Collaborative Research: Adaptive Distributed Processing .Collaborative Research: Adaptive...
Embed Size (px)
Transcript of Collaborative Research: Adaptive Distributed Processing .Collaborative Research: Adaptive...
Collaborative Research: Adaptive Distributed Processing in Volcano Sensor Networks
This project brings together researchers in geophysics and computer science to develop advanced distributed pro-cessing techniques for wireless sensor networks at active volcanoes. Software will be developed to process data froma large network of sensors for describing the 4D evolution of an erupting volcano in real time. Immediate feedbackon the state of the volcano will provide critical information on eruptive hazards and potential for a large-scale cri-sis. The results of this project will have significant impact on the next generation of volcano sensor networks forscientific study and hazard monitoring.
Distributed processing in the sensor network will greatly reduce radio bandwidth and power requirements, al-lowing us to drastically increase the number of sensors at a volcano. Increased sensor density in turn yields a deeperunderstanding of volcanic processes. Our team has previous experience developing and deploying wireless sensornetworks at active volcanoes (described in Section 2). Building upon our preliminary successes, we propose todevelop a range of adaptive, distributed sensor processing techniques with a specific emphasis on high-resolutionseismic and acoustic signals from an active volcano.
As part of this project, we propose to deploy and maintain a large-scale wireless sensor network consistingof 50 sensor nodes, divided into three local arrays, on an active volcano. Each node will be capable of samplingand processing seismic and acoustic data at high data rates. The core challenge that arises in this project is howto process the high volume of data across the sensor network to optimize network lifetime and data quality. Radiocommunication is power-hungry and offers limited bandwidth (less than 100 Kbps across the network). However, atypical sensor node can perform thousands of CPU cycles of computation for the same energy cost as transmitting asingle radio message. This tradeoff motivates the need for advanced processing to be performed on the sensor nodesthemselves, greatly reducing bandwidth requirements and increasing battery lifetime.
Intellectual merit: The goal of this project is to develop techniques to extract high-level information from theraw signals collected by sensors through adaptive, distributed signal processing. We intend to construct a range ofdistributed algorithms for calculating energy release, determining seismic and acoustic wave arrival times, charac-terizing and differentiating eruption earthquakes from sub-surface earthquakes, and precisely picking phase arrivalsfor discrete events.
The results of this per-sensor data analysis will enable high-resolution, rapid earthquake locations to be estimatedusing data from across the sensor network. Adaptive tomographic inversion of these signals will provide a real-time 4D movie of the evolving structure of the volcanos interior. Moreover, by allowing the network to beretasked on the fly, differing computations can be introduced as needed based on the changing dynamics of thevolcanos activity. Multiple users can also share the array during its deployment lifetime.
The focus of this project is high-level data processing for networked volcano sensor arrays, not on the systems-level issues of wireless sensor networks, such as radio communication, multihop routing, and time synchronization.Our previous work on wireless sensor networks has addressed many of these issues, and in this proposal we specifi-cally focus on the distributed data processing challenges that arise in this domain.
Broader impact: This project has substantial implications for monitoring active volcanoes and understandingthe complex geophysical processes involved. The ability to deploy large wireless sensor arrays on hazardous volca-noes also holds vast potential for real-time hazard monitoring and early warning systems. This collaboration betweenseismologists and computer scientists offers the opportunity to introduce a new technology for an important area ofgeophysical monitoring and to push the envelope for both the capabilities of wireless sensor networks and the toolsavailable for volcanic monitoring.
Moreover, the techniques developed in this project extend to other types of geophysical monitoring, includingtectonic earthquake studies, geothermal field exploration, terrestrial and glacial geodesy, and atmospheric sciences.For each discipline, additional types of sensors (e.g., GPS, tilt-meters, gas monitors, thermal sensors, anemometers,and strong-motion seismographs) can be readily integrated into the framework that will be developed in this project.
The educational activities of this project include developing a graduate Computer Science seminar coursearound the proposed research project; extending undergraduate courses in seismology and geophysics to incorporateemerging technology in data acquisition and networking, and involving undergraduates into the research programthrough independent study projects and assistance with our sensor network deployment. The project will supportgraduate student research at each institution where the unification of field acquisition, physical analysis and computerscience offers cross-disciplinary experience. We have organized this research so that all the participants, studentsincluded, will provide key input in the lab and in the field.
1.1 Research Team
This project brings together experts from computer science and seismology at Harvard, UNH, and UNC. Our grouphas been collaborating for the last two years in this area, and we have successfully undertaken two deployments ofwireless sensor arrays on active volcanoes in Ecuador [93, 92]. Through this project we endeavor to further thiscollaboration and leverage our previous experience.
PI Matt Welsh is an assistant professor of Computer Science at Harvard University. He is very active in thearea of wireless sensor networks, developing operating system and networking techniques for a number of appli-cations, including medical care [59, 55] and seismology (the subject of this proposal). He has also been workingon high-level programming abstractions for sensor networks. These include a programming model called abstractregions [90, 91], an adaptive resource-management technique called SORA [30, 57], and a programming languagecalled Regiment [66, 67]. Prior to joining Harvard, Welsh spent one year as a visiting researcher at Intel Research,Berkeley, as a member of the TinyOS project team. There he was one of the core developers of the NesC language used by TinyOS and the TOSSIM sensor network simulator [52, 83].
Co-PI Jeff Johnson is a research assistant professor in the Department of Earth Sciences at UNH. His primarytraining is in geophysics and volcanology. He is a pioneer in the use of infrasound and seismicity for modelingeruptive processes at volcanoes [18, 23, 24, 22, 19, 29, 20]. Much of his recent work is based upon seismo-acousticdata that he has collected in the field. Johnson has deployed seismic and acoustic arrays and networks at volcanoes inKamchatka, Ecuador, Antarctica, Italy, Hawaii, Guatemala, Mexico, and Chile. He has designed and built infrasonicsensors for both campaign-style and permanent deployments at many volcanoes.
Co-PI Jonathan Lees is an associate professor of Geophysics at University of North Carolina. He is a leader inthe field of tomographic techniques used to spatially image the interior structure of volcanoes . Lees is one ofthe first geophysicists to develop algorithms for rapid inversion of large sparse matrices as applied to geophysicaltomography. He has worked on and applied his methods on numerous volcanoes worldwide, including Mt. St.Helens, Mt. Rainier, Kliuchevskoi Volcano (Kamchatka, Russia), Mt. Fuji, and others. He has done extensive workinvestigating geothermal systems using seismological imaging methods and is currently developing rapid real-timemonitoring systems for geothermal fields in Iceland.
2 Background and Previous Work
Earth scientists monitor volcanoes for two non-exclusive reasons: (1) to assess the level of volcanic unrest so thatcivil authorities may mitigate hazards; and (2) to understand physical processes occurring within the volcano relatedto internal structure, magma migration, and eruption mechanisms . The most powerful tool used for these ends isthe seismometer, which detects ground-propagating elastic radiation from both sources internal to the volcano (e.g.,fracture induced by pressurization) and on the surface (e.g., expansion of gases during an eruption) . The seis-mometer is traditionally used as part of a multi-station network (dispersed station spacing) or array (dense clusteringof sensors) in order to sample the seismic wavefield and invert for earthquake location and velocity structure of theedifice.
In recent years it is has become standard to supplement seismic networks with additional geophysical monitors,such as, for example, infrasonic sensors [9, 11, 23, 78, 89]. Low-frequency (
Base stationat observatory
Long-distance radio modem link
Figure 1: Our proposed wireless sensor network architecture.
wavefield at a volcano. Infrasound monitoring is useful for differentiating shallow and surface seismicity and forquantifying eruptive styles and intensity . Although this project is focused on seismo-acoustic data acquisition,the distributed analysis techniques that will be developed will be appropriate for incorporation of additional types ofsensors (e.g., thermal, gas flux, geodetic) in the future.
For source location and tomography studies, it is desirable to record the elastic wavefield with a network ofdistributed sensor