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A Load Balancing Optimization Algorithm for Context-Aware Wireless Sensor Networks Based on Fuzzy Neural Networks
American Journal of Neural Networks and Applications
Volume 2, Issue 2, December 2016, Pages: 6-16
Received: Oct. 26, 2016; Accepted: Nov. 9, 2016; Published: Jan. 16, 2017
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Wencheng Zuo, College of Computer and Information Technology, China Three Gorges University, Yichang, China
Hui Xie, School of Law and Public Administration, China Three Gorges University, Yichang, China
Yuchi Lin, College of Computer and Information Technology, China Three Gorges University, Yichang, China
Hui Hu, School of Foreign Languages, China Three Gorges University, Yichang, China
Zhengying Cai, College of Computer and Information Technology, China Three Gorges University, Yichang, China
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In wireless sensor networks, the load imbalance will seriously affect the performance of the whole networks, such as local traffic overload, congestion, idle resources and other problems. In this paper, a novel fuzzy neural network algorithm is proposed to solve the problem. First, the problem of load balancing in context-aware wireless sensor networks is analyzed, and the mathematical model is built up. Second, a load balancing optimization algorithm is brought combing neural network and fuzzy theory, and the whole process is also illustrated including learning, association, recognition and information processing. Third, through analyzing and studying a case, a load balancing problem is solved by simulation and comparison to show the potential of the proposed method. Last, some interesting conclusions and future work are indicated at the end of the paper.
Context-Aware, Optimization Algorithm, Fuzzy Neural Networks, Load Balancing, Wireless Sensor Networks
To cite this article
Wencheng Zuo, Hui Xie, Yuchi Lin, Hui Hu, Zhengying Cai, A Load Balancing Optimization Algorithm for Context-Aware Wireless Sensor Networks Based on Fuzzy Neural Networks, American Journal of Neural Networks and Applications. Vol. 2, No. 2, 2016, pp. 6-16. doi: 10.11648/j.ajnna.20160202.11
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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