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A Clustering Routing Protocol for WSN Based on Type-2 Fuzzy Logic and Ant Colony Optimization

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Abstract

Aiming at the problem of load balancing and lifetime prolonging for wireless sensor networks (WSNs), and considering complex uncertainties existed in WSNs, this paper proposes a clustering routing protocol CRT2FLACO for WSN based on type-2 fuzzy logic and ant colony optimization (ACO). Specifically, in the cluster set-up phase, a type-2 Mamdnai fuzzy logic system (T2MFLS) is built to handle uncertainties better and balance the network load, in which three important factors—residual energy, the number of neighbor nodes and the distance to the base station (BS) of a node—are considered as inputs, and the probability of the node to be a candidate cluster head (CH) and the CH competition radius as outputs of our T2MFLS, to select the final CHs; in the steady-state phase, in order to reduce the transmission consumption, all the CHs are linked into a chain using ACO algorithm, then each CH send its data packet to the leader along link, which is a CH eventually transmitting packets to the BS. The simulation results show that the proposed routing protocol can effectively balance network load and reduce the transmission energy consumption of CHs, thus greatly prolong the lifetime of WSN.

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Acknowledgments

The authors would like to thank the reviewers for their very valuable recommendations. This work is supported by National Natural Science Foundation of China (Grant Nos. 61403011 and 61327807).

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Authors and Affiliations

  1. The School of Mathematics and Systems Science, LMIB of the Ministry of Education, Beihang University, Beijing, 100191, People’s Republic of China

    Qi-Ye Zhang, Ze-Ming Sun & Feng Zhang

  2. The Department of Basic Course, Beijing Institute of Fashion and Technology, Beijing, 100029, People’s Republic of China

    Wei-Xian Xie

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  1. Wei-Xian Xie
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Correspondence to Qi-Ye Zhang.

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Xie, WX., Zhang, QY., Sun, ZM. et al. A Clustering Routing Protocol for WSN Based on Type-2 Fuzzy Logic and Ant Colony Optimization. Wireless Pers Commun 84, 1165–1196 (2015). https://doi.org/10.1007/s11277-015-2682-x

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