xsense

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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