el nah raw y 04 are a based presentation

7/28/2019 El Nah Raw y 04 Are a Based Presentation

1/27

Using Area-based Presentations andMetrics for Localization Systems inWireless LANs

Eiman Elnahrawy, Xiaoyan Li, and Richard MartinDept. of Computer Science, Rutgers University

WLN, November 16th 2004


2/27

Dept. of Computer Science, Rutgers University

WLAN-Based Localization

Localization in indoor environments using802.11 and Fingerprinting

Numerous useful applications

Dual use infrastructure: a huge advantage


3/27


Background: Fingerprinting Localization

Classifiers/matching/learningapproaches

Offline phase:Collect training data (fingerprints)

Fingerprint vectors: [(x,y),SS]

Online phase:Match RSS to existingfingerprints probabilistically orusing a distance metric

[-80,-67,-50]

RSS

(x?,y?)

[(x,y),s1

,s2,s3]

[(x,y),s1,s2,s3]

[(x,y),s1,s2,s3]


4/27


Output:

A single location: the closest/best match

We call such approaches Point-based Localization

Examples:RADAR

Probabilistic approaches[Bahl00, Ladd02, Roos02, Smailagic02, Youssef03, Krishnan04]


5/27


Contributions: Area-based Localization

Returned answer is area/volumelikely to contain the localizedobject

Area is described by a set of tiles

Ability to describe uncertainty

Set of highly possible locations


6/27


Contributions: Area-based Localization

Show that it has critical advantages over point-basedlocalization

Introduce new performance metrics

Present two novel algorithms: SPM and ABP-c

Evaluate our algorithms and compare them againsttraditional point-based approaches

Related Work: different technologies/algorithms[Want92, Priyantha00, Doherty01, Niculescue01, Savvides01,Shang03, He03, Hazas03, Lorincz04]


7/27


Why Area-based?

Noise and systematic errors introduce position uncertainty

Areas improve systems ability to givemeaningful alternatives

A tool for understanding the confidence

Ability to trade Precision (area size) for Accuracy(distance the localized object is from the area)

Direct users in their search

Yields higher overall accuracy

Previous approaches that attempted to use areas only use themas intermediate result output still a single location


8/27


Area-based vs. Single-Location

Object can be in a single room or multiple rooms

Point-based to areasEnclosing circles --much larger

Rectangle? no longer point-based!

0200

0

10

20

30

40

50

60

70

0 200

80


9/27


Outline

Introduction, Motivations, and Related Work

Area-based vs. Point-based localization

Metrics

Localization AlgorithmsSimple Point Matching (SPM)

Area-based Probability (ABP-c)

Interpolated Map Grid (IMG)

Experimental EvaluationConclusion, Ongoing and Future Work


10/27


Performance Metrics

Traditional: Distance error between returned and true position

Return avg, 95th percentile, or full CDF

Does not apply to area-based algorithms!

Does not show accuracy-precision tradeoffs!


11/27


New Metrics: Accuracy Vs. Precision

Tile Accuracy % true tile is returned

Distance Accuracy distance betweentrue tile and returned tiles (sort anduse percentiles to capture distribution)

Precision size of returned area (e.g.,sq.ft.) or % floor size


12/27


Room-Level Metrics

Applications usually operate at the level of rooms

Mapping: divide floor into rooms and map tiles

(Point Room): easy

(Area Room): tricky

Metrics: accuracy-precision

Room Accuracy % true room is the returned roomTop-n rooms Accuracy % true room is among the returnedrooms

Room Precision avg number of returned rooms


13/27


1. Simple Point Matching (SPM)

Build a regular grid of tiles, tile expected fingerprint

Find tiles which fall within a thresholdof RSS for each AP

Eager: start from low threshold (= , 2 , )Threshold is picked based on the standard deviation of thereceived signal

Similar to Maximum Likelihood Estimation

=


14/27


2. Area-Based Probability (ABP-c)

Build a regular grid of tiles, tile expected fingerprint

Using Bayes rule compute likelihood of an RSS matching thefingerprint for each tile

p(Ti|RSS) p(RSS|Ti) . p(Ti)

Return top tiles bounded by an overall probability that theobject lies in the area (Confidence: user-defined)

Confidence Area size


15/27

Dept. of Computer Science, Rutgers University 40

45

50

55

60

65

70

75

80

85

90

x in feet

y

infeet

0 210

140

Measurement At Each Tile Is Expensive!

Interpolated Map Grid: (Surface Fitting)

Goal: Extends original training data to cover the entire floor byderiving an expected fingerprint in each tile

Triangle-based linear interpolation using Delaunay Triangulation

Advantages:

Simple, fast, and efficientInsensitive to the tile size


16/27


Impact of Training on IMG

Both location and number of training samples impactaccuracy of the map, and localization performance

Number of samples has an impact, but not strong!

Little difference going from 30-115, no difference using> 115 training samples

Different strategies [Fixed spacing vs. Averagespacing]: as long as samples are uniformly distributedbut not necessarily uniformly spaced methodology hasno measurable effect


17/27


Experimental Setup

CoRE802.11 data: 286 fingerprints (rooms + hallways)

50 rooms

200x80 feet

4 Access Points


18/27


Area-based Approaches: Accuracy-Precision Tradeoffs

Improving Accuracy worsens Precision (tradeoff)

0 50 100 150 200 25060

65

70

75

80

85

90

95

100

Training data size

%accuracy

Average Overall Room Accuracy

SPM

ABP-50

ABP-75

ABP-95

0 50 100 150 200 2500

2

4

6

8

10

Training data size

%floor

Average Overall Precision

SPM

ABP-50

ABP-75

ABP-95


19/27


0 20 40 60 80 1000

0.2

0.4

0.6

0.8

1

probability

ABP-75: Percentiles' CDF

Minimum

25 PercentileMedian75 PercentileMaximum

0 20 40 60 80 1000

0.2

0.4

0.6

0.8

1

probability


Minimum

25 PercentileMedian75 PercentileMaximum

0 20 40 60 80 1000

0.2

0.4

0.6

0.8

1

distance in feet

probability

SPM: Percentiles' CDF

Minimum25 PercentileMedian75 Percentile

Maximum0 20 40 60 80 100

0

0.2

0.4

0.6

0.8

1

distance in feet

probability


Minimum25 PercentileMedian75 Percentile

Maximum

A Deeper Look Into Accuracy


20/27


Sample Outputs

SPM

ABP-75ABP-50

ABP-95Area expands into the true room

Areas illustrate bias across different dimensions (APs location)


21/27


Comparison With Point-based localization:Evaluated Algorithms

RADARReturn the closest fingerprint to the RSS in the trainingset using Euclidean Distance in signal space (R1)

Averaged RADAR (R2), Gridded RADAR (GR)

Highest ProbabilitySimilar to ABP: a typical approach that uses Bayes rule

but returns the highest probability single location (P1)Averaged Highest Probability (P2), Gridded Highest Probability(GP)


22/27


Comparison With Point-based Localization:

Performance Metrics

Traditional error along with percentiles CDF for

area-based algorithms (min, median, max)

Room-level accuracy


23/27


CDFs for point-based algorithms fall in-between the min, maxCDFs for area-based algorithms

Point-based algorithms perform more or less the same, closelymatching the median CDF of area-based algorithms

Min

Max

Median


24/27


Similar top-room accuracyArea-based algorithms are superior at returning multiple rooms,

yielding higher overall room accuracy

If the true room is missed in point-based algorithms the userhas no clue!


25/27


Conclusion

Area-based algorithms present users a more intuitive way toreason about localization uncertainty

Novel area-based algorithms and performance metrics

Evaluations showed that qualitatively all the algorithms arequite similar in terms of their accuracy

Area-based approaches however direct users in their searchfor the object by returning an ordered set of likely roomsand illustrate confidence


26/27


Ongoing and Future Work

Eiman Elnahrawy, Xiaoyan Li, and Richard P. Martin, The Limits ofLocalization Using Signal Strength: A Comparative Study, In IEEESECON 2004

All algorithms behave the same: there is a fundamental uncertainty

due to environmental effects!

Point-based approaches find the peaks in the PDFs

Area-based approaches explore more of the PDF but cannot narrow it(cannot eliminate the uncertainty)

Useful accuracy, different ways to describe it to users

Future work: additional HW or complex models to improve accuracy


27/27


Thank You

el nah raw y 04 are a based presentation

Documents