Guojin Liu
ABSTRACT
Recent years have witnessed pilot deployments of inexpensive wireless sensor networks (WSNs) for active volcano monitoring. This paper studies the problem of picking arrival times of primary waves (i.e., P-phases) received by seismic sensors, one of the most critical tasks in volcano monitoring. Two fundamental challenges must be addressed. First, it is virtually impossible to download the real-time high-frequency seismic data to a central station for P-phase picking due to limited wireless network bandwidth. Second, accurate P-phase picking is inherently computation-intensive, and is thus prohibitive for many low-power sensor platforms. To address these challenges, we propose a new P-phase picking approach for hierarchical volcano monitoring WSNs where a large number of inexpensive sensors are used to collect fine-grained, real-time seismic signals while a small number of powerful coordinator nodes process collected data and pick accurate P-phases. We develop a suite of new in-network signal processing algorithms for accurate P-phase picking, including lightweight signal pre-processing at sensors, sensor selection at coordinators as well as signal compression and reconstruction algorithms. Testbed experiments and extensive simulations based on real data collected from a volcano show that our approach achieves accurate P-phase picking while only 16% of the sensor data are transmitted.
- AlertNet. http://bit.ly/f9JhLc.Google Scholar
- Gumstix. http://www.gumstix.com.Google Scholar
- List of wireless sensor nodes. http://bit.ly/TfLEom.Google Scholar
- Power modes and energy consumption for the imote2 sensor node. http://bit.ly/THlmRz.Google Scholar
- SimIt-ARM. http://bit.ly/T44mj1.Google Scholar
- TelosB datasheet. http://bit.ly/Psjj2S.Google Scholar
- VolcanoSRI project. http://sensornet.cse.msu.edu.Google Scholar
- A. Abbasi and M. Younis. A survey on clustering algorithms for wireless sensor networks. Computer communications, 30(14):2826--2841, 2007. Google ScholarDigital Library
- R. Berinde, A. Gilbert, P. Indyk, H. Karloff, and M. Strauss. Combining geometry and combinatorics: A unified approach to sparse signal recovery. In Annu. Allerton Conf. Commun., Control, and Comput., 2008.Google ScholarCross Ref
- F. Blais and M. Rioux. Real-time numerical peak detector. Signal Processing, 11(2):145--155, 1986. Google ScholarDigital Library
- T. Blumensath and M. Davies. Iterative hard thresholding for compressed sensing. Applied and Computational Harmonic Analysis, 27(3), 2009.Google Scholar
- E. Candès and M. Wakin. An introduction to compressive sampling. IEEE Signal Process. Mag., 25(2), 2008.Google ScholarCross Ref
- Y. Chan and K. Ho. A simple and efficient estimator for hyperbolic location. IEEE Trans. Signal Process., 42(8), 1994. Google ScholarDigital Library
- E. Endo and T. Murray. Real-time seismic amplitude measurement (RSAM): a volcano monitoring and prediction tool. Bulletin of Volcanology, 53(7), 1991.Google Scholar
- O. Gnawali, B. Greenstein, K.-Y. Jang, A. Joki, J. Paek, M. Vieira, D. Estrin, R. Govindan, and E. Kohler. The TENET Architecture for Tiered Sensor Networks. In SenSys, 2006. Google ScholarDigital Library
- V. Goyal, A. Fletcher, and S. Rangan. Compressive sampling and lossy compression. IEEE Signal Process. Mag., 25(2), 2008.Google ScholarCross Ref
- B. Greenstein, C. Mar, A. Pesterev, S. Farshchi, E. Kohler, J. Judy, and D. Estrin. Capturing high-frequency phenomena using a bandwidth-limited sensor network. In SenSys, 2006. Google ScholarDigital Library
- G. Hackmann, F. Sun, N. Castaneda, C. Lu, and S. Dyke. A holistic approach to decentralized structural damage localization using wireless sensor networks. In RTSS, 2008. Google ScholarDigital Library
- A. Kiely, M. Xu, W. Song, R. Huang, and B. Shirazi. Adaptive linear filtering compression on realtime sensor networks. In PerCom, 2009. Google ScholarDigital Library
- J. Lees and R. Crosson. Tomographic inversion for three-dimensional velocity structure at mount st. helens using earthquake data. J. Geophysical Research, 94(B5), 1989.Google ScholarCross Ref
- J. M. Lees. RSEIS: Seismic time series analysis tools. http://bit.ly/Qcj60K.Google Scholar
- G. Liu, R. Tan, R. Zhou, G. Xing, W.-Z. Song, and J. M. Lees. Volcanic earthquake timing in wireless sensor networks. Technical Report MSU-CSE-12--8, CSE Dept, Michigan State University, 2012.Google Scholar
- J. Nelder and R. Mead. A simplex method for function minimization. The computer journal, 7(4), 1965.Google Scholar
- J. Polastre, R. Szewczyk, and D. Culler. Telos: enabling ultra-low power wireless research. In IPSN, 2005. Google ScholarDigital Library
- C. Sadler and M. Martonosi. Data compression algorithms for energy-constrained devices in delay tolerant networks. In SenSys, 2006. Google ScholarDigital Library
- R. Sleeman and T. van Eck. Robust automatic p-phase picking: an on-line implementation in the analysis of broadband seismogram recordings. Physics of the earth and planetary interiors, 113, 1999.Google Scholar
- W. Song, R. Huang, M. Xu, A. Ma, B. Shirazi, and R. LaHusen. Air-dropped sensor network for real-time high-fidelity volcano monitoring. In MobiSys, 2009. Google ScholarDigital Library
- R. Tan, G. Xing, J. Chen, W. Song, and R. Huang. Quality-driven volcanic earthquake detection using wireless sensor networks. In RTSS, 2010. Google ScholarDigital Library
- G. Werner-Allen, S. Dawson-Haggerty, and M. Welsh. Lance: optimizing high-resolution signal collection in wireless sensor networks. In SenSys, 2008. Google ScholarDigital Library
- G. Werner-Allen, J. Johnson, M. Ruiz, J. Lees, and M. Welsh. Monitoring volcanic eruptions with a wireless sensor network. In EWSN, 2005.Google ScholarCross Ref
- G. Werner-Allen, K. Lorincz, J. Johnson, J. Lees, and M. Welsh. Fidelity and yield in a volcano monitoring sensor network. In OSDI, 2006. Google ScholarDigital Library
- M. Withers, R. Aster, C. Young, J. Beiriger, M. Harris, S. Moore, and J. Trujillo. A comparison of select trigger algorithms for automated global seismic phase and event detection. Bulletin of the Seismological Society of America, 88(1), 1998.Google Scholar
Index Terms
- Volcanic earthquake timing using wireless sensor networks
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