Advanced SearchSearch Tips
Entropy-based Correlation Clustering for Wireless Sensor Networks in Multi-Correlated Regional Environments
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Entropy-based Correlation Clustering for Wireless Sensor Networks in Multi-Correlated Regional Environments
Nga, Nguyen Thi Thanh; Khanh, Nguyen Kim; Hong, Son Ngo;
  PDF(new window)
The existence of correlation characteristics brings significant potential advantages to the development of efficient routing protocols in wireless sensor networks. This research proposes a new simple method of clustering sensor nodes into correlation groups in multiple-correlation areas. At first, the evaluation of joint entropy for multiple-sensed data is considered. Based on the evaluation, the definition of correlation region, based on entropy theory, is proposed. Following that, a correlation clustering scheme with less computation is developed. The results are validated with a real data set.
Entropy;Correlation clustering;Entropy correlation coefficient;Multi-correlation regions;
 Cited by
I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless Sensor Networks: A Survey", Computer Networks (Elsevier) Journal, vol. 38, no. 4, pp. 393-422, March 2002. crossref(new window)

D. Culler, D. E. M. Srivastava, "Overview of Sensor Network", IEEE Computer Magazine, vol. 37, no. 8, pp. 41-49, August 2004.

C. Siva Ram Murthy and B. Manoj, "Ad Hoc Wireless Networks: Architectures and Protocols", Prentice Hall, 2004.

J. N. Al-Karaki and A. E. Kamal, "Routing techniques in wireless sensor networks: a survey", IEEE Wireless Comm., vol. 11, pp. 6-28, 2004.

Ignacio Solis and Katia Obraczka, "Isolines: Energy efficient mapping in Sensor Networks", Proceedings of the 10th IEEE Symposium on Computers and Communications (ISCC'05), Cartagena, Spain, June 2005. Article (CrossRef Link)

A. Abbasi and M. Younis, "A Survey on Clustering Algorithms for Wireless Sensor Networks", Computer Communications, vol. 30, no. 14-15, pp. 2826-2841, 2007. crossref(new window)

N. Vlajic and D. Xia, "Wireless Sensor Networks: To Cluster or Not To Cluster?", in Proc. International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), June 2006.

Akyildiz, Ian F., Mehmet C. Vuran, and Ozgur B. Akan. "On exploiting spatial and temporal correlation in wireless sensor networks". Proceedings of WiOpt. Vol. 4. 2004.

Shakya, Rajeev K., Yatindra N. Singh, and Nishchal K. Verma. "Generic correlation model for wireless sensor network applications". IET Wireless Sensor Systems 3.4 (2013): 266-276. ) crossref(new window)

Rui Dai, Ian F. Akyildiz, A Spatial Correlation Model for Visual Information in Wireless Multimedia Sensor Networks, IEEE transaction on multimedia, vol. 11, No. 6, 10. 2009.

Becker, Hila. "A survey of correlation clustering". Advanced Topics in Computational Learning Theory (2005): 1-10.

Liu, Chong, Kui Wu, and Jian Pei. "A dynamic clustering and scheduling approach to energy saving in data collection from wireless sensor networks". SECON. Vol. 5. 2005.

D. Maeda, H. Uehara, and M. Yokoyama, Efficient Clustering Scheme Considering Non-uniform Correlation Distribution for Ubiquitous Sensor Networks, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 2007 E90-A(7):1344-1352.

N. T. T. Nga, H. Uehara, T. Ohira, "Attribute change adaptation routing protocol for energy efficiency of wireless sensor networks", ICITA 2009.

Taka, H., Uehara, H. and Ohira, T., Intermittent Transmission Method based on Aggregation Model for Clustering Scheme, Third International Conference on Ubiquitous and Future Networks (ICUFN), 2011, pp.107-111, Print ISBN 978-1-4577-1176-3, 15-17 June 2011.

Thomas M. Cover, Joy A. Thomas, "Elements of Information Theory", Copyright@1991 John Wiley & Sons, Inc. Print ISBN 0-471-06259-6 Online ISBN 0-471-20061-1 Chapter2 pp.13-49.

Cahill, Nathan D. "Normalized measures of mutual information with general definitions of entropy for multimodal image registration". Biomedical Image Registration. Springer Berlin Heidelberg, 2010. 258-268.

A.K. Jain, M.N. Murty, P.J. Flynn, Data Clustering: A Review, ACM Computing Surveys, Vol. 31, No. 3, 9.1999.

Intel Berkeley Research Lab