Washington State UniversitySensorweb Research Laboratory Air-dropped Sensor Network for Real-time High-fidelity Volcano Monitoring Wen-Zhan Song, Renjie

Washington State University

Sensorweb Research Laboratory

Air-dropped Sensor Network for Real-time

High-fidelityVolcano Monitoring

Wen-Zhan Song, Renjie Huang, Mingsen Xu, Andy Ma, Behrooz Shirazi


Richard LaHusenU.S. Geological Survey

ACM MobiSys 2009

Kraków, Poland, June 22-25 2009



Outline

Introduction System design Campus outdoor test Field deployment Conclusion

-3- Washington State University


Background: Volcano Hazards Volcanoes are everywhere - on Earth

and beyond

Magmatism is of fundamental importance to planetary evolution and essential to life as we know it

On Earth, volcanic risk is increasing rapidly as human population increases

Volcanic Earthquakes

Directed Blast

Tephra

Volcanic Gases

Lava Flows

Debris Avalanches, Landslides, and Tsunamis

Pyroclastic Surge

Pyroclastic Flows

Lahars



Sugar Bowl camera at Mount St. Helens, 2005

Volcano Crater: a harsh environment

Winter EDM survey at Mount St. Helens, 1980s



Camera and gas sampler spider shown pre-positioned at Sugar Bowl on 14 January 2005. Shortly after this picture was taken, spider was deployed within 100 m of extrusion site.

Volcano Crater: a harsh environment

Two days later, it looked like this.

So we need smarter sensors and networks to ensure continuous, spatially dense monitoring in hazardous areas



Mount St. Helens: an active volcano



Background: OASIS projectOptimized Autonomous Space In-situ Sensorweb

OASIS has two-way communication capability between ground and space assets, use both space and ground data for optimal allocation of limited power and bandwidth resources on the ground, and use smart management of competing demands for limited space assets.

1. In-situ sensor-web autonomously determines network topology, bandwidth and power allocation.

2. Activity level rises causing self-organization of in-situ network topology and a request for re-tasking of space assets.

3. High-resolution remote-sensing data is acquired and fed back to the control center.

4. In-situ sensor-web ingests remote sensing data and re-organizes accordingly. Data are publicly available at all stages.



Application Characteristics Challenging environment

Extreme weathers: temperature (baking/freezing), wind, snow, rain,

Dynamic environment: rock avalanche, land sliding, gas/steam emissions, volcanic eruptions, earthquake

Battery is the only reliable energy source. Solar panel is possible in summer, but frequently covered by ashes

Stations are frequently destroyed, some hot spot can only be accessed through air drop

Low signal noise ratio of both communication and sampling

High data rate, and require network synchronized sampling

Seismic sensor: 100-200Hz, 16 bit/sample Infrasonic sensor: 100-200Hz, 16 bit/sample Lightning sensor: 1Hz, 16 bit/sample GPS raw data: 200-300 bytes/10 seconds



System Requirements

Synchronized Sampling Real-time Continuous Raw Data One-year Robust Operation Online Configurable Fast Deployment



Hardware Design

•Seismic

•Infrasonic

•Lightning

iMote2

MDA320

UBlox GPS



Synchronized Sampling Design goal

Synchronize with UTC time Synchronized sampling – different nodes

sample channels at same time point, 1ms resolution

Hybrid Time Synchronization Stay synchronized with GPS if GPS is good Switch to modified FTSP (Flooding Time

Synchronization Protocol, Maróti, Sensys 2004) when GPS is disconnected



Configurable Sensing

Configurable Parameters Change sampling rate Add/Delete sensor Change data priority Change node priority



Configurable Sensing

Configurable Data Processing Tasks



Situation Awareness

RSAM (Real-Time Seismic-Amplitude Measurement)

RSAM period: 1 sec

STA window: 8 sec

LTA window: 30 sec

Trigger ratio: 2 LTA and STA calculation

Detect seismic events and give higher priority to event data.



Situation Awareness STA/LTA event detection

Monitor the ratio of Short-Term Average (STA) and Long-Term Average (LTA)

Event is triggered when ratio is over threshold



Situation Awareness

Prioritization Assigning priorities based on data and event

type Assigning retransmission opportunities based

on priorities



Agile Data Collection Routing Invalid route when a node detects a loop,

or it does not receive route beacon from its parent for more than 6 beacon periods, or all packet transmissions in last 15 seconds fail. Asymmetric links will be avoided.

Maintain alternative parent (if available) in neighbor table, which will be used if its current parent lost, instead of rediscovering a new parent.

Accelerate good news and bad news propagation.



Reliable Data Dissemination

Opportunistic broadcast flow

Parent-children monitoring

Explicit and implicit ACK

Retry and request

Cascades: reliable fast data dissemination



Light-weight Remote Procedure Call Mechanism

Module designers decide which interface or command to be allowed to call remotely, by simply adding @rpc();

interface SensingConfig @rpc(); It will be translated to XML and used by client

for remote control

<SmartSensingM.SensingConfig.setSamplingRate commandID="23" componentName="SmartSensingM" functionName="setSamplingRate" functionType="command" interfaceName="SensingConfig" interfaceType="SensingConfig" numParams="2" provided="1" signature=" command result_t SmartSensingM.SensingConfig.setSamplingRate ( uint8_t type, uint16_t samplingRate ) ">

<params> <param0 name="type"> <type typeClass="unknown" typeDecl="uint8_t" typeName="uint8_t" /> </param0> <param1 name="samplingRate"> <type typeClass="unknown" typeDecl="uint16_t" typeName="uint16_t" /> </param1> </params> <returnType typeClass="unknown" typeDecl="result_t" typeName="result_t" />

</SmartSensingM.SensingConfig.setSamplingRate>

Network Control

Originated from Marionette, IPSN 2006



System Robustness Watchdog mechanism to restart nodes

If any illegal operations, such as divide by 0 If radio did not send or receive for 5 minutes

(when the network data rate is high). If some memory buffer is full and never get

cleared for 5 minutes. Sanity check is necessary. We found some

unexpected things in tinyos: Radio corrupts pending tinyos message

header and cause the pointer not to return to correct up layer

Event sendDone signaled twice to up layer Message passed CRC check, but has shorter or

longer length than its length field



Test Lessons Hardware verification shall start as

early as possible, do not wait until last minute We had a headache to extend tx range in

last one month Quantitative measurement is essential,

do not rely on other’s experiences After we added RF amplified, RSSI was

strong, but LQI and link reliability was weak

It taught us that: RSSI reflects signal+noise, while LQI reflects signal/noise ratio.



Test Lessons Open for any possibility – need

critical thinking skills. During test, a node’s signal quality

decreased during 1PM-6PM sunny days (when temperature is high), we changed everything except cable

After we changed the high-quality cables (LMR@-400-ULTRAFLEX COAXIAL CABLE TIMES MICROWAVE SYSTEMS) to some lower-quality cables (BELDEN 8262M17/155-00001 MIL-C-17 16428 2137 19:22 ROHS), the problem is gone.

This problem does not happen in other nodes, even with same cable. Still do not know exact reasons – it might be related to RF impedence!



System Deployment





Node 16

10/15/08



System statistics

gray color:

Hour-averaged loss ratio

black color:

Parent node’s LQI



System statistics

The uptime of nodes and data server



Node 15 disappears in 18 hours, because Node 15 disappears in 18 hours, because …………

Node 15

10/22/08

Node 15 disappear in first week because …



Wind speed peaks at 120 Wind speed peaks at 120 miles/hourmiles/hour

Infrasonic sensor records the unusual gust …



Comparison with existing USGS stations

Several types of USGS stations in place: Dual frequency GPS with digital store and

forward telemetry when polled – not continuous!

Short period seismic stations with geophones and analog telemetry – not digital

Broad band seismic stations with digital telemetry – cost above $10K and several days to deploy

Microphones for explosion detection added to the short period seismic stations



Cost and function comparison



Data quality comparison

Magnitude 1 Earthquake Magnitude 1 Earthquake Mount St. Helens

3 km depth November 4, 2008



Conclusion Meets the system requirement, with the goal to

replace data loggers for volcano monitoring. Synchronized Sampling Real-time Continuous Raw Data One-year Robust Operation Online Configurable Fast Deployment

Clears the doubts of domain scientists and proves that the low-cost sensor network system can work in extremely harsh environments.

Next deployment on Summer/Fall 2009 15 stations into crater and around flanks Integrate TreeMAC (Song etc, PerCom’09), ALFC

compression (Kiely etc, PerCom’09), Over-the-air programming



Thank You!Thank You!

WenZhan SongEmail: [email protected]

Deployment video http://www.youtube.com/watch?v=IbCpioUlF0I

More information, visithttp://sensorweb.vancouver.wsu.edu



Hardware DesignHardware Design Controller: Intel Mote2

CPU: PXA271 13-416MHz with Dynamic Voltage Scaling. 13MHz operates at a low voltage (0.85V)

Storage: 256kB SRAM, 32MB SDRAM, 32MB Flash 802.15.4 radio: CC2420

Other Hardware Components Seismic: low noise MEMS accelerometer (Silicon Designs Model

1221J-002) Infrasonic: low range differential pressure sensor (All Sensors's

Millivolt Output Pressure Sensors Model 1 INCH-D-MV) Lightning (for ash detection): custom USGS/CVO RF pulse detector GPS (for deformation measurement): L1 GPS (Ublox model LEA-4T) Customized SmartAmp 2.4GHz, 250mW, amplify -3dBm input to

20dBm output. Antenna: 12 dB omni, withstand extreme wind speeds in excess of

130 ++ MPH Battery: a bundle of Cegasa air-alkaline industrial batteries

Documents

Washington State UniversitySensorweb Research Laboratory Air-dropped Sensor Network for Real-time High-fidelity Volcano Monitoring Wen-Zhan Song, Renjie