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.
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Xsense
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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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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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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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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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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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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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
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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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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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