Determining the State of the Sensor Nodes Based on Fuzzy Theory in WSNs
AbstractThe low-cost, limited-energy, and large-scale sensor nodes organize wireless sensor networks (WSNs). Sleep scheduling algorithms are introduced in these networks to reduce the energy consumption of the nodes in order to enhance the networklifetime. In this paper, a novel fuzzy method called Fuzzy Active Sleep (FAS) is proposed to activate the appropriate nodes of WSNs. It uses the selection probability of nodes based on their remaining energy and number of previous active state. Theproposed method focuses on a balanced sleep scheduling in order to belong the network lifetime. Simulation results show that the proposed method is more efficient and effective than the compared methods in terms of average network remaining energy, number of nodes still alive, number of active state, and network lifetime.
 Zhang, P. et al (2013); Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy-harvesting sensors, Comput Netw, ISSN 1389-1286, 57(14): 2689-2704.
 Cerami, M.; Straccia, U. (2013); On the (un)decidability of fuzzy description logics under Lukasiewicz t-norm, Inform Sciences, ISSN 0020-0255, 227: 1-21.
 Chiasserini, C.F.; Garetto, M. (2004); Modeling the performance of wireless sensor networks, Proc. IEEE Infocom ser, ISSN 0743-166X, 220-231.
 Xiao, Y. et al (2007); Modeling detection metrics in randomized scheduling algorithm in wireless sensor networks, Proc. IEEE WCNC, ISSN 1525-3511, 3741-3745.
 Liu, J. et al (2010); Analysis of random sleep scheme for wireless sensor networks, International Journal of Sensor Networks, ISSN 1748-1279, 7(1): 71-84.
 Xiao, Y. et al (2010); Coverage and detection of a randomized scheduling algorithm in wireless sensor networks, IEEE T Comput, ISSN 0018-9340, 59(4): 507-521.
 Keh, H.C. et al (2011); Power saving mechanism with optimal sleep control in wireless sensor networks, Tamkang J. Sci. Eng, ISSN 1560-6686, 14(3): 235-243.
 Li, W.W. (2011); Several characteristics of active/sleep model in wireless sensor networks, Proc. IEEE NTMS'4, ISSN 2157-4952, 1-5.
 Zhang, Y.; Li, W. (2012); Modeling and energy consumption evaluation of a stochastic wireless sensor network, Eurasip J Wirel Comm, ISSN 1687-1499, 2012(1): 1-11.
 Zhao, J.; Bose, B.K. (2002); Evaluation of membership functions for fuzzy logic controlled induction motor drive, Proc. IEEE IECON'2, 1: 229-234.
 Alcalá, R. et al (1999); Approximate Mamdani-type Fuzzy Rule-Based Systems: Features and Taxonomy of Learning Methods, Citeseer, Technical Report DECSAI-990117, pp.1-23.
 Runkler, T.A. (1997); Selection of appropriate defuzzification methods using application specific properties, IEEE T Fuzzy Syst, ISSN 1063-6706, 5(1): 72-79.
 Heinzelman, W.B. et al (2002); An application-specific protocol architecture for wireless microsensor networks, IEEE T Wirel Commun, ISSN 1536-1276, 1(4): 660-670.
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