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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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  1. 1. X-SenseMonitoring Alpine Mass Movements at Multiple Scales- Annual Meeting 13 th 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 1
  2. 2. [Eiger east-face rockfall, July 2006, images courtesy of Arte Television] 2
  3. 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 3
  4. 4. Climate change and cryosphere as(additional) elements of surprise 4
  5. 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 5
  6. 6. X-Sense Platform Host Station processing, fusion, storage Reference GPS Moving debris moving rock slope 6
  7. 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 7
  8. 8. - Design Approach –Develop a methodology for the design of dependable wireless sensor networks 8
  9. 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 9
  10. 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 10
  11. 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) 11
  12. 12. - Deployment – Provide a prototype system that allows to quantify mountain cryosphere phenomenaand can be used in early warning scenarios. 12
  13. 13. Field Site Selection 13
  14. 14. Vanessa Wirz Vanessa WirzLocation 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 14
  15. 15. Bernhard Buchli Tonio Gsell, Christoph WalserNew 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 15
  16. 16. Current Test Deployment in Valais 16
  17. 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 17
  18. 18. Current and Planned Installation 18
  19. 19. - Methodology –Provide methods and tools for the design of adependable, long-term sensing infrastructure in extreme environments. 19
  20. 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!) 20
  21. 21. Challenge: Low Power Operation 21
  22. 22. Formal Conformance Test Matthias Woehrle 22
  23. 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] 23
  24. 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 24
  25. 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 25
  26. 26. Sources of Errors included in ModelData Loss ^ Clock Drift ρ [ -ρ; +ρ] ^ Node reboot Directly affects measurement of • Sampling period T ✗ • Contribution to elapsed time te ✗✗ Indirectly leading to inconsistenciesWaiting Queue reset Emptypackets queue • Time stamp order tp vs. order of packet generation sPacket 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 26
  27. 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] 27
  28. 28. - Data Processing –Develop models and algorithms that process multi-scale data and allow to quantify mountain cryosphere phenomena. 28
  29. 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/dayContinuous GPS monitoring reveals velocity changes at hightemporal resolution strongly correlated with ambient parameters. 29
  30. 30. GPS Testbed •Kinematic positioning error [m] •GPS positions (unfiltered) •Velocity •15 months [Limpach, GGL, 2011] 30
  31. 31. Measured displacement rate and simulated ground temperature Stefano Endrizzi 31
  32. 32. Measured displacement rate and simulated soil water content Stefano Endrizzi 32
  33. 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 33
  34. 34. Data Fusion of GPS and InSARHigh resolution GPSstations provide aquasi continuousobservation of surfacepoints.SAR images can beused to extend andimprove the surfacemotion modelling inthe area of interest atany point in time. [Neyer, GGL, 2011] 34
  35. 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 EnvironmentInterested in more?http://www.permasense.ch 35