期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 47, 期 3, 页码 772-783出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2016.2526683
关键词
Consensus; distributed algorithm; fuzzy c-means; hard clustering; k-means; soft clustering; wireless; sensor network (WSN)
类别
资金
- National Natural Science Foundation of China [61333012, 61473269]
- Fundamental Research Funds for the Central Universities [WK2100100023]
- Anhui Provincial Natural Science Foundation [1408085QF105]
- Australian Research Council [DP120104986]
This paper is concerned with developing a distributed k-means algorithm and a distributed fuzzy c-means algorithm for wireless sensor networks (WSNs) where each node is equipped with sensors. The underlying topology of the WSN is supposed to be strongly connected. The consensus algorithm in multiagent consensus theory is utilized to exchange the measurement information of the sensors in WSN. To obtain a faster convergence speed as well as a higher possibility of having the global optimum, a distributed k-means++ algorithm is first proposed to find the initial centroids before executing the distributed k-means algorithm and the distributed fuzzy c-means algorithm. The proposed distributed k-means algorithm is capable of partitioning the data observed by the nodes into measuredependent groups which have small in-group and large out-group distances, while the proposed distributed fuzzy c-means algorithm is capable of partitioning the data observed by the nodes into different measure-dependent groups with degrees of membership values ranging from 0 to 1. Simulation results show that the proposed distributed algorithms can achieve almost the same results as that given by the centralized clustering algorithms.
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