1. Ad-Hoc Wireless Sensor Positioning in Hazardous Areas Rainer Mautz a, Washington Ochieng b, Hilmar Ingensand a a ETH Zurich, Institute of Geodesy and Photogrammetry b Imperial College London July 4th, 2008, Session TS THS-1

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 cm – dm over 10 km 2 ↓ Spatially distributed monitoring for early warning system  SAR interferometry: update rate 35 days  Geodetic GNSS: expensive, energy consuming Feasibility of a WLAN positioning system with densely deployed location aware nodes 1. Motivation GPS WLAN Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

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

5. 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

6. 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

7. Object of study: Sakurajima Stratovolcano, summit split into three peaks, island with 77 km 2 1117 m height Extremely active, densely populated Monitored with levelling, EDM, GPS 3. Simulation Setup Landsat image, created by NASA Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

8. 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

9. Parameters for Simulation ParameterDefault ValueRange Number of WLAN 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

10. 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

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

12. 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

13. 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

14. 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

15. 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

16.  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

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

18. Ambiguity problem when creating the smallest rigid structure 2. Positioning Algorithm