Learning compressive sensing models for big spatio-temporal data

SIAM International Conference on Data Mining 2015, SDM 2015

Volumn , Issue , 2015, Pages 667-675

  Lee, Dongeun ^a   Choi, Jaesik ^a  

^a Ulsan National Institute of Science and Technology   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

BIOSENSORS; DATA MINING; SIGNAL RECONSTRUCTION; SPACE DIVISION MULTIPLE ACCESS;

BIOMEDICAL SENSORS; COMPRESSIVE SENSING; ENCODING PROCESS; GAMMA DISTRIBUTION; NUMBER OF PRINCIPAL COMPONENTS; RESOURCE CONSUMPTION; SENSING DEVICES; SPATIO-TEMPORAL DATA;

COMPRESSED SENSING;

EID: 84961912312     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611974010.75     Document Type: Conference Paper

References (17)

1. Sensorscope: Sensor networks for environmental monitoring. http://lcav.epfl.ch/op/eclit/sensorscope-en
2. W. Bajwa, J. Haupt, A. Sayeed. and R. Nowak, Compressive wireless sensing, in Proc. Int'l Conf. Inf. Process. Sens. Netw. (IPSN '06), 2006, pp. 134-142.
3. R. G. Baraniuk, Compressive sensing [lecture notes], IEEE Signal Process. Mag., 24 (2007), pp. 118-121.
4. R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, Model-based compressive sensing, IEEE Trans. Inf. Theory, 56 (2010), pp. 1982-2001.
5. D. Baron, M. F. Duarte, M. B. Wakin, S. Sarvotham, and R. G. Baraniuk, Distributed compressive sensing, arXiv preprint arXiv:0901.3403, 2009.
6. J. A. Burke, D. Estrin, M. Hansen, A. Parker, N. Ra-manathan, S. Reddy, and M. B. Srivastava, Participa-tory sensing, Center for Embedded Network Sensing, 2006.
7. E. J. Candes and M. B. Wakin, An introduction to compressive sampling, IEEE Signal Process. Mag., 25 (2008), pp. 21-30.
8. T. M. Cover and J. A. Thomas, Elements of Information Theory, John Wiley & Sons, 2nd ed., 2006.
9. M. F. Duarte, M. B. Wakin, D. Baron, and R. G. Baraniuk, Universal distributed sensing via random projections, in Proc. Int'l Conf. Inf. Process. Sens. Netw. (IPSN '06), 2006, pp. 177-185.
10. S. Foucart and H. Rauhut, A Mathematical Introduction to Compressive Sensing, Springer, 2013.
11. D. Lee and J. Choi, Low complexity sensing for big spatio-temporal data, in Proc. Int'l Conf. Big Data (BigData '14), 2014, pp. 323-328.
12. C. Luo, F. Wu, J. Sun, and C. W. Chen, Compressive data gathering for large-scale wireless sensor networks, in Proc. MobiCom '09, 2009, pp. 145-156.
13. D. Noh, D. Lee, and H. Shin, QoS-aware geographic routing for solar-powered wireless sensor networks, IE-ICE Trans. Commun., 90 (2007), pp. 3373-3382.
14. G. Quer, R. Masiero, D. Munaretto, M. Rossi, J. Wid-mer, and M. Zorzi, On the interplay between routing and signal representation for compressive sensing in wireless sensor networks, in Proc. Inf. Theory Appl. Workshop (ITA '09), 2009, pp. 206-215.
15. G. Quer, R. Masiero, G. Pillonetto, M. Rossi, and M. Zorzi, Sensing, compression, and recovery for WSNs: Sparse signal modeling and monitoring framework, IEEE Trans. Wireless Commun., 11 (2012), pp. 3447-3461.
16. K. Sayood, Introduction to Data Compression, Morgan Kaufmann, 4th ed., 2012.
17. M. C. Vuran, Ö. B. Akan, and I. F. Akyildiz, Spatio-temporal correlation: Theory and applications for wireless sensor networks, Comput. Netw., 45 (2004), pp. 245-259.