Suppression and failures in sensor networks: A Bayesian approach

33rd International Conference on Very Large Data Bases, VLDB 2007 - Conference Proceedings

Volumn , Issue , 2007, Pages 842-853

  Silberstein, Adam ^a   Puggioni, Gavino ^b   Gelfand, Alan ^b   Munagala, Kamesh ^a   Yang, Jun ^a  

^a Duke University   (United States)

^b DUKE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ACQUISITION; ECONOMIC AND SOCIAL EFFECTS; INFERENCE ENGINES; REDUNDANCY; SENSOR NETWORKS;

APPLICATION LEVEL; BAYESIAN APPROACHES; BAYESIAN INFERENCE; CONTINUOUS DATA COLLECTIONS; EXPERIMENTAL EVALUATION; NETWORK INFERENCE; PROCESS MODELING; REDUNDANCY SCHEME;

BAYESIAN NETWORKS;

EID: 85011015549     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. R project for statistical computing. http://www.r-project.org.
2. TinyOS. http://www.tinyos.net.
3. C. C. Aggarwal and S. Parthasarathy. Mining massively incomplete data sets by conceptual reconstruction. In Proceedings of the 2001 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 227-232, San Francisco, California, USA, Aug. 2001.
4. P. Agrawal, O. Benjelloun, A. D. Sarma, C. Hayworth, S. Nabar, T. Sugihara, and J. Widom. Trio: A system for data, uncertainty, and lineage. In Proc. of the 2006 Intl. Conf. on Very Large Data Bases, Seoul, South Korea, Sept. 2006.
5. D. Chu, A. Deshpande, J. Hellerstein, and W. Hong. Approximate data collection in sensor networks using probabilistic models. In Proc. of the 2006 Intl. Conf. on Data Engineering, Atlanta, Georgia, USA, Apr. 2006.
6. J. Considine, F. Li, G. Kollios, and J. Byers. Approximation aggregation techniques for sensor databases. In Proc. of the 2004 Intl. Conf. on Data Engineering, Boston, Massachusetts, USA, Mar. 2004.
7. Crossbow Inc. MPR-Mote Processor Radio Board User's Manual, 2003.
8. Crossbow Inc. Smart Dust Application Note, 2004.
9. N. N. Dalvi and D. Suciu. Efficient query evaluation on probabilistic databases. In Proceedings of the 2004 International Conference on Very Large Data Bases, pages 864-875, Toronto, Canada, Sept. 2004.
10. A. Deshpande, C. Guestrin, and S. Madden. Using probabilistic models for data management in acquisitional environments. In Proc. of the 2005 Conf. on Innovative Data Systems Research, Asilomar, California, USA, Jan. 2005.
11. A. Deshpande, C. Guestrin, S. Madden, J. Hellerstein, and W. Hong. Model-driven data acquisition in sensor networks. In Proc. of the 2004 Intl. Conf. on Very Large Data Bases, Toronto, Canada, Aug. 2004.
12. A. Deshpande and S. Madden. MauveDB: Supporting modelbased user views in database systems. In Proc. of the 2006 ACM SIGMOD Intl. Conf. on Management of Data, Chicago, Illinois, USA, June 2006.
13. M. Franklin, S. Jeffery, S. Krishnamurthy, F. Reiss, S. Rizvi, E. Wu, O. Cooper, A. Edakkunni, and W. Hong. Design considerations for high fan-in systems: The HiFi approach. In Proc. of the 2005 Conf. on Innovative Data Systems Research, Asilomar, California, USA, Jan. 2005.
14. A. Gelfand, S. Banerjee, and D. Gamerman. Spatial Process Modelling for Univariate and Multivariate Dynamic Spatial Data. Environmentrics, 16:465-479, 2005.
15. A. Gelfand and A. Smith. Sampling Based Approaches to Calculating Marginal Densities. Journal Amer. Stat. Assoc., pages 398-409, 1990.
16. A. Gelman, J. Carlin, H. Stern, and D. Rubin. Bayesian Data Analysis. Chapman and Hall/CRC, 2003.
17. B. Hull, K. Jamieson, and H. Balakrishnan. Mitigating congestion in wireless sensor networks. In Proc. of the 2004 SEN-SYS, Baltimore, Maryland, USA, Nov. 2004.
18. A. Jain, E. Chang, and Y. Wang. Adaptive stream resource management using Kalman filters. In Proc. of the 2004 ACM SIGMOD Intl. Conf. on Management of Data, Paris, France, June 2004.
19. S. Jeffery, G. Alonso, M. Franklin, W. Hong, and J. Widom. Declarative support for sensor data cleaning. In Proc. of the 2006 Intl. Conference on Pervasive Computing, Dublin, Ireland, May 2006.
20. N. Khoussainova, M. Balazinska, and D. Suciu. Towards correcting input data errors probabilistically using integrity constraints. In Proc. of the 2006 ACM Workshop on Data Engineering for Wireless and Mobile Access, Chicago, Illinois, USA, June 2006.
21. A. Manjhi, S. Nath, and P. Gibbons. Tributaries and deltas: Efficient and robust aggregation in sensor network streams. In Proc. of the 2005 ACM SIGMOD Intl. Conf. on Management of Data, Baltimore, Maryland, USA, June 2005.
22. S. Nath, P. Gibbons, S. Seshan, and Z. Anderson. Synopsis diffusion for robust aggregation in sensor networks. In Proc. of the 2004 SENSYS, Baltimore, Maryland, USA, Nov. 2004.
23. C. Olston, J. Jiang, and J. Widom. Adaptive filters for continuous queries over distributed data streams. In Proc. of the 2003 ACM SIGMOD Intl. Conf. on Management of Data, San Diego, California, USA, June 2003.
24. C. Olston, B. Loo, and J. Widom. Adaptive precision setting for cached approximate values. In Proc. of the 2001 ACM SIGMOD Intl. Conf. on Management of Data, Santa Barbara, California, USA, May 2001.
25. J. Polastre, J. Hill, and D. Culler. Versatile low power media access for wireless sensor networks. In Proc. of the 2004 SENSYS, Baltimore, Maryland, USA, Nov. 2004.
26. G. Rodriguez-Yam, R. Davis, and L. Scharf. Efficient Gibbs sampling of truncated multivariate normal with application to constrained linear regression. Technical report, Colorado State University, 2004.
27. A. Silberstein, R. Braynard, and J. Yang. Constraint-chaining: On energy-efficient continuous monitoring in sensor networks. In Proc. of the 2006 ACM SIGMOD Intl. Conf. on Management of Data, Chicago, Illinois, USA, June 2006.
28. D. Tulone and S. Madden. PAQ: Time series forecasting for approximate query answering in sensor networks. In Proc. of the 2006 EWSN, Zurich, Switzerland, Feb. 2006.
29. M. West and P. Harrison. Bayesian Forecasting and Dynamic Models. Springer, 1997.
30. A. Woo, T. Tong, and D. Culler. Taming the underlying challenges of reliable multihop routing in sensor networks. In Proc. of the 2003 SENSYS, Los Angeles, California, USA, Nov. 2003.
31. J. Zhao and R. Govindan. Understanding packet delivery performance in dense wireless sensor networks. In Proc. of the 2003 SENSYS, Los Angeles, California, USA, Nov. 2003.