Location Tracking1 Multifloor tracking algorithms in Wireless Sensor Networks Devjani Sinha Masters...

Location Tracking 1

Multifloor tracking algorithms in Wireless Sensor Networks

Devjani SinhaMasters Project

University of Colorado at Colorado Springs

12/3/2005 Devjani Location Tracking 2

Why Location Tracking is Useful? Adapted from Motetrack presentation

Assist Firefighters in Search/Rescue inside building Often cannot see because of heavy smoke + are unfamiliar with

building Use wireless sensors (badge/beacon); GPS does not work in

buildings Can greatly benefit from a heads-up display to track their

location and monitor safe exit routes Chicago City Council … all buildings more than 80 feet tall must

submit electronic floor plans [Forefront, Fall 2003] Incident commander can better coordinate rescuers from

command post


Related work

Motetrack (Harvard Lorincz and Matt Welsh) TinyOS/Mote based. 3D location tracking using radio signal information Distributed/reference signature based. Thus more reliable. No Multi floor implementation

Spot-on (Washington, Jeffrey Hightower and Gaetano Borriello/XeroxParc, Roy Want)

RFID, 3D location Tracking Requires customized special software centralized No Simulation yet for Multi Floor

FRSN Location Tracking TinyOS/Mote based. Multi-Floor Simulation 3D location tracking using radio signal information


Research Goals

Single Floor Location Tracking Use Jeff Rupp's Obstructed Radio

Model (2D) 2D Hill climbing algorithm

Multi Floor Location Tracking (3D) Extend Obstructed Radio Model to 3D Extend Hill climbing algorithm to 3D

Analyze the performance and impact factors such as scaling, height, initial sensor sets

Develop tool to visualize the results.


Why Motes/TinyOS seems to be the right platform

MOTES are small in size Easy to embed in environment and equipment

MOTES can operate off of battery + it is low power Resilient to infrastructure failure

TinyOS is a well established platform Used by over 150 research groups worldwide Easy to integrate new sensors/actuators

Mica2 mote


Modeling and Simulation

TinyOS – mote operating system

TOSSIM - Simulate TinyOS mote network

TinyViz – visual TOSSIM Standard Java application Uses a ‘plug-in’ architecture to allow for expansion Wide array of existing plugins Easy to expand


Obstructed Radio Model Plugin

Authored by Jeff Rupp, UCCS Plug-in is based in the Radio Model done by Nelson Lee

at Berkeley Assumes 60dB equates to a maximum bit error rate Radio signals are obstructed by varying amounts by

different materials Loss in free space over distance walls presented low attenuation, about 3-12dB


Multi Floor model assumptions

For sake of simplicity, the following assumptions were made:

The layout of each floor is identical. Every floor is setup with equal number of Beacon

nodes 10ft above the floor. The mote layout is identical for each floor.

The floor height is set at 10 ft. The attenuation of the floor/ceiling is assumed to be

20dB. Cubicle attenuation is assumed to be 15dB Outer Wall attenuation is assumed to be 35dB


Multi floor Setup in the GUI

symbol is beacon sensor node. The label is sensor ID.

Here small rooms has one sensor, large room has two. The hallway has 6 sensors. The top one is the sink node which collecting the sensor data.


Hill Climbing Algorithm

Legend:

Red square is actual target location.

4 purple/grey dots are sensors with strongest signals.


Hill Climbing Algorithm

x

Based on the initial sensor set, an estimated location, x, is computed.

Through perturbation, four neighboring locations from x is calculated and the one with closest estimated signal strengths will be chosen for next round.


Responder Position in GUI

Here the red squares are randomly generated firefighter locations.

The overlay green squares are estimated locations.


Performance: Effect of Scaling Factors

Identical results for SF=1 and SF=2

SF=2 results in error and variance in tracking

1.19 1.19

35%

2.29

35%

2.29

0.0

0.5

1.0

1.5

2.0

2.5

Tracking ErrAvg (ft)

Tracking ErrVar (ft)

%Unconverged

Single Flr, Z Vary,Top 4, SF=1


2.61

12.80

40%5.31

104.69

47%

0

20

40

60

80

100

120



% Unconverged

Multi Flr, Z Vary,Top 4, SF=1


Single Floor Multi Floor


Varying Z value for responder

1.03

2.12

25%

1.19

35%

2.29

0.0

0.5

1.0

1.5

2.0

2.5



% Unconverged

Single Flr, Z Fixed,Top 4, SF=1


Marginal Differences


Top4 vs. Top3 motes

1.03

2.12

25%

2.29

12.43

20%

0

2

4

6

8

10

12

14

Tracking Err Avg(ft)

Tracking Err Var(ft)

% Unconverged



2.61

12.80

40%

4.63

35.84

0%

0

5

10

15

20

25

30

35

40



% Unconverged



Top3 results in error and variance in tracking

Top3 results in zero convergence issues


Conclusions

This concept can be developed using small, inexpensive and low-power devices

Using radio signal information alone, it is possible to determine the location of a roaming node at close to meter-level accuracy.

First Responder Sensor Network software provides an attractive solution to the critical problem of indoor location tracking.

The multi floor model is quite robust to variations in z co-ordinate of responder.

Using top 4 beacon motes in the algorithm gives more accurate results


Future Work

Incorporate Java 3D API in TinyViz 2D Mote Network conversion to 3D Multi Floor display of Responder Positions Implementation of Multi Floor FRSN


Key References

Konrad Lorincz and Li Li, “MoteTrack: A Robust, Decentralized Approach to RF-Based Location Tracking,” Proceedings of the International Workshop on Location and Context-Awareness (LoCA 2005) at Pervasive 2005, May 2005.

“MoteTrack: An Indoor Location Detection System for Sensor Networks”, Konrad Lorincz and Li Li, Harvard University. (http://www.eecs.harvard.edu/~konrad/projects/motetrack/)

“Radio Signal Obstruction Plug-in for TinyViz” by Jeff Rupp, CS526 from UCCS CO 80933-7150, Fall 2003.

“TOSSIM: A Simulator for TinyOS Networks” by Philip Levis and Nelson Lee, (Version 1.0 - June 26, 2003), September 17, 2003.


Questions?

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