Entropy Correlation and Its Impacts on Data Aggregation in a Wireless Sensor Network

A correlation characteristic has significant potential advantages for the development of efficient communication protocols in wireless sensor networks (WSNs). To exploit the correlation in WSNs, the correlation model is required. However, most of the present correlation models are linear and distance-dependent. This paper proposes a general distance-independent entropy correlation model based on the relation between joint entropy and the number of members in a group. This relation is estimated using entropy of individual members and entropy correlation coefficients of member pairs. The proposed model is then applied to evaluate two data aggregation schemes in WSNs including data compression and representative schemes. In the data compression scheme, some main routing strategies are compared and evaluated to find the most appropriate strategy. In the representative scheme, with the desired distortion requirement, a method to calculate the number of representative nodes and the selection of these nodes are proposed. The practical validations showed the effectiveness of the proposed correlation model and data reduction schemes.

[1]  Huazhong Yang,et al.  Energy-efficient spatially-adaptive clustering and routing in wireless sensor networks , 2009, 2009 Design, Automation & Test in Europe Conference & Exhibition.

[2]  Fan Wang,et al.  Energy-Efficient Clustering Using Correlation and Random Update Based on Data Change Rate for Wireless Sensor Networks , 2016, IEEE Sensors Journal.

[3]  Moustafa Ghanem,et al.  Distributed Clustering-Based Aggregation Algorithm for Spatial Correlated Sensor Networks , 2011, IEEE Sensors Journal.

[4]  Carey L. Williamson,et al.  Cluster-Based Correlated Data Gathering in Wireless Sensor Networks , 2010, 2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems.

[5]  Dongming Lu,et al.  Distributed Spatial Correlation-based Clustering for Approximate Data Collection in WSNs , 2013, 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA).

[6]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[7]  Vishnu Navda,et al.  Efficient gathering of correlated data in sensor networks , 2008, TOSN.

[8]  Kannan Ramchandran,et al.  Distributed source coding using syndromes (DISCUS): design and construction , 2003, IEEE Trans. Inf. Theory.

[9]  Roger Wattenhofer,et al.  Gathering correlated data in sensor networks , 2004, DIALM-POMC '04.

[10]  Nishchal K. Verma,et al.  Generic correlation model for wireless sensor network applications , 2013, IET Wirel. Sens. Syst..

[11]  M. C. Vuran,et al.  On the Interdependence of Congestion and Contention in Wireless Sensor Networks , 2005 .

[12]  Ian F. Akyildiz,et al.  A Spatial Correlation Model for Visual Information in Wireless Multimedia Sensor Networks , 2009, IEEE Transactions on Multimedia.

[13]  Ramesh Govindan,et al.  The impact of spatial correlation on routing with compression in wireless sensor networks , 2008, TOSN.

[14]  Wu-chi Feng,et al.  RIDA: A Robust Information-Driven Data Compression Architecture for Irregular Wireless Sensor Networks , 2007, EWSN.

[15]  Simon A. Dobson,et al.  Compression in wireless sensor networks , 2013 .

[16]  Deborah Estrin,et al.  Impact of network density on data aggregation in wireless sensor networks , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[17]  Deborah Estrin,et al.  The impact of data aggregation in wireless sensor networks , 2002, Proceedings 22nd International Conference on Distributed Computing Systems Workshops.

[18]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[19]  Zygmunt J. Haas,et al.  Encoded Sensing for Energy Efficient Wireless Sensor Networks , 2018, IEEE Sensors Journal.

[20]  Matteo Gaeta,et al.  Multisignal 1-D compression by F-transform for wireless sensor networks applications , 2015, Appl. Soft Comput..

[21]  Jie Wu,et al.  Dependable Structural Health Monitoring Using Wireless Sensor Networks , 2015, IEEE Transactions on Dependable and Secure Computing.

[22]  Tossaporn Srisooksai,et al.  Practical data compression in wireless sensor networks: A survey , 2012, J. Netw. Comput. Appl..

[23]  Jian Pei,et al.  An Energy-Efficient Data Collection Framework for Wireless Sensor Networks by Exploiting Spatiotemporal Correlation , 2007, IEEE Transactions on Parallel and Distributed Systems.

[24]  Nathan D. Cahill,et al.  Normalized Measures of Mutual Information with General Definitions of Entropy for Multimodal Image Registration , 2010, WBIR.

[25]  Son Ngo Hong,et al.  Entropy-based Correlation Clustering for Wireless Sensor Networks in Multi-Correlated Regional Environments , 2016 .

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

[27]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[28]  Özgür B. Akan,et al.  Spatio-temporal correlation: theory and applications for wireless sensor networks , 2004, Comput. Networks.

[29]  Fei Yuan,et al.  Data Density Correlation Degree Clustering Method for Data Aggregation in WSN , 2014, IEEE Sensors Journal.

[30]  Son Ngo Hong,et al.  Entropy correlation and its impact on routing with compression in wireless sensor network , 2016, SoICT.