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      Mobility-Aware Node Clustering with Fuzzy Logic for Wireless Mesh Network

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      https://www.riss.kr/link?id=A102885720

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      다국어 초록 (Multilingual Abstract)

      In recent times, Wireless Mesh Network (WMN) has received prominence worldwide due to the evolution of wireless networks as a ubiquitous and seamless broadband service provider. Routing on WMNs is one of the most prominent research issues on these day...

      In recent times, Wireless Mesh Network (WMN) has received prominence worldwide due to the evolution of wireless networks as a ubiquitous and seamless broadband service provider. Routing on WMNs is one of the most prominent research issues on these days. Hierarchical clustering based routing protocols are proposed by the researchers for large networks. In these protocols, all the available nodes are grouped into clusters. Each clus-ter consists of cluster members, cluster head and gateway nodes where cluster head plays the most vital role to form an efficient cluster. Selection of a better cluster head depends on the decisions made from different parameters and their relations. In this situation, fuzzy logic suits better by providing better analysis and helps to make the right decision based on different parameter values and their relations. In this paper, a fuzzy logic based hierarchical clustering technique is proposed for WMN. The proposed technique along with the existing Fuzzy Logic Control Clustering Algorithm(FLCCA) are experimented using the simulation software NS3 and the simulated results are compared to establish the efficiency of the proposed technique.

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      목차 (Table of Contents)

      • Abstract
      • 1. Introduction
      • 2. Related Study
      • 2.1. Hierarchical Cluster Based Routing for WMN [12]
      • 2.2. Fuzzy Logic Control Clustering Algorithm (FLCCA)
      • Abstract
      • 1. Introduction
      • 2. Related Study
      • 2.1. Hierarchical Cluster Based Routing for WMN [12]
      • 2.2. Fuzzy Logic Control Clustering Algorithm (FLCCA)
      • 3. Proposed Method
      • 3.1. Fuzzy Input and Output Parameters
      • 3.2. Fuzzy Logic Inference Engine and Fuzzy Rule Base
      • 3.3. Defuzzification
      • 3.4. Clusterhead Selection and Cluster Maintenance Algorithm
      • 4. Results and Discussion
      • 4.1. no. of Clusterheads (CHs)
      • 4.2. Cluster Stability
      • 4.3. Clustering Overheads
      • 5. Conclusion
      • References
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