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Ad-Hoc Wireless Sensor Positioning in Hazardous Areas

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.

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Ad-Hoc Wireless Sensor Positioning in Hazardous Areas

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  1. Ad-Hoc Wireless Sensor Positioning in Hazardous Areas Rainer Mautza, Washington Ochiengb, Hilmar Ingensanda aETH Zurich, Institute of Geodesy and Photogrammetry bImperial College London July 4th, 2008, Session TS THS-1

  2. Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook Contents • Motivation • Positioning Algorithm • Simulation Setup • Simulation Results • Conclusion & Outlook

  3. Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook GPS WLAN 1. Motivation • Volcanoes experience pre-eruption surface deformation • cm – dm over 10 km2 • ↓ • 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

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

  5. Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook 2. Positioning Algorithm Iterative Multi-Lateration: Initialanchors Step1: becomesanchor becomes anchor Step 2: Step 3: becomesanchor

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

  7. Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook 3. Simulation Setup Object of study: Sakurajima Stratovolcano, summit split into three peaks, island with 77 km2 1117 m height Extremely active, densely populated Monitored with levelling, EDM, GPS Landsat image, created by NASA

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

  9. Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook 3. Simulation Setup Parameters for Simulation

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

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

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

  13. Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook 4. Simulation Results Number of located nodes in dependency of the number of anchor nodes

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

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

  16. Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook 5. Conclusions • 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

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

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