F3N: An Intelligent Fuzzy-Based Cluster Head Selection System for WSNs and Its Performance Evaluation

Donald Elmazi, Evjola Spaho, Keita Matsuo, Tetsuya Oda, Makoto Ikeda and Leonard Barolli
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Donald Elmazi: Polytechnic University of Tirana, Tirana, Albania
Evjola Spaho: Polytechnic University of Tirana, Tirana, Albania
Keita Matsuo: Fukuoka Prefectural Fukuoka Technical High School, Fukuoka, Japan
Tetsuya Oda: Fukuoka Institute of Technology, Fukuoka, Japan
Makoto Ikeda: Fukuoka Institute of Technology, Fukuoka, Japan
Leonard Barolli: Fukuoka Institute of Technology, Fukuoka, Japan

International Journal of Distributed Systems and Technologies (IJDST), 2015, vol. 6, issue 2, 28-44

Abstract: Sensor networks supported by recent technological advances in low power wireless communications along with silicon integration of various functionalities are emerging as a critically important computer class that enable novel and low cost applications. There are many fundamental problems that Wireless Sensor Networks (WSNs) research will have to address in order to ensure a reasonable degree of cost and system quality. Cluster formation and cluster head selection are important problems in WSN applications and can drastically affect the net- work's communication energy dissipation. However, selecting of the cluster head is not easy in different environments which may have different characteristics. In this paper, in order to deal with this problem, the authors propose a power reduction algorithm for WSNs based on Fuzzy Logic (FL) and Number of Neighbour Nodes (3N). They call this system F3N. The authors evaluate F3N and LEACH by many simulation results. The performance of F3N system is evaluated for tree different parameters: Remaining Battery Power of Sensor (RPS); Degree of Number of Neighbour Nodes (D3N); and Distance from Cluster Centroid (DCC). From the simulation results, they found that the probability of a sensor node to be a cluster head is increased with increase of number of neighbour nodes and remained battery power and is decreased with the increase of distance from the cluster centroid.

Date: 2015
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