Ad-Hoc Wireless Sensor Positioning in Hazardous Areas

Rainer Mautza, Washington Ochiengb, Hilmar Ingensanda

aETH Zurich, Institute of Geodesy and PhotogrammetrybImperial College London

July 4th, 2008, Session TS THS-1

1. Motivation

2. Positioning Algorithm

3. Simulation Setup

4. Simulation Results

5. Conclusion & Outlook

Contents

Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

Volcanoes experience pre-eruption surface deformationcm – 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

1. Motivation

GPS

WLAN

Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

Principle of Wireless Positioning: Multi-Lateration

2. Positioning Algorithm

known node

unknown node

range measurement

Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

Iterative Multi-Lateration:

2. Positioning Algorithm

Initial anchors

Step 1:

Step 2:

Step 3:

becomes anchor

becomes anchor

becomes anchor

Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

Positioning Strategy

find 5 fully connected

nodes

free LS adjustment

return refined coordinatesand standard variations

return local coordinates

failed

no

input ranges

achieved

input anchor nodesyes

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

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

3. Simulation Setup

Landsat image, created by NASA

Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

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

Parameters for Simulation

Parameter Default Value Range

Number of WLAN nodes 400 100 – 1000

Number of GPS nodes (anchors) 10 1 – 5 %

Maximum range (radio link) 400 m 200 – 500 m

Inter-nodal connectivity 10 4 - 12

Range observation accuracy 1 cm 0 – 1 m

Node distribution grid / optimised

3. Simulation Setup

Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

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

Optimised positions. 5024 lines of sight with less than 500 m

4. Simulation Results

Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

200 250 300 350 400 450 5000

100

200

300

400

maximal range [m]

conn

ecte

d no

des

200 250 300 350 400 450 5005

10

15

20

25

30

maximal range [m]

rang

es p

er n

ode

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

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

4. Simulation Results

Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

Correlation between Ranging Error and Positioning Error

0 0.2 0.4 0.6 0.8 10

0.5

1

1.5

2

2.5

3

3.5

4

noise [m]

mea

n er

rors

/ d

evia

tion

[m]

+ true deviation● mean error (as result of adjustment)

4. Simulation Results

Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook

0 100 200 300 4000

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

node number

mea

n er

ror

[m]

XYZP

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

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

End

Motivation Positioning Algorithm Simulation Setup Simulation Results Conclusions & Outlook