AdHoc Wireless Sensor Positioning in Hazardous Areas Rainer

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

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

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

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

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

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

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 km 2 1117 m height Extremely active, densely populated Monitored with levelling, EDM, GPS Landsat image, created by NASA

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

Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook 3. Simulation Setup Parameters for Simulation Parameter Default Value Range Number of WLAN nodes 400 100 – 1000 Number of GPS nodes (anchors) 10 1– 5% 400 m 200 – 500 m 10 4 - 12 1 cm 0– 1 m Maximum range (radio link) Inter-nodal connectivity Range observation accuracy Node distribution grid / optimised

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

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

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

Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook 4. Simulation Results Number of located nodes in dependency of the number of anchor nodes Number of anchors Anchor fraction Number of located nodes Success rate Number of ranges 3 0. 8 % 3 1% 3 5 1. 2 % 191 48 % 3556 10 2. 5 % 354 88 % 4553 15 3. 8 % 371 93 % 4874 20 5. 0 % 400 100 % 5024

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)

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 3 D point errors (P)

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

Motivation Positioning Algorithm Simulation Setup End Simulation Results Conclusions & Outlook