Transcript Evaluating Wi-Fi Location Estimation Technique for Indoor Navigation Roshmi Bhaumik Outline        Motivation Problem Definition Related work Our Contribution Experiment Case Study Conclusion and Future work.

Evaluating Wi-Fi Location
Estimation Technique for
Indoor Navigation
Roshmi Bhaumik
Outline
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Motivation
Problem Definition
Related work
Our Contribution
Experiment
Case Study
Conclusion and Future work
Motivation
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Wireless communications
• Chaska city wide Wi-Fi available from July 2004
• Minneapolis city wide Wi-Fi proposal , 2006
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Indoor location aware applications
• Indoor Navigation aid for visually impaired
• Navigation through Exhibition Halls and
Museums
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Challenges of indoor Wi-Fi positioning
• Noisy characteristics of wireless channels
• Multi-path fading
Motivation(2)
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Evaluating Wi-Fi based Indoor Location
Estimation techniques for indoor
navigation
Location aware
solution (client)
Location
based Server
Core Common
Technology
Indoor Positioning
Technology
System Architecture of Location Based Service
Outline
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Motivation
Problem Definition
Related work
Our Contribution
Experiment
Case Study
Conclusion and Future work
Problem Definition
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Given
• Wi-Fi Localization scheme
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Evaluate
• Given localization scheme for indoor
navigation system
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Objective
• Fulfilling accuracy requirements of navigation
application
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Constraints
• Using existing Wi-Fi Access Points (APs)
• Experiment carried out in typical building
Outline
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Motivation
Problem Definition
Related work
Our Contribution
Experiment
Case Study
Conclusion and Future work
Related Work
Requirements of positioning for
indoor navigation:
1.
Accuracy
2.
Integrity- issue alarm in case
of large estimation errors
3.
Availability (Coverage)
4.
Continuity of service (Location
Estimation response time)
(Swiss Federal Institute of
Technology 2003)
Related Work (2)
Evaluation criteria:
 Location labeling
 System performance
 Architecture
 Cost
Hightower et al, 2001 [14]
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Algorithm vs. accuracy
Performance vs. accuracy tradeoff
Fault-tolerance
P. Prasithsangaree et al, 2002 [11]
Outline
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Motivation
Problem Definition
Related work
Our Contribution
Experiment
Case Study
Conclusion and Future work
Our Contribution
Limitations of related work:
 Did not consider effect of different
motion patterns on accuracy
Our Contribution:
 Evaluate the positioning accuracy
with different types of movement
 Other lessons learnt
 Case Study: Indoor navigation aid
for the visually impaired
Outline
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Motivation
Problem Definition
Related work
Our Contribution
Experiment
Case Study
Conclusion and Future work
Experiment Design
Floor Map
Ekahau
Software
Calibration
Track
controlled
movement
Location
Estimates
Accuracy
Calculations
1.
2.
3.
4.
5.
Stationary
Stop and Go
Smooth about a point
Smooth uniform motion
Change in direction
Constant parameters:
•Device Scan Interval (500msec)
•Accurate Mode
Variable parameters:
•Location Update Interval
(500 - 6000msec)
Error measured= Euclidean distance between gold
standard and estimated location
Movements for a point
A
A
B
A
< =3ft
Slow motion
Quick motion
Static
B
<= 3ft
C
1) Stationary:
Gold standard= A
10 Readings taken after
2min
2)Stop and Go
Gold standard= B
10 Readings taken within
30 sec
3)Smooth about a point
Gold Standard= B
Start taking readings at A
and end at C
Smooth Motion on Tracking Rail
Slow motion
Tracking rail
4) Smooth uniform motion
Gold standard= points on the tracking rail
Readings taken using the Ekahau Accuracy
Tool
Error calculation is done by the tool
Direction Change
5) Changes in direction
a) Turning back 180 degrees on the
path
A
B
A
B
Gold Standard = Point B
Readings taken at B after turning
b) Intersections with ambiguous
signals
Gold Standard = Point B
Readings taken at B for all types of
movement for a point
Slow motion
Tracking rail
Turn back
Elliot Hall First Floor
Elliot Hall Third Floor
Ekahau Positioning Software
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Ekahau Client (on every mobile
device)
• Retrieves signals from visible Access Points
through the network cards
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Ekahau Manager
• For site calibration, adding logical areas,
tracking and accuracy analysis
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Ekahau Positioning Engine (EPE)
• Server stores calibration data
• Calculate location estimates
• Algorithm: Maximum A Posteriori Estimate
using Bayes Rule
• Applications retrieve positioning data
through YAX protocol /Java SDK
Results
Comparing Types of Motion: Floor 1
Actual X,Y
Stationary
- Error (ft)
Smooth slow
about point -Error
(ft)
Averaged X,Y –
Error (ft)
Stop and go Error (ft)
703,79
0.78
7.0
6.97
9.79
703,848
6.33
11.82
6.94
11.06
703,969
11.58
18.43
15.55
11.73
703,1268
3.17
10.92
9.36
10.68
702,1452
5.53
5.64
4.50
5.98
457,1419
2.25
3.5
3.22
4.37
780,1485
6.89
6.41
5.34
26.50
Average
5.22
9.10
7.41
11.44
Comparing Types of Motion: Floor 1
Actual
Stop go
Smooth Avg
Stationary
Comparing Types of Motion: Floor 3
Average Accuracy variation with
type of motion: (1, 2, 3 &5a)
9.7,9.1,10,9.2
Actual
Smooth -5.8
Stationary -5.5
Stop n Go- 10.2
Turn around – 6.8
7.2,7.2,17.5,7.7
2.3,2.3,5.6
0.7,3.3,0.7,4.1
6.5,4.8,6.2,6.2
Smooth uniform motion: Floor 1
•Use Ekahau Accuracy
Tool
•Smooth uniform
motion in a straight line
along the tracking rail
•Error vector shown in
red color
Floor
Elliot-1st
units in ft
Min
0.4
Max
26.2
Average 90%
8.8
16.8
Med
7.6
Smooth uniform motion: Floor 3
Readings taken
using Ekahau
Accuracy Tool
Floor
Elliot -3rd
units in ft
Min
0.8
Max
16.8
Average 90%
7.2
12.4
Med
8.1
Ambiguous Intersection
All types of
movement for a point
are considered
Average Error worse
than 12 ft for this
specific location
Actual X,Y
Stationary
- Error (ft)
Smooth slow about
point -Error (ft)
Averaged X,Y –
Error (ft)
Stop and go -Error
(ft)
703,969
11.58
18.43
15.55
11.73
Analysis
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Accuracy requirements are met for the
listed types of movements except :
• Stop and go
• Direction change at ambiguous intersection
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System does not respond to quick changes
and error increases is due to
overestimation
Ambiguous signal patterns cause large
errors
Other lessons learned
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Calibration
• Stability: Stable over 4 months
• Transferability: Calibration done with one
client, used to track any other supported
clients with same accuracy
• Tested with Cisco, Orinoco and Dell’s built-in
WLAN cards and Toshiba PDA
• Calibration is transferable as EPE performs
normalization of RSSI* values from different
network cards and devices
*Radio Signal Strength Indicator
Other lessons learned
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Tracking on multiple floors
• Adjacent floor maps linked at certain
positions (e.g. start of staircase)
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Latency (5-15 sec) depends on device
speed.
• Connection points between floors are
not specified
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Correct floor detected with greater latency
(2 min)
• Floors are significant barriers to signal
propagation
• Points in adjacent floors have different
signal patterns
Other Lessons learned
*Location Update Interval
12
10
8
Accurate
6
Real Time
4
2
Location Update Interval (ms)
6000
5500
5000
4500
4000
3500
3000
2500
2000
1500
1000
0
500
Average Error (ft)
Accurate mode meets
accuracy requirement and
latency is 5 sec
Variation of LUI* does
not completely control the
effect of history
At low RSSI, changes in
signal strength with
distance is very small.
This causes signal aliasing
and reduces accuracy
Average Error with LUI
Outline
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Motivation
Problem Definition
Related work
Our Contribution
Experiment
Case Study
Conclusion and Future work
Case Study:
Indoor Navigation Aid for the Visually Impaired
We used EPE to build an Indoor Guidance System
(IGS) meant to help the visually impaired to find
their way inside buildings
BUILDING DATABASE
Spatial data: X,Y
CLIENT
USER
Location Aware
Application
Audio/ Visual Output
Wi-Fi Sensors
Attribute data:
Physical & Logical
spaces
LOCATION SERVER
(EPE)
Positioning Model:
floor maps and
calibration data
Information Flow Diagram
Existing Work
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Building Database
Data Entry interface: building and
floor data
Audio/Visual output : a list of
surrounding features, orientation and
distance from current user position
User input needed to get current user
position
My Additions
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Infer current user location in building
database coordinates
Multiple floor tracking and transferable
calibration
Improve accuracy and latency of location
estimate
• Background thread to collect location & error
estimates
• Average location estimates over specified time
intervals
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Add location buoys spaced 15 ft
• Reduce computation
• Describe the surrounding with finer granularity
IGS screen shot
Application Design Guidelines
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During calibration, sample points should
be rejected if RSSI is below certain
threshold
Averaging location estimates over
optimum time interval is better than
getting a single instantaneous estimate
Accurate mode gives better location
estimates for normal walking speeds
Accuracy will vary depending on the type
of movement
Conclusions and Future Work
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EPE location estimation scheme performs
well for an indoor navigation application
but some areas can be improved:
Calibration stability and transferability is
good
Tracking over multiple floors works well
Accuracy is good for smooth movement in
a straight line and stationary state
Accuracy is poor when signal strengths are
low
Conclusions and Future work(2)
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Accuracy is poor if the path of movement has a
number of intersections, specially with
ambiguous signal pattern
• Using directional APs for asymmetric coverage
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Accuracy is poor for stop and go motion
• Using some method to detect still and moving state
• Use above to choose the transition probabilities
• Related work –LOCADIO[7]
Further tuning of the application can achieve
better accuracy for indoor navigation
Future work:
 Implement own location detection scheme.
 Incorporate suggested improvements
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Keywords
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Wi-Fi : Wireless fidelity, IEEE802.11b
APs: Access Points, Within the range (~50ft) of an AP, the
wireless end-user has a full network connection with the benefit of
mobility.
RSSI : Received Signal Strength Indicator
Bayesian networks :A probabilistic graphical model . The nodes
represent variables and directed arcs represent conditional
dependencies between variables.
Stochastic model :A mathematical model which contains random
(stochastic) components or inputs; consequently, for any specified
input scenario, the corresponding model output variables are
known only in terms of probability distributions in contrast to a
deterministic model
Machine learning :A method for creating computer programs by
the analysis of data sets.
IGS: Indoor Guidance system, a navigation aid developed
for the Low Vision Lab, Psychology department, UMN
References
Papers:
1. P. Bahl and V. N. Padmanabhan, “RADAR: An In-Building RF-Based User Location and Tracking System,” Proceedings of
IEEE Infocom 2000, March 2000, pp. 775–784
2. P. Myllymaki, T. Roos, H. Tirri, P. Misikangas, and J. Sievanen, “A Probabilistic Approach to WLAN User Location
Estimation,” Proceedings of the 3rd IEEE Workshop on Wireless LANs, September 2001, pp. 59–69.
3. R. Battiti, A. Villani, and T. Le Nhat, “Neural network models for intelligent networks: deriving the location from signal
patterns,” in Proceedings of AINS2002, (UCLA), May 2002.
4. Castro, P., et al. A Probabilistic Room Location Service for Wireless Networked Environments. in Ubicomp 2001.
5. Ladd, A.M., et al. Robotics-Based Location Sensing using Wireless Ethernet. in Eighth International Conference on Mobile
Computing and Networking. 2002.
6. John Krumm, “Probabilistic Inferencing for Location”, Proceedings of the 2003
Workshop on Location-Aware Computing, October 2003.
7. John Krumm and Eric Horvitz, "LOCADIO: Inferring Motion and Location from Wi-Fi Signal Strengths", First Annual
International Conference on Mobile and Ubiquitous Systems: Networking and Services (Mobiquitous 2004), August 2004
8. D. Fox, J. Hightower, H. Kautz, L. Liao, and D. Patterson. "Bayesian techniques for location estimation" in Proceedings of
The 2003 Workshop on Location-Aware Computing, October2003.
9. L. R. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proc. of the IEEE,
Vol.77, No.2 pp.257--286, 1989.
10. J.A.Tauber.Indoor Location Systems for PervasiveComputing http://theory.lcs.mit.edu/~josh/papers/location.pdf
11. P. Prasithsangaree, P. Krishnamurthy, and P. K. Chrysanthis, "On Indoor Position
Location With Wireless LANs ," The 13th IEEE International Symposium on Personal, Indoor, and Mobile Radio
Communications (PIMRC 2002), Lisbon, Portugal, September 2002.
12. http://www.dinf.ne.jp/doc/english/Us_Eu/conf/csun_98/csun98_008.htm
13. http://topo.epfl.ch/publications/paper_IAIN03_epfl.pdf
14. Jeffrey Hightower and Gaetano Borriello.Location systems for ubiquitous computing.IEEE Computer,August2001.
Slides:
P1.”Graphical Models on Manhattan: A probabilistic approach to mobile device positioning” presented by Petri Myllymäki and
Henry Tirri
P2. faculty.cs.tamu.edu/dzsong/teaching/ fall2004/netbot/Yutu_Liu_Robot.ppt