Optimizing quality-of-information in cost-sensitive sensor data fusion

2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11

Volumn , Issue , 2011, Pages

  Wang, Dong ^a   Ahmadi, Hossein ^a   Abdelzaher, Tarek ^a   Chenji, Harsha ^b   Stoleru, Radu ^b   Aggarwal, Charu C ^c  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

^c IBM RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COST CONSTRAINTS; COST SAVING; COST-SENSITIVE; DATA FUSION APPLICATIONS; DATA-FUSION SYSTEMS; NETWORK OPERATIONS; NODE LOCALIZATION; NON-TRIVIAL; PHYSICAL WORLD; PREDICTION ERRORS; PREDICTION PROBLEM; QUALITY OF INFORMATION; SENSOR NETWORK APPLICATIONS;

COSTS; DISTRIBUTED COMPUTER SYSTEMS; FORECASTING; INFORMATION FUSION; OPTIMIZATION; SENSOR NETWORKS; SENSOR NODES; SENSORS; TARGET TRACKING; TELECOMMUNICATION EQUIPMENT;

SENSOR DATA FUSION;

EID: 80052477877     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DCOSS.2011.5982175     Document Type: Conference Paper

References (40)

1. H. Ahmadi and T. Abdelzaher. An adaptive-reliability cyber-physical transport protocol for spatio-temporal data. In Proc. 30th IEEE International Real-Time Systems Symposium (RTSS'09), pages 238-247, 2009.
2. H. Ahmadi, T. Abdelzaher, and I. Gupta. Congestion control for spatiotemporal data in cyber-physical systems. In Proc. 1st International Conference on Cyber-Physical Systems (ICCPS'10), pages 89-98, 2010.
3. H. Ahmadi, T. Abdelzaher, J. Han, N. Pham, and R. Ganti. The sparse regression cube: A reliable modeling technique for open cyber-physical systems. In Proc. 2nd International Conference on Cyber-Physical Systems (ICCPS'11), 2011.
4. A.J. Dobson. An Introduction to Generalized Linear Models (2nd ed.). Chapman and Hall, 2001.
5. D. Blatt and A. Hero. Distributed maximum likelihood estimation for sensor networks. In Proc. International Conference on Acoustics, Speech, and Signal Processing, 2004.
6. L. Breima, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth International Group, 1984.
7. L. A. Breslow and D. W. Aha. Simplifying decision trees: A survey. Knowledge Engineering Review, 12(1):1 - 40, 1997.
8. B. Chen, L. Chen, Y. Lin, and R. Ramakrishnan. Prediction cubes. In Proc. VLDB'05, pages 982-993, 2005.
9. M. R. Chmielewski and J. W. Grzymala-busse. Global discretization of continuous attributes as preprocessing for machine learning. In International Journal of Approximate Reasoning, pages 294-301, 1996.
10. V. Delouille, R. Neelamani, and R. Baraniuk. Robust distributed estimation in sensor networks using the embedded polygons algorithm. In Proc. IPSN'04, pages 405 - 413, 2004.
11. R. Duda, P. Hart, and D. Stork. Pattern Classification (2nd. ed.). John Wiley and Sons, 2001.
12. Y. C. et al. Regression cubes with lossless compression and aggregation. In IEEE Trans. on Knowl. and Data Eng.18(12):1585-1599, 2006.
13. U. M. Fayyad and K. B. Irani. Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In Proc. 13th International Joint Conference on Uncertainly in Artificial Intelligence(IJCAI'93), pages 1022-1029, 1993.
14. R. K. Ganti, N. Pham, H. Ahmadi, S. Nangia, and T. F. Abdelzaher. GreenGPS: a participatory sensing fuel-efficient maps application. In Proc. MobiSys'10, pages 151-164, 2010.
15. R. Goetschalckx, K. Driessens, and S. Sanner. Cost-sensitive parsimonious linear regression. In Proc. 8th IEEE International Conference on Data Mining (ICDM'08), pages 809-814, 2008.
16. I. Guyon and A. Elisseeff. An introduction to variable and feature selection. J. Mach. Learn. Res., 3:1157-1182, 2003.
17. C. Heij, A. Ran, and F. van Schagen. Introduction to Mathematical Systems Theory: Linear Systems, Identification and Control. Birkhuser Base, 2007.
18. J. Han and M. Kamber. Data Mining: Concepts and Techniques, Second Edition. Morgan Kaufman, 2006.
19. S. Ji and L. Carin. Cost-sensitive feature acquisition and classification. Pattern Recogn., 40:1474-1485, May 2007.
20. L. Jiang et al. SenSearch: GPS and witness assisted tracking for delay tolerant sensor networks. In Proc. 8th International Conference Ad-Hoc, Mobile and Wireless Networks, pages 255 - 269, 2009.
21. N. B. John, J. Heidemann, and D. Estrin. GPS-less low cost outdoor localization for very small devices. IEEE Personal Communications Magazine, 7:28-34, 2000.
22. J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
23. I. Kononenko and S. J. Hong. Attribute selection for modelling. Future Generation Computer Systems, 13(2-3):181 - 195, 1997.
24. M. Kunter, C. Nachtsheim, J. Neter, and W. Li. Applied Linear Statistical Models. McGraw-Hill, 2005.
25. P. Melville, M. Saar-Tsechansky, F. Provost, and R. Mooney. Active feature-value acquisition for classifier induction. In Proc. 4th IEEE International Conference on Data Mining (ICDM'04), pages 483 - 486, 2004.
26. P. Melville, M. Saar-Tsechansky, F. Provost, and R. Mooney. An expected utility approach to active feature-value acquisition. In Proc. 5th IEEE International Conference on Data Mining (ICDM'05), page 4 pp., Nov. 2005.
27. D. Niculescu and B. Nath. Dv based positioning in ad hoc networks. Telecommunication Systems - Modeling, Analysis, Design and Management, 22(1-4):267 - 280, 2003.
28. J. B. Predd, S. R. Kulkarni, and H. V. Poor. Regression in sensor networks: Training distributively with alternating projections. In CoRR, vol. abs/cs/0507039, 2005.
29. M. Rabbat and R. Nowak. Distributed optimization in sensor networks. In Proc. IPSN'04, pages 20-27, 2004.
30. N. Roy and A. Mccallum. Toward optimal active learning through sampling estimation of error reduction. In In Proc. 18th International Conf. on Machine Learning, pages 441-448, 2001.
31. M. Saar-Tsechansky, P. Melville, and F. Provost. Active feature-value acquisition. Manage. Sci., 55:664-684, April 2009.
32. S. Santini and K. Roemer. An adaptive strategy for quality-based data reduction in wireless sensor networks. In Proc. 3rd International Conference on Networked Sensing Systems (INSS'06), 2006.
33. V. S. Sheng and C. X. Ling. Partial example acquisition in cost-sensitive learning. In Proc. 13th international conference on Knowledge Discovery and Data Mining (KDD'07), pages 638-646, 2007.
34. Y.-S. Shih. Families of splitting criteria for classification trees. Statistics and Computing, 9(4):309 - 315, 1999.
35. R. Stoleru, T. He, J. A. Stankovic, and D. Luebke. A high-accuracy, low-cost localization system for wireless sensor networks. In Proc. SenSys'05, pages 13-26, 2005.
36. R. Stoleru, J. Stankovic, and S. Son. On composability of localization protocols for wireless sensor networks. IEEE Network, 22(4):21 - 25, 2008.
37. M. Tan. Csl: a cost-sensitive learning system for sensing and grasping objects. In Proc. 1990 IEEE International Conference on Robotics and Automation, pages 858-863 vol.2, 1990.
38. P. Tan, M. Steinbach, and V. Kumar, editors. Introduction to Data Mining. Addison-Wesley, 2005.
39. D. Tulone and S. Madden. PAQ: time series forecasting for approximate query answering in sensor networks. In Proc. 3rd European Workshop on Wireless Sensor Networks (EWSN'06), 2006.
40. P. D. Turney. Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Artificial Intelligence Research, pages 369-409, 1995.