Volcano Monitoring: Addressing Data Quality Through Iterative Deployment

  • Geoffrey Challen
  • Matt Welsh


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.

Volcano Sensor network Data quality Optimization Deployment Time synchronization Lance 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Benz J et al (1996) Three-dimensional P and S wave velocity structure of Redoubt Volcano, Alaska. J Geophys Res 101:8111–8128CrossRefGoogle Scholar
  2. 2.
    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):2331Google Scholar
  3. 3.
    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 SurveyGoogle Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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’07Google Scholar
  6. 6.
    Lees J, Crosson R (1989) Tomographic inversion for three-dimensional velocity structure at Mount St. Helens using earthquake data. J Geophys Res 94:5716–5728CrossRefGoogle Scholar
  7. 7.
    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, USAGoogle Scholar
  8. 8.
    Maroti M, Kusy B, Simon G, Ledeczi A (2004) The flooding time synchronization protocol. In: Second ACM Conference on Embedded Networked Sensor SystemsGoogle Scholar
  9. 9.
    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 99–146Google Scholar
  10. 10.
    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):10,775–10,786Google Scholar
  11. 11.
    Moss D, Hui J, Levis P, Choi JI (2007) Cc2420 radio stack. TinyOS Extension Proposal TEP-126,
  12. 12.
    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 5–10Google Scholar
  13. 13.
    Neuberg J, Luckett R, Ripepe M, Braun T (1994) Highlights from a seismic broadband array on Stromboli volcano. Geophys Res Lett 21(9):749–752CrossRefGoogle Scholar
  14. 14.
    Paxson V (1998) On calibrating measurements of packet transit times. In: Measurement and Modeling of Computer Systems, pp 11–21,
  15. 15.
    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
  16. 16.
    Phillips W, Fehler M (1991) Traveltime tomography: A comparison of popular methods. Geophys 56:1649–1649CrossRefGoogle Scholar
  17. 17.
    Scarpa R, Tilling R (1996) Monitoring and Mitigation of Volcano Hazards. Springer-Verlag, BerlinGoogle Scholar
  18. 18.
    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)Google Scholar
  19. 19.
    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)Google Scholar
  20. 20.
    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, WAGoogle Scholar
  21. 21.
    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, USAGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Harvard School of Engineering and Applied SciencesHarvard UniversityCambridgeUSA

Personalised recommendations