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Recent observed environmental changes as well as projections in the fourth assessment report of the Intergovernmental Panel on Climate Change shed light on likely dramatic consequences of a changing mountain cryosphere following climate change. Some very destructive geological processes are triggered or intensified, influencing the stability of slopes and possibly inducing landslides. Unfortunately, the interaction between these complex processes is poorly understood. This project addresses the key issues in response to such changing conditons: monitoring and warning systems for the spatial and temporal detection of newly forming hazards, as well as extending the quantitative understanding of these changing natural systems and our predictive capabilities.
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X-SenseMonitoring Alpine Mass Movements at Multiple Scales- Annual Meeting 13th May 2011 -
Lothar Thiele, Jan Beutel ETH Zurich, Embedded/WirelessStephan Gruber University Zurich, Physical GeographyAlain Geiger ETH Zurich, Geodesy and PhotogrammetryTazio Strozzi, Urs Wegmüller GAMMA SA, SAR Remote SensingHugo Raetzo BAFU/FOEN
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[Eiger east-face rockfall, July 2006, images courtesy of Arte Television]
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X-Sense Hypothesis
Anticipation of future environmental states and risk is improved by a systematic combination of environmental sensing at
diverse temporal and spatial scales and process modeling
Wireless Sensor Network Technology allows to quantify mountain cryosphere phenomena and their
transient response to climate change can be used for safety critical applications in an hostile
environment
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Climate change and cryosphere as (additional) elements of surprise
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New Avenues for X-SenseDetecting and measuring large-scale terrain movement Understanding newly-developed slope movements
Sensor challenges Complex sensors (combinations
of sensors, different scales) Variable data rates User interaction (feedback) In-network processing
> 100 cm/year50-100 cm/year10-50 cm/year2-10 cm/year0-2 cm/year
Current methods: InSAR measurements Manual D-GPS
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X-Sense PlatformHost Stationprocessing, fusion, storage
Reference GPS
Moving debrismoving rock slope
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Sensor Network Promises
Sensor nodes are cheap, so we can have plenty of them.Nodes may be cheap, but deployment and maintenance is expensive.
Additional redundant nodes make the system fault tolerant automatically.More nodes make the system more fragile.
End-to-end Predictability and Efficiency
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- Design Approach –
Develop a methodology for the design of dependable wireless sensor networks
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Challenge: The Physical EnvironmentLightning, avalanches, rime, prolonged snow/ice cover, rockfallStrong daily variation of temperature −30 to +40°C ∆T ≦ 20°C/hour
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Traditional iterative design approach: waterfall-modelRepeated for individual system layers
Challenge: The Design Approach
Testbed [Matthias Woehrle]
insufficient knowledge of target application / environment working on resource limits
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Top-down Approach: In-situ Design & Test
Behavioral DataRefinedPlatform
Specification
Flexible in-situ exploration (testbed ≠ real system)Real sensor data, real environmentIntegration with live data management (system of systems)
Feature-rich Platform
observe,experiment,learn on-site
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- Deployment –
Provide a prototype system that allows to quantify mountain cryosphere phenomena
and can be used in early warning scenarios.
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Field Site Selection
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Location Planning of Measurement Devices
•Dirru rock glacier•velocity > 1 m/a
•reference devices
•TerraSAR-X•(Sept. 2009, 11 days)
Field site selection based on aerial photographs, satellite-based InSARdetection and fieldwork
Vanessa Wirz Vanessa Wirz
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New GPS Logger Devices30 GPS logger devices have been designed and manufactured in partnership with Art-of-Technology AGFinancially supported by BAFU/FOEN and canton WallisDeployment started Q4/2010
Bernhard BuchliTonio Gsell, Christoph WalserRoman Lim, Mustafa Yucuel
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Current Test Deployment in Valais
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Wireless Infrastructure Randa/Dirruhorn20 km WLAN link from Zermatt to Randa Collaboration with CCES projects: APUNCH + COGEAR (P.
Burlando; ETHZ, S. Loew)Longest low-power wireless sensor network link Uses TinyNode184 and directional antenna Stable operation since 08/2010
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Current and Planned Installation
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- Methodology –
Provide methods and tools for the design of a dependable, long-term sensing infrastructure
in extreme environments.
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Ultra Low-Power Multi-hop NetworkingDozer ultra low-power data gathering system Beacon based, 1-hop synchronized TDMA Optimized for ultra-low duty cycles 0.167% duty-cycle, 0.032mA (@ 30sec beacons)
But in reality: Connectivity can not be guaranteed… Situation dependent transient links (scans/re-connects use energy) Account for long-term loss of connectivity (snow!)
time
jitter
slot 1 slot 2 slot k
data transfercontention
window beacon
[Burri, IPSN2007]
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Challenge: Low Power Operation
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Formal Conformance Test Matthias Woehrle
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Formal Conformance Test
•System in operation •Expected behavior
•Power trace
•Model of observed behavior
•PT
•Model of expected behavior
•Sys
•Verify Reachability in
UPPAAL
•[FORMATS 2009]
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Challenge: Data Integrity• Long term deployment• Up to 19 sensor nodes• TinyOS/Dozer [Burri, IPSN2007]
• Constant rate sampling• < 0.1 MByte/node/day
Matthias Keller
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Data is not Correct-by-DesignArtifacts observed Packet duplicates Packet loss Wrong ordering Variations in received vs. expected packet rates
Necessitates further data cleaning/validation
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Sources of Errors included in ModelData Loss
Packet Duplicates Node Restarts• Cold restart: Power cycle• Warm restart: Watchdog reset
• Shortens packet period• Resets/rolls over certain counters
✗
Retransmission
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1
3
Lost 1-hop ACK
Waitingpackets
✗✗✗
Node reboot
Queue reset Emptyqueue
Clock Drift ρ [ -ρ; +ρ] Directly affects measurement of
• Sampling period T• Contribution to elapsed time te
Indirectly leading to inconsistencies• Time stamp order tp vs. order of
packet generation s
<TT
^ ^
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Model-based Data Validation Case Study
[Keller, IPSN2011]
Reconstructionof correct temporalorder
Validation of correctsystem function
Domain user interested in “correct” data
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- Data Processing –
Develop models and algorithms that process multi-scale data and allow to quantify
mountain cryosphere phenomena.
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GPS Data AnalysisChallenges Processing strategies Optimal duty-cycle strategy Near real-time GPS
processing techniques
Continuous observations of surface motion with low cost GPS Differential L1 carrier phase post-processing and velocity estimation
based on piecewise polynomial fit. Reliable observation of velocities < 2 cm/day
Continuous GPS monitoring reveals velocity changes at high temporal resolution strongly correlated with ambient parameters.
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GPS Testbed
•15 months
•Kinematic positioning error [m]
•Velocity
•GPS positions (unfiltered)
[Limpach, GGL, 2011]
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Measured displacement rate and simulated ground temperature
Stefano Endrizzi
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Measured displacement rate and simulated soil water content
Stefano Endrizzi
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Data Fusion of GPS and InSARIdea Quasi continuous observations of surface motion with low cost GPS SAR satellite measurements cover surface area at certain time
epochs (SAR data processing by GAMMA) Data fusion between continuous GPS velocity field at receiver
locations and InSAR displacement field in LOS between specific time epochs
Ongoing Developments Modeling 3-D surface displacement field based on GPS results Incorporate 1-D InSAR displacement field Increase model accuracy using different filter techniques Development of time dependent surface movement using accurate
DTM Computation of strain and stress fields
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Data Fusion of GPS and InSAR
High resolution GPS stations provide a quasi continuous observation of surface points.
SAR images can be used to extend and improve the surface motion modelling in the area of interest at any point in time.
[Neyer, GGL, 2011]
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Interested in more?
http://www.permasense.ch
• ETH Zurich– Computer Engineering and Networks Lab– Geodesy and Geodynamics Lab
• University of Zurich– Department of Geography
• Gamma SA– SAR Remote Sensing
• BAFU/FOEN– Federal Office for the Environment