Node Clustering Based on Feature Correlation and Maximum Entropy for WSN

Recently, wireless sensor network (WSN) has been drawing a great deal of attention both in academia and industry. Numerous schemes have been developed to maximize the performance and reliability of WSN, and node clustering is commonly employed for efficient management of the sensor nodes. In this paper a novel node clustering scheme is proposed which is based on the correlation between the features collected from the nodes, while the features are weighted using the maximum entropy model. It allows efficient measurement of the similarity between the features, and thus proper node clustering is achieved. Extensive computer simulation demonstrates that the proposed scheme significantly outperforms the existing representative schemes in terms of Adjusted Rand Index, Fowlkes-Mallows Index, and relative effectiveness.

[1]  Hila Becker.  A Survey of Correlation Clustering , 2005 .

[2]  Daniel Minoli,et al.  Wireless Sensor Networks: Technology, Protocols, and Applications , 2007 .

[3]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[4]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[5]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[6]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[7]  Shu-Kun Lin.  Gibbs Paradox and the Concepts of Information, Symmetry, Similarity and Their Relationship , 2008, Entropy.

[8]  Yacine Challal,et al.  Energy efficiency in wireless sensor networks: A top-down survey , 2014, Comput. Networks.

[9]  Mitsuo Yokoyama,et al.  Efficient Clustering Scheme Considering Non-uniform Correlation Distribution for Ubiquitous Sensor Networks , 2007, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[10]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[11]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[12]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[13]  Hans-Peter Kriegel,et al.  Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering , 2009, TKDD.

[14]  GeunSik Jo,et al.  Error-Based Collaborative Filtering Algorithm for Top-N Recommendation , 2007, APWeb/WAIM.

[15]  Dario Pompili,et al.  Underwater acoustic sensor networks: research challenges , 2005, Ad Hoc Networks.

[16]  Elijah Blessing Rajsingh,et al.  Trust based data prediction, aggregation and reconstruction using compressed sensing for clustered wireless sensor networks , 2018, Comput. Electr. Eng..

[17]  Amit Konar,et al.  Metaheuristic Clustering , 2009, Studies in Computational Intelligence.

[18]  G. Krishna,et al.  Agglomerative clustering using the concept of mutual nearest neighbourhood , 1978, Pattern Recognit..

[19]  E. Massera,et al.  On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario , 2008 .

[20]  Kamalrulnizam Abu Bakar,et al.  Multipath Routing in Wireless Sensor Networks: Survey and Research Challenges , 2012, Sensors.

[21]  Daniel Müllner,et al.  Modern hierarchical, agglomerative clustering algorithms , 2011, ArXiv.

[22]  Sudipto Guha,et al.  CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.

[23]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[24]  Rich Caruana,et al.  Meta Clustering , 2006, Sixth International Conference on Data Mining (ICDM'06).