research-article

Error estimation in wireless sensor networks

Authors:

Alejandro C. Frery,

José Alencar-Neto,

Eduardo NakamuraAuthors Info & Claims

SAC '08: Proceedings of the 2008 ACM symposium on Applied computing

Pages 1923 - 1928

https://doi.org/10.1145/1363686.1364151

Published: 16 March 2008 Publication History

Abstract

We present an analogy between the operation of a Wireless Sensor Network and the sampling and reconstruction of a signal. We measure the impact of three factors on the quality of the reconstructed data, namely, the granularity of the process under study, the spatial distribution of sensors, and the protocol for clustering and data aggregation. In order to quantify this influence, a Monte Carlo study is performed for estimating the error introduced by the observation process. The phenomenon being observed is described by a Gaussian random field with varying scale, the distribution of sensors is modeled by a new point process and two protocols are assessed: Leach and Skater. We show that Skater performs better than Leach, at the expense of using the sampled data on the clustering stage.

References

[1]

I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cyirci. Wireless sensor networks: A survey. Computer Networks, 38(4):393--422, March 2002.

Digital Library

[2]

R. M. Assunção, M. C. Neves, G. Câmara, and C. da Costa Preitas. Efficient regionalization techniques for socio-economic geographical units using minimum spanning trees. International Journal of Geographical Information Science, 20(7):797--811, 2006.

[3]

F. Aurenhammer. Voronoi diagrams: a survey of a fundamental geometric data structure. ACM Computing Surveys, 23(3):345--405, 1991.

Digital Library

[4]

A. Baddeley. Spatial point processes and their application. In W. Weil, editor, Stochastic Geometry, volume 1892 of Lecture Notes in Mathematics, pages 1--75. Springer, Belin, 2006.

[5]

A. Baddeley and R. Turner. spatstat: An R package for analyzing spatial point patterns. Journal of Statistical Software, 12(6):1--42, 2005.

[6]

K. K. Berthelsen and J. Møller. A primer on perfect simulation for spatial point processes. Bulletin of the Brazilian Mathematical Society, 33(3):351--367, 2002.

[7]

A. Brayner, A. Lopes, D. Meira, R. Vasconcelos, and R. Menezes. ADAGA - ADaptive AGgregation Algorithm for sensor networks. In XXI Brazilian Simposium on Dadabases, pages 191--205, Florianópolis, Brazil, October 2006.

[8]

J.-H. Cui, J. Kong, M. Gerla, and S. Zhou. The challenges of building scalable mobile underwater wireless sensor networks for aquatic applications. IEEE Network, 20(3):12--18, 2006.

Digital Library

[9]

T. Hara, N. Murakami, and S. Nishio. Replica allocation for correlated data items in ad hoc sensor networks. SIGMOD Record, 33(1):38--43, 2004.

Digital Library

[10]

W. B. Heinzelman, A. Chandrakasan, and H. Balakrishnan. An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communication, 1:660--670, 2002.

Digital Library

[11]

C. Intanagonwiwat, R. Govindan, and D. Estrin. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proceedings of the 6th ACM International Conference on Mobile Computing and Networking (MobiCom '00), pages 56--67, Boston, MA, USA, August 2000. ACM Press.

Digital Library

[12]

M. Minnaar and D. W. Ngwenya. Automated generation of Poisson-Voronoi tesselations in R² for NS. In IEEE Africon, volume 2, pages 1091--1097, 2004.

[13]

J. Møller and R. P. Waagepetersen. Modern statistics for spatial point processes. Technical report, Department of Mathematical Sciences, Aalborg University, 2006.

[14]

R. Müller, G. Alonso, and D. Kossmann. SwissQM: Next generation data processing in sensor networks. In Proceedings of the 3rd. Biennial Conference on Innovative Data Systems Research (CIDR '07), pages 1--9, Asilomar, CA, USA, January 2007.

[15]

E. F. Nakamura, A. A. F. Loureiro, and A. C. Frery. Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Computing Surveys, 39(3):9/1--55, 2007.

Digital Library

[16]

H. A. B. F. Oliveira, E. F. Nakamura, A. A. F. Loureiro, and A. Boukerche. Directed position estimation: A recursive localization approach for wireless sensor networks. In Proceedings of the 14th IEEE International Conference on Computer Communications and Networks (ICCCN '05), pages 557--562, San Diego, USA, October 2005.

[17]

I. A. Reis, G. Câmara, R. Assuncao, and A. M. V. Monteiro. Data-aware clustering for geosensor networks data collection. In Anais XIII Simpósio Brasileiro de Sensoriamento Remoto, pages 6059--6066, Florianópolis, SC, Brazil, 2007.

[18]

B. D. Ripley. Spatial Statistics. Wiley, New York, 1981.

[19]

K. Römer and M. Friedemann. The design space of wireless sensor networks. IEEE Wireless Communications, 11(6):54--61, December 2004.

Digital Library

[20]

M. Schlather. Simulation and analysis of random fields. R News, 1/2:18--20, 2001.

[21]

N. Shrivastava, C. Buragohain, D. Agrawal, and S. Suri. Medians and beyond: new aggregation techniques for sensor networks. In Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems (SenSys '04), pages 239--249, Baltimore, MD, USA, November 2004.

Digital Library

[22]

X. Tang and J. Xu. Extending network lifetime for precision-constrained data aggregation in wireless sensor networks. In INFOCOM 2006 (25th IEEE International Conference on Computer Communications), Barcelona, Spain, April 2006.

[23]

W. N. Venables and B. D. Ripley. Modern Applied Statistics with S. Statistics and Computing. Springer, New York, 4 edition, 2002.

Digital Library

[24]

V. Vivekanandan and V. W. Wong. Concentric anchor-beacons localization algorithm for wireless sensor networks. IEEE Transactions on Vehicular Technology, in press.

[25]

K. Wu, D. Dreef, B. Sun, and Y. Xiao. Secure data aggregation without persistent cryptographic operations in wireless sensor networks. Ad Hoc Networks, 5(1):100--111, 2007.

[26]

H.-Y. Yang, W.-C. Peng, and C.-H. Lo. Optimizing multiple in-network aggregate queries in wireless sensor networks. In Advances in Databases: Concepts, Systems and Applications, 12th International Conference on Database Systems for Advanced Application (DASFAA '07), pages 870--875, Bangkok, Thailand, April 2007.

Digital Library

[27]

O. Younis, M. Krunz, and S. Ramasubramanian. Node clustering in wireless sensor networks: Recent developments and deployment challenges. IEEE Network, 20(3):20--25, 2006.

Digital Library

Cited By

Aydin OJanikas MAssunção RLee T(2021)A quantitative comparison of regionalization methodsInternational Journal of Geographical Information Science10.1080/13658816.2021.190581935:11(2287-2315)Online publication date: 5-Apr-2021
https://doi.org/10.1080/13658816.2021.1905819
Aquino AFilho PWanner ERabelo R(2013)Sensor Stream ReductionIntelligent Sensor Networks10.1201/b14300-17(329-349)Online publication date: 28-Mar-2013
https://doi.org/10.1201/b14300-17
Furlaneto Sdos Santos AHara C(2012)An efficient data acquisition model for urban sensor networks2012 IEEE Network Operations and Management Symposium10.1109/NOMS.2012.6211889(113-120)Online publication date: Apr-2012
https://doi.org/10.1109/NOMS.2012.6211889
Show More Cited By

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Error estimation in wireless sensor networks

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Published In

cover image ACM Conferences

SAC '08: Proceedings of the 2008 ACM symposium on Applied computing

March 2008

2586 pages

ISBN:9781595937537

DOI:10.1145/1363686

Conference Chairs:
Roger L. Wainwright
University of Tulsa
,
Hisham M. Haddad
Kennesaw State University

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 16 March 2008

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SAC '08

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SAC '08: The 2008 ACM Symposium on Applied Computing

March 16 - 20, 2008

Fortaleza, Ceara, Brazil

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Aydin OJanikas MAssunção RLee T(2021)A quantitative comparison of regionalization methodsInternational Journal of Geographical Information Science10.1080/13658816.2021.190581935:11(2287-2315)Online publication date: 5-Apr-2021
https://doi.org/10.1080/13658816.2021.1905819
Aquino AFilho PWanner ERabelo R(2013)Sensor Stream ReductionIntelligent Sensor Networks10.1201/b14300-17(329-349)Online publication date: 28-Mar-2013
https://doi.org/10.1201/b14300-17
Furlaneto Sdos Santos AHara C(2012)An efficient data acquisition model for urban sensor networks2012 IEEE Network Operations and Management Symposium10.1109/NOMS.2012.6211889(113-120)Online publication date: Apr-2012
https://doi.org/10.1109/NOMS.2012.6211889
Frery ARamos HAlencar-Neto JNakamura ELoureiro A(2010)Data Driven Performance Evaluation of Wireless Sensor NetworksSensors10.3390/s10030215010:3(2150-2168)Online publication date: 16-Mar-2010
https://doi.org/10.3390/s100302150
Villas LBoukerche AAraujo RLoureiro AAbu-Ghazaleh NBononi LBensaou BSyrotiuk V(2009)A reliable and data aggregation aware routing protocol for wireless sensor networksProceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems10.1145/1641804.1641846(245-252)Online publication date: 26-Oct-2009
https://dl.acm.org/doi/10.1145/1641804.1641846
Silva Junior OAquino AMini RFigueiredo C(2009)Multivariate reduction in wireless sensor networks2009 IEEE Symposium on Computers and Communications10.1109/ISCC.2009.5202248(726-729)Online publication date: Jul-2009
https://doi.org/10.1109/ISCC.2009.5202248

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