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A Distributed and Energy-efficient Clustering Method for Hierarchical Wireless Sensor Networks

초록

영어

In the hierarchical wireless sensor network (WSN), cluster-based network architecture can enhance network self-control capability and resource efficiency, and prolong the whole network lifetime. Thus, clustering has also been a topic of interest in many different disciplines. Finding an energy-effective and efficient way to generate cluster is very important in WSN. We propose a distributed, energy-efficient and flexible clustering approach. Based on the hierarchical agglomerative clustering (HAC) method, the algorithm uses the qualitative connectivity data as input data, and tailor simple numerical method to generate a cluster tree. Simulation results demonstrate that this method is energy-effective and flexible, which can enhance network self-control capability and resource efficiency, and prolong the whole network lifetime.

목차

Abstract
 1. Introduction
 2. Distributed Hierarchical Agglomerative Clustering
  2.1 DHAC Introduction and Notations Definitions
  2.2 Input Data Set
  2.3 Build the Similarity Matrix
  2.4 Executing the Distributed Clustering Algorithm
  2.5 The Last Three Steps of DHAC
 3. Simulation and Performance Evaluation
 4. Conclusions
 Acknowledgments
 References

저자정보

  • Sai Ji Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, College of Computer & Software, Nanjing University of Information Science & Technology, The Aeronautic Key Laboratory for Smart Materials and Structures, Nanjing University of Aeronautics and Astronautics
  • Liping Huang Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, College of Computer & Software, Nanjing University of Information Science & Technology
  • Jin Wang Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, College of Computer & Software, Nanjing University of Information Science & Technology

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