WSNs. A low-energy adaptive clustering hierarchy method was used to analyze the results. It was discovered that searching for harmful nodes with GA-ANFIS using weighted trust evaluation significantly increased the lifespan of WSNs. For evaluation of the proposed method we used the mean of energy of all sensors against of the round, data packets received in base station, minimum energy versus rounds and number of alive sensors versus rounds. Also, in this paper we compared the proposed method results with LEACH, LEACH-DT, Random, SIF and GA-Fuzzy methods. As results the proposed method has high life time than other methods. A representation of the overall system was implemented using MATLAB software.