Share This Article:

Modeling of Data Reduction in Wireless Sensor Networks

Abstract Full-Text HTML Download Download as PDF (Size:406KB) PP. 283-294
DOI: 10.4236/wsn.2011.38029    4,817 Downloads   8,649 Views   Citations

ABSTRACT

In this paper, we present a stochastic model for data in a Wireless Sensor Network (WSN) using random field theory. The model captures the space-time behavior of the underlying phenomenon being observed by the network. We present results regarding the size and spatial distribution of the regions of the network that sense statistically extreme values of the underlying phenomenon using the theory of extreme excursion regions. These results compliment many existing works in the literature that describe algorithms to reduce the data load, but lack an analytical approach to evaluate the size and spatial distribution of this load. We show that if only the statistically extreme data is transmitted in the network, then the data load can be significantly reduced. Finally, a simple performance model of a WSN is developed based on a collection of asynchronous M/M/1 servers that work in parallel. We derive several performance measures from this performance model. The presented results will be useful in the design of large scale sensor networks.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

G. Patterson and M. Mehmet-Ali, "Modeling of Data Reduction in Wireless Sensor Networks," Wireless Sensor Network, Vol. 3 No. 8, 2011, pp. 283-294. doi: 10.4236/wsn.2011.38029.

References

[1] X. Meng, T. Nandagopal, et. al. “Contour Maps: Monitoring and Diagnosis in Sensor Networks,” Computer Networks: The International Journal of Computer and Telecommunication Networking (ACM), Vol. 50, issue 15, 2006, pp. 2820-2838.
[2] S. R. Madden, M. J. Franklin, J. M. Hellerstein, W. Hong, “TAG: a Tiny Aggregation Service for Ad-Hoc Sensor Networks”, Proceedings of OSDI, Boston, MA, June 2002.
[3] S. Yoon, C. Shahabi, “The Clustered AGgregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks,” ACM Transactions on Sensor Networks, Vol. 3, issue 1, article No. 3, Mar. 2007.
[4] S. Lindsey, K. M. Sivalingam, “Data Gathering Algorithms in Sensor Networks Using Energy Metrics,” IEEE Transactions on Parallel and Distributed Systems. Vol. 13, No. 9, 2002, pp. 924-934.
[5] E. Vanmarcke, “Random Fields: Analysis and Synthesis,” MIT Press, 1983.
[6] R. Adler, “Random Fields,” Wiley Encyclopedia of Statistical Sciences, 2006. http://www.mrw.interscience.wiley.com/emrw/9780471667193/ess/article/ess2164/current/html
[7] P. Abrahamsen, “A Review of Gaussian Random Fields and Correlation Functions,” 2nd Edition, Norwegian Computing Center, April 1997. http://www.math.ntnu.no/~omre/TMA4250/V2007/abrahamsen2.ps.
[8] H. Karl, A. Willig, “Protocols and architectures for wireless sensor networks,” Wiley, 2005.
[9] M. Vuran, O. Akan, I. Akyildiz, “Spatio-Temporal Correlation: Theory and Applications for Wireless Sensor Networks,” Computer Networks J (Elsevier), Vol. 45, No. 3, 2004, pp. 245-259.
[10] M. Stein, “Space-Time Covariance Functions,” Journal of American Statistical Association. Vol. 100, 2005, pp. 310-321.
[11] G.A. Fenton, “A Random Field Excursion Model of Salt-Induced Concrete Delamination”, Journal of Research of the National Institute of Standards and Technology, Vol. 99, No. 4, 1994, pp. 475-483.

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.