Abstract
Deploying wireless sensor networks to support geophysics presents an interesting challenge. High data-rates required by geophysical instrumentation preclude continuous data collection from even moderately-sized networks. However, geoscientists are used to working directly with complete signals, and therefore uncomfortable with in-network processing that could reduce bandwidth by reporting data products.
Over five years of working with seismologists we have developed a lineage of solutions driven by their scientific goals. Three field deployments have provided valuable lessons and helped drive each successive design iteration. We began by addressing datum quality, encompassing per-sample resolution, accuracy, and time synchronization. Later deployments focused on holistic data quality, which requires considering constraints limiting full data collection in order to maximize the value of the limited data retrieved. This chapter uses our three deployments to demonstrate the benefits of iteration. The first two illustrate our work on datum quality, while the last presents a new approach to optimizing overall dataset quality.
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References
Benz J et al (1996) Three-dimensional P and S wave velocity structure of Redoubt Volcano, Alaska. J Geophys Res 101:8111–8128
Chouet B et al (2003) Source mechanisms of explosions at Stromboli Volcano, Italy, determined from moment-tensor inversions of very-long-period data. J Geophys Res 108(B7):2331
Dietel C et al (1989) Data summary for dense GEOS array observations of seismic activity associated with magma transport at Kilauea Volcano, Hawaii. Tech. Rep. 89–113, U.S. Geological Survey
Hui JW, Culler D (2004) The dynamic behavior of a data dissemination protocol for network programming at scale. In: Proc. 2nd ACM Conference on Embedded Networked Sensor Systems (SenSys’04)
Kim S, Fonseca R, Dutta P, Tavakoli A, Culler D, Levis P, Shenker S, Stoica I (2007) Flush: A Reliable Bulk Transport Protocol for Multihop Wireless Networks. In: Proc. SenSys’07
Lees J, Crosson R (1989) Tomographic inversion for three-dimensional velocity structure at Mount St. Helens using earthquake data. J Geophys Res 94:5716–5728
Li M, Agrawal D, Ganesan D, Venkataramani A (2009) Block-switched networks: A new paradigm for wireless transport. In: NSDI’09: Proceedings of the 6th conference on Symposium on Networked Systems Design & Implementation, USENIX Association, Berkeley, CA, USA
Maroti M, Kusy B, Simon G, Ledeczi A (2004) The flooding time synchronization protocol. In: Second ACM Conference on Embedded Networked Sensor Systems
McNutt S (1996) Seismic monitoring and eruption forecasting of volcanoes: A review of the state of the art and case histories. In: Scarpa, Tilling (eds) Monitoring and Mitigation of Volcano Hazards, Springer-Verlag Berlin Heidelberg, pp 99–146
Moran S, Lees J, Malone S (1999) P wave crustal velocity structure in the greater Mount Rainier area from local earthquake tomography. J Geophys Res 104(B5):10,775–10,786
Moss D, Hui J, Levis P, Choi JI (2007) Cc2420 radio stack. TinyOS Extension Proposal TEP-126, http://www.tinyos.net/tinyos-2.x/doc/txt/tep126.txt
Murray T, Endo E (1992) A real-time seismic-amplitude measurement system (rsam). In: Ewart, Swanson (eds) Monitoring Volcanoes: Techniques and Strategies Used by the Staff of the Cascades Volcano Observatory, 1980-1990, vol 1966, USGS Bulletin, pp 5–10
Neuberg J, Luckett R, Ripepe M, Braun T (1994) Highlights from a seismic broadband array on Stromboli volcano. Geophys Res Lett 21(9):749–752
Paxson V (1998) On calibrating measurements of packet transit times. In: Measurement and Modeling of Computer Systems, pp 11–21, citeseer.ist.psu.edu/paxson98calibrating.html
Paxson V (2004) Strategies for sound internet measurement. In: IMC ’04: Proceedings of the 4th ACM SIGCOMM conference on Internet measurement, ACM Press, New York, NY, USA, pp 263–271, DOI http://doi.acm.org/10.1145/1028788.1028824
Phillips W, Fehler M (1991) Traveltime tomography: A comparison of popular methods. Geophys 56:1649–1649
Scarpa R, Tilling R (1996) Monitoring and Mitigation of Volcano Hazards. Springer-Verlag, Berlin
Werner-Allen G, Johnson J, Ruiz M, Lees J, Welsh M (2005) Monitoring volcanic eruptions with a wireless sensor network. In: Proc. Second European Workshop on Wireless Sensor Networks (EWSN’05)
Werner-Allen G, Swieskowski P, Welsh M (2005) MoteLab: A Wireless Sensor Network Testbed. In: Proc. the Fourth International Conference on Information Processing in Sensor Networks (IPSN’05)
Werner-Allen G, Lorincz K, Johnson J, Lees J, Welsh M (2006) Fidelity and yield in a volcano monitoring sensor network. In: Proc. 7th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2006), Seattle, WA
Werner-Allen G, Dawson-Haggerty S, Welsh M (2008) Lance: Optimizing high-resolution signal collection in wireless sensor networks. In: Proc. ACM Conference on Embedded Networked Sensor Systems (Sensys), Raleigh, NC, USA
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Challen, G., Welsh, M. (2010). Volcano Monitoring: Addressing Data Quality Through Iterative Deployment. In: Gaura, E., Allen, M., Girod, L., Brusey, J., Challen, G. (eds) Wireless Sensor Networks. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5834-1_4
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