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dc.contributor.advisorCamp, Tracy
dc.contributor.authorRubin, Marc J.
dc.date.accessioned2007-01-03T07:04:54Z
dc.date.accessioned2022-02-09T08:59:22Z
dc.date.available2007-01-03T07:04:54Z
dc.date.available2022-02-09T08:59:22Z
dc.date.issued2014
dc.date.submitted2014
dc.identifierT 7615
dc.identifier.urihttps://hdl.handle.net/11124/10629
dc.description2014 Fall.
dc.descriptionIncludes illustrations (some color).
dc.descriptionIncludes bibliographical references (pages 165-171).
dc.description.abstractIn this dissertation, we present our research contributions geared towards creating an automated and efficient wireless sensor network (WSN) for geohazard monitoring. Specifically, this dissertation addresses three overall technical research problems inherent in implementing and deploying such a WSN, i.e., 1) automated event detection from geophysical data, 2) efficient wireless transmission, and 3) low-cost wireless hardware. In addition, after presenting algorithms, experimentation, and results from these three overall problems, we take a step back and discuss how, when, and why such scientific work matters in a geohazardous risk scenario. First, in Chapter 2, we discuss automated geohazard event detection within geophysical data. In particular, we present our pattern recognition workflow that can automatically detect snow avalanche events in seismic (geophone sensor) data. This workflow includes customized signal preprocessing for feature extraction, cluster-based stratified sub-sampling for majority class reduction, and experimentation with 12 different machine learning algorithms; results show that a decision stump classifier achieved 99.8% accuracy, 88.8% recall, and 13.2% precision in detecting avalanches within seismic data collected in the mountains above Davos, Switzerland, an improvement on previous work in the field. To address the second overall research problem (i.e., efficient wireless transmission), we present and evaluate our on-mote compressive sampling algorithm called Randomized Timing Vector (RTV) in Chapter 3 and compare our approach to four other on-mote, lossy compression algorithms in Chapter 4. Results from our work show that our RTV algorithm outperforms current on-mote compressive sampling algorithms and performs comparably to (and in many cases better than) the four state-of-the-art, on-mote lossy compression techniques. The main benefit of RTV is that it can guarantee a desired level of compression performance (and thus, radio usage and power consumption) without subjugating recovered signal quality. Another benefit of RTV is its simplicity and low computational overhead; by sampling directly in compressed form, RTV vastly decreases the amount of memory space and computation time required for on-mote compression. Third, in Chapter 5, we present and evaluate our custom, low-cost, Arduino-based wireless hardware (i.e., GeoMoteShield) developed for wireless seismic data acquisition. In particular, we first provide details regarding the motivation, design, and implementation of our custom GeoMoteShield and then compare our custom hardware against two much more expensive systems, i.e., a traditional wired seismograph and a "from-the-ground-up" wireless mote developed by SmartGeo colleagues. We validate our custom WSN of nine GeoMoteShields using controlled lab tests and then further evaluate the WSN's performance during two seismic field tests, i.e., a "walk-away" test and a seismic refraction survey. Results show that our low-cost, Arduino-based GeoMoteShield performs comparably to a much more expensive wired system and a "from the ground up" wireless mote in terms of signal precision, accuracy, and time synchronization. Finally, in Chapter 6, we provide a broad literature review and discussion of how, when, and why scientific work matters in geohazardous risk scenarios. This work is geared towards scientists conducting research within fields involving geohazard risk assessment and mitigation. In particular, this chapter reviews three topics from Science, Technology, Engineering, and Policy (STEP): 1) risk, scientific uncertainty, and policy, 2) society's perceptions of risk, and 3) the effectiveness of risk communication. Though this chapter is not intended to be a comprehensive STEP literature survey, it addresses many pertinent questions and provides guidance to scientists and engineers operating in such fields. In short, this chapter aims to answer three main questions, i.e., 1) "when does scientific work influence policy decisions?", 2) "how does scientific work impact people's perception of risk?", and 3) "how is technical scientific work communicated to the non-scientific community?".
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2010-2019 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectwireless sensor network
dc.subjectavalanche
dc.subjectpattern recognition
dc.subjectlossy compression
dc.subjectcompressive sensing
dc.subjectseismic
dc.subject.lcshAvalanches -- Forecasting
dc.subject.lcshWireless sensor networks
dc.subject.lcshPattern recognition systems
dc.subject.lcshMachine learning
dc.subject.lcshHazard mitigation
dc.subject.lcshCommunication in science
dc.titleEfficient and automatic wireless geohazard monitoring
dc.typeText
dc.contributor.committeememberWakin, Michael B.
dc.contributor.committeemembervan Herwijnen, Alec
dc.contributor.committeememberNavidi, William Cyrus
dc.contributor.committeememberSchneider, Jennifer J.
thesis.degree.nameDoctor of Philosophy (Ph.D.)
thesis.degree.levelDoctoral
thesis.degree.disciplineElectrical Engineering and Computer Science
thesis.degree.grantorColorado School of Mines


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