The document presents the X-Sense project focused on monitoring alpine mass movements through a combination of wireless sensor networks and process modeling to better understand the impacts of climate change on the cryosphere. It outlines challenges in sensor design and deployment in extreme environments, including data integrity, energy efficiency, and connectivity. Key methodologies include data processing and fusion techniques to analyze GPS and SAR data for accurate monitoring of terrain movement.

1. X-Sense
Monitoring Alpine Mass Movements at Multiple Scales
- Annual Meeting 13 th May 2011 -
Lothar Thiele, Jan Beutel ETH Zurich, Embedded/Wireless
Stephan Gruber University Zurich, Physical Geography
Alain Geiger ETH Zurich, Geodesy and Photogrammetry
Tazio Strozzi, Urs Wegmüller GAMMA SA, SAR Remote Sensing
Hugo Raetzo BAFU/FOEN
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2. [Eiger east-face rockfall, July 2006, images courtesy of Arte Television]
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3. 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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4. Climate change and cryosphere as
(additional) elements of surprise
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5. New Avenues for X-Sense
Detecting and measuring large-scale terrain movement
 Understanding newly-developed slope movements
Current methods: > 100 cm/year
 InSAR measurements 50-100 cm/year
10-50 cm/year
 Manual D-GPS 2-10 cm/year
0-2 cm/year
Sensor challenges
 Complex sensors (combinations
of sensors, different scales)
 Variable data rates
 User interaction (feedback)
 In-network processing
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6. X-Sense Platform
Host Station
processing, fusion, storage
Reference GPS
Moving debris
moving rock slope
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7. 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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8. - Design Approach –
Develop a methodology for the design of
dependable wireless sensor networks
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9. Challenge: The Physical Environment
Lightning, avalanches, rime,
prolonged snow/ice cover, rockfall
Strong daily variation of temperature
 −30 to +40°C
 ∆T ≦ 20°C/hour
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10. Challenge: The Design Approach
Traditional iterative design approach: waterfall-model
Repeated for individual system layers
Testbed [Matthias Woehrle]
 insufficient knowledge of target application / environment
 working on resource limits
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11. Top-down Approach: In-situ Design & Test
Feature-rich Platform
Refined
Behavioral Data Platform
Specification
observe,
experiment,
learn on-site
Flexible in-situ exploration (testbed ≠ real system)
Real sensor data, real environment
Integration with live data management (system of systems)
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12. - Deployment –
Provide a prototype system that allows to
quantify mountain cryosphere phenomena
and can be used in early warning scenarios.
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13. Field Site Selection
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14. Vanessa Wirz
Vanessa Wirz
Location Planning of Measurement Devices
•TerraSAR-X
Field site selection based
•(Sept. 2009, 11 days) on aerial photographs,
satellite-based InSAR
detection and fieldwork
•reference devices
•Dirru rock glacier
•velocity > 1 m/a
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15. Bernhard Buchli
Tonio Gsell, Christoph Walser
New GPS Logger Devices Roman Lim, Mustafa Yucuel
30 GPS logger devices have been
designed and manufactured in
partnership with Art-of-Technology AG
Financially supported by BAFU/FOEN
and canton Wallis
Deployment started Q4/2010
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16. Current Test Deployment in Valais
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17. Wireless Infrastructure Randa/Dirruhorn
20 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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18. Current and Planned Installation
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19. - Methodology –
Provide methods and tools for the design of a
dependable, long-term sensing infrastructure
in extreme environments.
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20. Ultra Low-Power Multi-hop Networking
Dozer ultra low-power data gathering system [Burri, IPSN2007]
 Beacon based, 1-hop synchronized TDMA
 Optimized for ultra-low duty cycles
 0.167% duty-cycle, 0.032mA (@ 30sec beacons)
contention
window data transfer beacon
jitter
slot 1 slot 2 slot k time
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!)
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21. Challenge: Low Power Operation
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22. Formal Conformance Test Matthias Woehrle
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23. Formal Conformance Test
•Model of •Model of
•Verify
observed Reachability in expected
behavior behavior
•Power UPPAAL
trace •PT •Sys
•System in operation •Expected behavior
•[FORMATS 2009]
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24. Challenge: Data Integrity Matthias Keller
• Long term deployment
• Up to 19 sensor nodes
• TinyOS/Dozer [Burri, IPSN2007]
• Constant rate sampling
• < 0.1 MByte/node/day
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25. Data is not Correct-by-Design
Artifacts observed
 Packet duplicates
 Packet loss
 Wrong ordering
 Variations in received vs. expected packet rates
Necessitates further data cleaning/validation
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26. Sources of Errors included in Model
Data Loss ^
Clock Drift ρ [ -ρ; +ρ] ^
Node reboot Directly affects measurement of
• Sampling period T
✗ • Contribution to elapsed time te
✗✗
Indirectly leading to inconsistencies
Waiting Queue reset Empty
packets queue
• Time stamp order tp vs. order of
packet generation s
Packet Duplicates Node Restarts
• Cold restart: Power cycle
2 Lost 1-hop ACK
✗ • Warm restart: Watchdog reset
1
T <T
3 • Shortens packet period
Retransmission • Resets/rolls over certain counters
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27. Model-based Data Validation Case Study
Reconstruction
of correct temporal
order
Validation of correct
system function
Domain user
interested
in “correct” data
[Keller, IPSN2011]
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28. - Data Processing –
Develop models and algorithms that process
multi-scale data and allow to quantify
mountain cryosphere phenomena.
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29. GPS Data Analysis
Challenges
 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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30. GPS Testbed
•Kinematic positioning error [m]
•GPS positions (unfiltered)
•Velocity
•15 months [Limpach, GGL, 2011]
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31. Measured displacement rate and
simulated ground temperature
Stefano Endrizzi
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32. Measured displacement rate and
simulated soil water content
Stefano Endrizzi
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33. Data Fusion of GPS and InSAR
Idea
 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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34. 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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35. • 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
Interested in more?
http://www.permasense.ch
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