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Monitoring Volcanic Eruptions with a Wireless Sensor Networks Geoffrey Werner-Allen, Jeff Johnson, Mario Ruiz, Jonathan Lees, and Matt Welsh Harvard University EWSN ’ 05 Presented by Tim
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Outline Introduction Background System Design Deployment Distributed Event Detection Evaluation Conclusion
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Introduction Volcanic monitoring has a wide range of goals, related to both scientific studies and hazard monitoring. Volcanologists currently use wired arrays of sensors to monitor volcanic eruptions. Wireless sensor networks have the potential to greatly benefit studies of volcanic activity.
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Background Infrasound (Infrasonic wave) Sound with very low frequency (1~50Hz) Very high amplitude but not audible Seismic wave Wave travels through the Earth, often as the result of an earthquake or explosion
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Volcanic Monitoring
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Challenges and Issues Existing data loggers store data locally e.g., 1 or 2 Gb microdrives, store about 15 days' worth of data Must trek up to the station to retrieve the data Usually very inaccessible: can take several hours to drive/hike in Very high power consumption Two car batteries plus solar panels to recharge Very expensive Individual data logger costs thousands of $$$ Still need PCs/laptops to process and store data permanently Hard to deploy large number of stations Size, cost, power requirements,...
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Opportunities for wireless sensor networks Data sampling rates of ~100 Hz Very small, low power, easy to deploy Can put out a larger number of sensors in an area Can customize software on the motes for capture, preprocessing, etc.
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Outline Introduction Background System Design Deployment Distributed Event Detection Evaluation Conclusion
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System Architecture
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Infrasound Node Sample data continuously at 102.4Hz A set of 25 consecutive samples is packed into a 32-byte packet and transmitted at approximately 4 Hz. The aggregator will send acknowledgement back. If source node does not receive ack, it ’ ll retransmit up to 5 times.
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Aggregator Node
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GPS Receiver Node Motes record sample # and GPS time seq # in message Can be used to align samples from each mote
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Time Regression Uncertainties The sampling rate of individual note may vary slightly over time, due to changes in temperature and battery voltage. The log do not record the precise time. Apply a linear regression to the data log stream and map individual sample to a “ true ” time.
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Physical Packaging
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Outline Introduction Background System Design Deployment Distributed Event Detection Evaluation Conclusion
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Volcano Tungurahua Active volcano in central Ecuador – 5018 m Site of much ongoing seismological research
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Deployment Three infrasound nodes, one central aggregator node and a GPS receiver. The GPS receiver and FreeWave modem were powered by a 12 V car battery. All other nodes were powered by 2 AA batteries. The distance between sensors and observatory is about 9km. The deployment was active from July 20 – 22, 2004 and collected over 54 hours of infrasonic signals.
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Deployment
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Data Analysis- Loss Rate Weather conditions (e.g., rain) affected radio transmission. Mote 4 experienced very low loss, due to its position with line-of-sight to the receiver. Mote 3 experienced higher loss, probably due to antenna orientation.
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Data Analysis- Correlation The result of wireless sensor array shows high correlation with wired station.
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Outline Introduction Background System Design Deployment Distributed Event Detection Evaluation Conclusion
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Distributed Event Detection The initial deployment is not feasible for larger arrays deployed over long period of time. To save bandwidth and energy, it is desired to avoid transmitting signals when the volcano is quiescent.
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Mechanism Each node samples data continuously at 102.4 Hz. When the local event detector triggers, the node broadcasts a vote message. If any node receives enough votes from its neighbor nodes, it initiates global data collection by flooding a message to all nodes in the network. Token-based scheme for scheduling transmissions. The order depends on node ID.
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Local Detector Design Threshold-based detector Exponentially weighted moving average based detector
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Local Detector Design Threshold-based detector Triggered whenever a signal rises above T hi and falls below another T lo during some time window W. Because it relies on absolute thresholds, it is sensitive to particular microphone gain on each node.
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Local Detector Design Exponentially weighted moving average based detector For each sample, calculate two moving averages with different gain parameters, α short,α long,and compare the ratio of the two averages. e.g., (α short = 0.05,α long =0.002) If the ratio exceeds some threshold T (i.e., the short- term average exceeds the long-term average by a significant amount), the detector is triggered.
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Outline Introduction Background System Design Deployment Distributed Event Detection Evaluation Conclusion
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Evaluation Use 8 mica2 nodes in the lab, but only 4 nodes with infrasound sensor board. The infrasound signals were produced by closing the lab door. Three parts Energy usage Bandwidth usage Detector accuracy
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Energy usage Each node exhibits a baseline current draw of about 18mA and supply voltage is 3 V. Assuming that nodes detect a correlated signal every ½ hours, and locally vote at twice this rate.
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Bandwidth usage Continuous sampling scheme consumes nx4x32 bytes/sec of bandwidth (n:# of nodes, each node transmit one pkt every ¼ sec, size of pkt :32bytes) Because of the low frequency of eruptions, distributed event detection uses less bandwidth.
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Detector Accuracy Fed the detectors with the complete trace of data recorded on Tungurahua.
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Future Work & Conclusion Seismology presents many exciting opportunities for wireless sensor networks. To expand the number of sensors in the array and distribute them over a wider aperture. The long-term plans are to provide a permanent, reprogrammable sensor array on Tungurahua.
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My Comments The idea is simple but it ’ s hard work to deploy the motes in such a place. To do research needs lots of passion. The first mote-based application to volcanic monitoring! Provide a wealth of experience to develop more sophisticated tools.
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