Study on Entropy Correlation in Wireless Sensor Networks for Energy Efficiency
Main Article Content
Abstract
Correlation characteristic can bring many significant potential advantages for the development of efficient communication protocols for wireless sensor networks. To exploit the correlation in WSNs, it is necessary to build the correlation model. However, most of the present correlation models only consider the linear and distance dependence correlation or computation complexity. This paper presents a novel entropy correlation model with less computation complexity that could be applied practically. Moreover, two energy efficient aggregation schemes including on-off scheme which offers an efficient way to choose representative nodes in a cluster with permitted distortion and compression scheme which reduces in-network message length suitable to high correlation data are also presented in this paper using the proposed correlation models.
Keywords
Entropy correlation coefficient, Correlation model, Compression, Representative node, Distortion
Article Details
References
[1] I. F. Akyildiz, et al. Wireless Sensor Networks: A Survey. Computer Networks (Elsevier) Journal, vol. 38, no. 4, (March 2002), 393-422.
[2] Srisooksai, et al. Practical data compression in wireless sensor networks: A survey. Journal of Network and Computer Applications 35.1 (2012), 37-59.
[3] M. C. Vuran, et al. “Spatio-temporal correlation: Theory and applications for wireless sensor networks,” Comput. Netw., vol. 45, no. 3, pp. 245–259, 2004.
[4] Shakya, Rajeev K., et al. "Generic correlation model for wireless sensor network applications." IET Wireless Sensor Systems 3.4 (2013): 266-276.
[5] Liu, Chong, et al. "An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation" IEEE Trans. on parallel and distributed systems 18.7 (2007).
[6] Gupta, Himanshu, et al. "Efficient gathering of correlated data in sensor networks." ACM Tran. on Sensor Networks (TOSN)4.1 (2008)
[7] Ma, Yajie, et al. "Distributed clustering-based aggregation algorithm for spatial correlated sensor networks." IEEE Sensors Journal 11.3 (2011): 641-648.
[8] Yuan, Fei, et al. "Data density correlation degree clustering method for data aggregation in WSN." IEEE Sensors Journal 14.4 (2014): 1089-1098.
[9] Pattem, et al. "The impact of spatial correlation on routing with compression in wireless sensor networks." ACM Trans. on Sensor Networks (TOSN) 4.4 (2008): 24.
[10] Dai, Rui, and et al. "A spatial correlation model for visual information in wireless multimedia sensor networks." IEEE Trans. on Multi. 11.6 (2009): 1148-1159.
[11] Wang, Fan, et al. "Energy-efficient clustering using correlation and random update based on data change rate for wireless sensor networks." IEEE Sensors Journal (2016): 1-1.
[12] D. Maeda, et al. Efficient Clustering Scheme Considering Non-uniform Correlation Distribution for Ubiquitous Sensor Networks. IEICE Trans. on Fund. of Electronics, Comm. and Computer Sciences E90-A (7), (2007), 1344-1352.
[13] N. T. T. Nga, et al. Correlation-based Clustering for Energy Saving in Wireless Sensor Network. Journal of Science and Technology Technical Universities, No. 115 (2016), pp. 51-57
[14] N. T. T. Nga, et al. Entropy-based Correlation Clustering for Wireless Sensor Networks in Multi-Correlated Regional Environments. IEIE Trans. on Smart Proc. and Comput., vol. 5, no. 2, (2016), 85-93.
[15] N. T. T. Nga, et al. Entropy correlation and its impact on routing with compression in wireless sensor network. SoICT 2016: 235-242
[16] Thomas M. Cover et al. Elements of Information Theory. Copyright@1991 John Wiley & Sons, Inc. Chapter 2 pp. 13-49.
[17] A.K. Jain, et al. Data Clustering: A Review. ACM Computing Surveys, Vol.31, No.3, (Sept. 1999).
[18] Pradhan, et al. Distributed source coding using syndromes (DISCUS): Design and construction. IEEE Trans. on Information Theory 49.3 (2003), 626-643.
[19] Intanagonwiwat, et al. Impact of network density on data aggregation in wireless sensor networks. Distributed Computing Systems, 2002. Proceedings. 22nd International Conference on. IEEE.
[20] Anna Scaglione et al. On the interdependence of routing and data compression in multi-hop sensor networks. MobiCom '02. ACM, New York, NY, USA.
[2] Srisooksai, et al. Practical data compression in wireless sensor networks: A survey. Journal of Network and Computer Applications 35.1 (2012), 37-59.
[3] M. C. Vuran, et al. “Spatio-temporal correlation: Theory and applications for wireless sensor networks,” Comput. Netw., vol. 45, no. 3, pp. 245–259, 2004.
[4] Shakya, Rajeev K., et al. "Generic correlation model for wireless sensor network applications." IET Wireless Sensor Systems 3.4 (2013): 266-276.
[5] Liu, Chong, et al. "An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation" IEEE Trans. on parallel and distributed systems 18.7 (2007).
[6] Gupta, Himanshu, et al. "Efficient gathering of correlated data in sensor networks." ACM Tran. on Sensor Networks (TOSN)4.1 (2008)
[7] Ma, Yajie, et al. "Distributed clustering-based aggregation algorithm for spatial correlated sensor networks." IEEE Sensors Journal 11.3 (2011): 641-648.
[8] Yuan, Fei, et al. "Data density correlation degree clustering method for data aggregation in WSN." IEEE Sensors Journal 14.4 (2014): 1089-1098.
[9] Pattem, et al. "The impact of spatial correlation on routing with compression in wireless sensor networks." ACM Trans. on Sensor Networks (TOSN) 4.4 (2008): 24.
[10] Dai, Rui, and et al. "A spatial correlation model for visual information in wireless multimedia sensor networks." IEEE Trans. on Multi. 11.6 (2009): 1148-1159.
[11] Wang, Fan, et al. "Energy-efficient clustering using correlation and random update based on data change rate for wireless sensor networks." IEEE Sensors Journal (2016): 1-1.
[12] D. Maeda, et al. Efficient Clustering Scheme Considering Non-uniform Correlation Distribution for Ubiquitous Sensor Networks. IEICE Trans. on Fund. of Electronics, Comm. and Computer Sciences E90-A (7), (2007), 1344-1352.
[13] N. T. T. Nga, et al. Correlation-based Clustering for Energy Saving in Wireless Sensor Network. Journal of Science and Technology Technical Universities, No. 115 (2016), pp. 51-57
[14] N. T. T. Nga, et al. Entropy-based Correlation Clustering for Wireless Sensor Networks in Multi-Correlated Regional Environments. IEIE Trans. on Smart Proc. and Comput., vol. 5, no. 2, (2016), 85-93.
[15] N. T. T. Nga, et al. Entropy correlation and its impact on routing with compression in wireless sensor network. SoICT 2016: 235-242
[16] Thomas M. Cover et al. Elements of Information Theory. Copyright@1991 John Wiley & Sons, Inc. Chapter 2 pp. 13-49.
[17] A.K. Jain, et al. Data Clustering: A Review. ACM Computing Surveys, Vol.31, No.3, (Sept. 1999).
[18] Pradhan, et al. Distributed source coding using syndromes (DISCUS): Design and construction. IEEE Trans. on Information Theory 49.3 (2003), 626-643.
[19] Intanagonwiwat, et al. Impact of network density on data aggregation in wireless sensor networks. Distributed Computing Systems, 2002. Proceedings. 22nd International Conference on. IEEE.
[20] Anna Scaglione et al. On the interdependence of routing and data compression in multi-hop sensor networks. MobiCom '02. ACM, New York, NY, USA.