Modeling spatially-correlated data of sensor networks with irregular topologies

The physical phenomena monitored by sensor net- works, e.g. forest temperature, usually yield sensed data that are strongly correlated in space. We have recently introduced a mathematical model for such data, and used it to generate synthetic traces and study the performance of algorithms whose behavior depends on this spatial correlation (1). That work studied sensor networks with grid topologies. This work extends our modeling methodology to sensor networks with irregular topologies. We describe a rigorous mathematical procedure and a simple practical method to extract the model parameters from real traces. We also show how to efficiently generate synthetic traces that correspond to sensor networks with arbitrary topologies using the proposed model. The correctness of the model is verified by statistically com- paring synthetic and real data. Further, the model is validated by comparing the performance of algorithms whose behavior depends on the degree of spatial correlation in data, under real and synthetic traces. The real traces are obtained from both publically available sensor data, and sensor networks that we deploy. Finally, we augment our existing trace-generation tool with new functionality suited for sensor networks with irregular topologies.

[1]  Baltasar Beferull-Lozano,et al.  On network correlated data gathering , 2004, IEEE INFOCOM 2004.

[2]  Deborah Estrin,et al.  Using more realistic data models to evaluate sensor network data processing algorithms , 2004, 29th Annual IEEE International Conference on Local Computer Networks.

[3]  David E. Culler,et al.  Calibration as parameter estimation in sensor networks , 2002, WSNA '02.

[4]  Martin Vetterli,et al.  On the optimal density for real-time data gathering of spatio-temporal processes in sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[5]  Ricardo A. Olea,et al.  Geostatistics for Engineers and Earth Scientists , 1999, Technometrics.

[6]  Deborah Estrin,et al.  An evaluation of multi-resolution storage for sensor networks , 2003, SenSys '03.

[7]  Gaurav S. Sukhatme,et al.  Adaptive sampling for environmental robotics , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

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

[9]  Deborah Estrin,et al.  Synthetic Data Generation to Support Irregular Sampling in Sensor Networks , 2004 .

[10]  Jeongyeup Paek,et al.  A wireless sensor network for structural health monitoring: performance and experience , 2005, The Second IEEE Workshop on Embedded Networked Sensors, 2005. EmNetS-II..

[11]  D. Jain.  Linear integral equations: theory and technique : Ram P. Kanwal. 296 pages, 11 diagrams. New York, Academic Press, 1971. Price $16.50 (approx. £6.50). , 1974 .

[12]  Brian D. Ripley,et al.  Modern applied statistics with S, 4th Edition , 2002, Statistics and computing.

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

[14]  Ian F. Akyildiz,et al.  Spatial correlation-based collaborative medium access control in wireless sensor networks , 2006, IEEE/ACM Transactions on Networking.

[15]  Ahmed Helmy,et al.  RUGGED: RoUting on finGerprint Gradients in sEnsor Networks , 2004, The IEEE/ACS International Conference on Pervasive Services.

[16]  P. Kythe,et al.  Computational Methods for Linear Integral Equations , 2002 .

[17]  Deborah Estrin,et al.  Dimensions: why do we need a new data handling architecture for sensor networks? , 2003, CCRV.

[18]  Mingyan Liu,et al.  On the Many-to-One Transport Capacity of a Dense Wireless Sensor Network and the Compressibility of Its Data , 2003, IPSN.

[19]  Deborah Estrin,et al.  Simultaneous Optimization for Concave Costs: Single Sink Aggregation or Single Source Buy-at-Bulk , 2003, SODA '03.

[20]  R. Olea.  Geostatistics for Natural Resources Evaluation By Pierre Goovaerts, Oxford University Press, Applied Geostatistics Series, 1997, 483 p., hardcover, $65 (U.S.), ISBN 0-19-511538-4 , 1999 .

[21]  J. Chou,et al.  Tracking and exploiting correlations in dense sensor networks , 2002, Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002..

[22]  Konstantinos Psounis,et al.  Modeling spatially-correlated sensor network data , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[23]  Ian F. Akyildiz,et al.  Spatial correlation-bas ed col-laborative medium access control in wireless sensor networks , 2004 .

[24]  D. Stirling,et al.  Integral Equations: A Practical Treatment, from Spectral Theory to Applications , 1990 .