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

Outline  Introduction  Background  System Design  Deployment  Distributed Event Detection  Evaluation  Conclusion

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.

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

Volcanic Monitoring

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,...

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.

Outline  Introduction  Background  System Design  Deployment  Distributed Event Detection  Evaluation  Conclusion

System Architecture

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.

Aggregator Node

GPS Receiver Node  Motes record sample # and GPS time seq # in message Can be used to align samples from each mote

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.

Physical Packaging

Outline  Introduction  Background  System Design  Deployment  Distributed Event Detection  Evaluation  Conclusion

Volcano Tungurahua  Active volcano in central Ecuador – 5018 m  Site of much ongoing seismological research

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.

Deployment

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.

Data Analysis- Correlation  The result of wireless sensor array shows high correlation with wired station.

Outline  Introduction  Background  System Design  Deployment  Distributed Event Detection  Evaluation  Conclusion

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.

Mechanism  Each node samples data continuously at 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.

Local Detector Design  Threshold-based detector  Exponentially weighted moving average based detector

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.

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.

Outline  Introduction  Background  System Design  Deployment  Distributed Event Detection  Evaluation  Conclusion

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

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. 

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.

Detector Accuracy  Fed the detectors with the complete trace of data recorded on Tungurahua.

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.

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.