GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks

Muhammad K. Shahzad, S. M. Riazul Islam, Mahmud Hossain, Mohammad Abdullah-Al-Wadud, Atif Alamri and Mehdi Hussain
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Muhammad K. Shahzad: Department of Computing, National University of Sciences and Technology, Islamabad 44000, Pakistan
S. M. Riazul Islam: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
Mahmud Hossain: Department of Computer Science, University of Alabama at Birmingham (UAB), Birmingham, AL 35294, USA
Mohammad Abdullah-Al-Wadud: Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Atif Alamri: Research Chair of Pervasive and Mobile Computing, King Saud University, Riyadh 11543, Saudi Arabia
Mehdi Hussain: Department of Computing, National University of Sciences and Technology, Islamabad 44000, Pakistan

Mathematics, 2020, vol. 9, issue 1, 1-18

Abstract: In recent years, the deployment of wireless sensor networks has become an imperative requisite for revolutionary areas such as environment monitoring and smart cities. The en-route filtering schemes primarily focus on energy saving by filtering false report injection attacks while network lifetime is usually ignored. These schemes also suffer from fixed path routing and fixed response to these attacks. Furthermore, the hot-spot is considered as one of the most crucial challenges in extending network lifetime. In this paper, we have proposed a genetic algorithm based fuzzy optimized re-clustering scheme to overcome the said limitations and thereby minimize the effect of the hot-spot problem. The fuzzy logic is applied to capture the underlying network conditions. In re-clustering, an important question is when to perform next clustering. To determine the time instant of the next re-clustering (i.e., number of nodes depleted—energy drained to zero), associated fuzzy membership functions are optimized using genetic algorithm. Simulation experiments validate the proposed scheme. It shows network lifetime extension of up to 3.64 fold while preserving detection capacity and energy-efficiency.

Keywords: wireless sensor networks; fuzzy logic systems; genetic algorithms; optimization; en-route filtering; network lifetime; re-clustering (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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