1 / 17

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.

phyllis-weeks
Download Presentation

Ad-Hoc Wireless Sensor Positioning in Hazardous Areas

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related