1. Simulation for a volcano monitoring network Rainer Mautz ETH Zurich, Institute of Geodesy and Photogrammetry November 22 nd, 2008 Session 9: Natural hazards and risks

2. 1.Motivation 2.Positioning Algorithm 3.Simulation Setup 4.Simulation Results 5.Conclusion & Outlook Contents Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

3. Volcanoes experience pre-eruption surface deformation Reason: internal magma pressure cause surface bulge displacements  direction: upwards and outwards  horizontal: radial pattern up to 10 cm  vertical: uplift of 4 - 6 cm / year (typical)  area: over 10 km 2 goal  spatially distributed position based monitoring system for early warning  positioning for spatio-temoral referencing of additional sensors e.g. seismicity, geothermal, gravity, geomagnetic data 1. Motivation Mount St. Helens, Washington Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

4.  SAR interferometry: update rate 35 days  Geodetic GNSS: expensive, energy consuming Feasibility of a positioning system with deployed location aware sensor nodes 1. Motivation  tiny nodes  low cost  battery-powered  self positioning  ranging capability  high density short range – low power Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

5. 1. Motivation Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook GPS (anchor nodes) tiny nodes inter-node distances Tiny Node GPS Station

6. Principle of Wireless Positioning: Multi-Lateration 2. Positioning Algorithm known node unknown node range measurement Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

7. Iterative Multi-Lateration: 2. Positioning Algorithm Initial anchors Step 1 : Step 2 : Step 3 : becomes anchor Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

8. Ambiguity problem when creating the smallest rigid structure 2. Positioning Algorithm Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

9. Positioning Strategy find 5 fully connected nodes free LS adjustment return refined coordinates and standard variations return local coordinates failed no input ranges achieved input anchor nodes yes volume test ambiguity test assign local coordinates Expansion of minimal structure (iterative multilateration) Merging of Clusters (6-Parameter Transformation) Transformation into a reference system Coarse Positioning anchor nodes available? failed achieved failed achieved Creation of a robust structure 2. Positioning Algorithm Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

10. Object of study: Sakurajima Stratovolcano, summit with three peaks, island 77 km 2 1117 m height extremely active: strombolian, plinian densely populated: Kagoshima, 680.000 on island 7.000 monitored by Sakurajima Volcano Observatory (levelling, EDM, GPS) 3. Simulation Setup Landsat image, created by NASA Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

11. Data provided by Kokusai Kogyo Co. Ltd 3. Simulation Setup Sakurajima Mountain – Digital Surface Model 10 x 10 m grid Central part of volcano Area 2 km x 2.5 km Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

12. Parameters for Simulation ParameterDefault ValueRange Number of tiny nodes400100 – 1000 Number of GPS nodes (anchors)101 – 5 % Maximum range (radio link)400 m200 – 500 m Inter-nodal connectivity104 - 12 Range observation accuracy1 cm0 – 1 m Node distributiongrid / optimised 3. Simulation Setup Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

13. 400 nodes on a 100 m x 125 m grid. 1838 lines of sight with less than 500 m 4. Simulation Results Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

14. Optimised positions. 5024 lines of sight with less than 500 m 4. Simulation Results Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

15. Maximum radio range versus number of positioned nodes 4. Simulation Results Maximum radio range versus number of range measurements Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

16. Number of located nodes in dependency of the number of anchor nodes Number of anchors Anchor fraction Number of located nodes Success rateNumber of ranges 30.8 % 3 1 % 3 51.2 %19148 %3556 102.5 %35488 %4553 153.8 %37193 %4874 205.0 %400100 %5024 4. Simulation Results Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

17. Correlation between Ranging Error and Positioning Error + true deviation ● mean error (as result of adjustment) 4. Simulation Results Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

18. Mean errors of the X- Y- and Z-components sorted by the mean 3D point errors (P) 4. Simulation Results Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

19.  Feasibility of a wireless sensor network shown  Direct line of sight requirement difficult to achieve  10 % GPS equipped nodes required  Error of height component two times larger  Position error ≈ range measurement error Outlook  Precise ranging (cm) between networks to be solved  Protocol & power management 5. Conclusions Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

20. End Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook