Contour maps: Monitoring and diagnosis in sensor networks

Computer Networks

Volumn 50, Issue 15, 2006, Pages 2820-2838

  Meng, Xiaoqiao ^a   Nandagopal, Thyaga ^b   Li, Li ^b   Lu, Songwu ^a  

^a University of California   (United States)

^b LUCENT TECHNOLOGIES   (United States)

Author keywords

Contour map; Sensor network; Spatio temporal sampling

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DATA ACQUISITION; INTERPOLATION; PACKET NETWORKS; SENSORS; TELECOMMUNICATION;

CONTOUR MAP; POWER SAVINGS; SENSOR NETWORK; SPATIO-TEMPORAL SAMPLING;

CIRCUIT THEORY;

EID: 33746656283     PISSN: 13891286     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.comnet.2005.09.038     Document Type: Article

References (34)

1. Available from: .
2. Available from: .
3. S. Bandyopadhyay, E. Coyle. Spatio-temporal sampling rates and energy efficiency in wireless sensor networks, in: IEEE INFOCOM, 2004.
4. Bloom B. Space/time trade-offs in hash coding with allowable errors. CACM 13 7 (1970) 422-426
5. C. Schurgers, V. Tsiatsis, S. Ganeriwal, M. Srivastava. Topology management for sensor networks: exploiting latency and density, in: ACM MOBIHOC, 2002.
6. S. Ganeriwal, C.C. Han, M. Srivastava. Spatial average of a continuous physical process in sensor networks, in: Poster in ACM Sensys, 2003.
7. Ganesan D., Greenstein B., Perelyubskiy D., Estrin D., and Heidemann J. An evaluation of multi-resolution storage for sensor networks. Proceedings of the First International Conference on Embedded Networked Sensor Systems (2003), ACM Press 89-102
8. Ganesan D., Ratnasamy S., Wang H., and Estrin D. Coping with irregular spatio-temporal sampling in sensor networks. SIGCOMM Comput. Commun. Rev. 34 1 (2004) 125-130
9. A. Giridhar, P.R. Kumar, Data fusion over sensor networks: computing and communicating functions of measurements, J. Selected Areas Commun., in press.
10. T. He, C. Huang, B.M. Blum, J.A. Stankovic, T. Abdelzaher, Range-free localization schemes for large scale sensor networks, in: Proceedings of the 9th Annual International Conference on Mobile Computing and Networking, 2003, pp. 81-95.
11. J.M. Hellerstein, W. Hong, S. Madden, K. Stanek, Beyond average: toward sophisticated sensing with querie, in: Information Processing in Sensor Networks (IPSN), 2003, pp. 63-79.
12. C.T. Inc. Available from: .
13. L. Doherty, K.S.J. Pister, Scattered data selection for dense sensor networks, in: Information Processing in Sensor Networks (IPSN), 2004.
14. S. Madden, M. Franklin, J. Hellerstein, W. Hong, Tag: a tiny aggregation service for ad-hoc sensor networks, in: Proceedings of OSDI, 2002.
15. Madden S., Franklin M.J., Hellerstein J.M., and Hong W. Tag: a tiny aggregation service for ad-hoc sensor networks. SIGOPS Oper. Syst. Rev. 36 SI (2002) 131-146
16. ns2 network simulator. Available from: .
17. S. Papadimitriou, H. Kitagawa, P.B. Gibbons, C. Faloutsos, LOCI: fast outlier detection using the local correlation integral, in: Proceedings of the 19th International Conference on Data Engineering (ICDE), 2003, pp. 315-326.
18. Product specification. Available from: .
19. Przydatek B., Song D., and Perrig A. Sia: secure information aggregation in sensor networks. Proceedings of the first international conference on Embedded networked sensor systems (2003), ACM Press 255-265
20. rf monolithics. Available from: .
21. Vasudevan L., Ortega A., and Mitra U. Application-specific compression for time delay estimation in sensor networks. Proceedings of the first international conference on Embedded networked sensor systems (2003), ACM Press 243-254
22. Y.B. Ko, N.H. Vaidya, Location-aided routing (LAR) in mobile ad hoc networks, in: ACM MOBICOM, 1998.
23. W. Ye, J. Heidemann, D. Estrin, An energy-efficient mac protocol for wireless sensor networks, in: IEEE INFOCOM, 2002.
24. X. Yu, S. Mehrotra, N. Venkatasubramanian, W. Yang, Approximate monitoring in wireless sensor networks, submitted for publication.
25. J. Zhao, R. Govindan, D. Estrin, Residual energy scans for monitoring wireless sensor networks, in: IEEE Wireless Communications and Networking Conference (WCNC), 2002.
26. J. Zhao, R. Govindan, D. Estrin, Computing aggregates for monitoring wireless sensor networks, in: First IEEE International Workshop on Sensor Network Protocols and Applications (SNPA), 2003.
27. S. Yoon, C. Shahabi, Exploiting spatial correlation towards an energy efficient clustered aggregation technique (CAG), in: IEEE International Conference on Communications (ICC), Korea, May 2005.
28. C. Guestrin, P. Bodik, R. Thibaux, M. Paskin, S. Madden, Distributed Regression: An Efficient Framework for Modeling Sensor Network Data In Information Processing in Sensor Networks (IPSN), Berkeley, April 2004.
29. Goel S., and Imielinski T. Prediction-based monitoring in sensor networks taking lessons from MPEG. ACM Comput. Commun. Rev. 31 5 (2001) 82-98
30. L. Rossi, B. Krishnamachari, C.C.J. Kuo, Distributed parameter estimation for monitoring diffusion phenomena using physical models, in: First IEEE International Conference on Sensor and Ad hoc Communications and Networks (SECON), Santa Clara, CA, October 2004.
31. S. Pattem, B. Krishnamachari, R. Govindan, The impact of spatial correlation on routing with compression in wireless sensor networks, in: Information Processing in Sensor Networks (IPSN), Berkeley, April 2004.
32. R. Cristescu, B. Beferull-Lozano, M. Vetterli, On network correlated data gathering with explicit communication: NP-completeness and algorithms, in: INFOCOM, Hong Kong, 2004.
33. A. Jindal, K. Psounis, Modeling spatially-correlated sensor network data, in: First IEEE International Conference on Sensor and Ad hoc Communications and Networks (SECON), Santa Clara, CA, October 2004.
34. Y. Yu, D. Ganesan, L. Girod, D. Estrin, R. Govindan, Synthetic data generation to support irregular sampling in sensor networks, in: Geo Sensor Networks, Portland Maine, October 2003.