Fingerprinting-based Indoor Positioning

Fingerprinting-based Indoor Positioning

Dr. R. Montoliu, Dr J. Torres-Sospedra, Dr. A. Pérez-Navarro, Dr. J. Conesa, Dr. O. Belmonte

The main objective of this tutorial is:

● The students will learn how to develop a fingerprint-based localization algorithm from zero avoiding the same mistakes we faced when we started.

Professors:Dr. R. Montoliu

Dr. J. Torres-Sospedra

Dr. A. Pérez-Navarro

Dr. J. Conesa

Dr. O. Belmonte

Phd student G. Mendoza

Table of contentsPart Time Content

1 17:00-17:15 Introduction to fingerprinting

2 17:15-17:30 Theoretical background

3 17:30-17:45 The training step

4 17:45-18:15 Time to perform the training step

5 18:15-18:30 The operational step

6 18:30-18:45 Time to play with operational source code

7 18:45-19:00 How to improve the ILS

8 19:00-19:15 Time to improve the ILS

9 19:15-19:30 Awards

Part 1: Introduction to fingerprinting

Introduction to fingerprinting● Four types of indoor localization algorithms:

○ Deploy beacons○ Use existing beacons and the position of the beacons is known○ Use existing beacons and the position of the beacons is unknown○ No use beacons

1. Deploy beacons

1. Deploy beacons

https://www.kinvey.com/wp-content/uploads/2014/05/beacon.jpg



1. Deploy beacons

Beacon ID Localization

1 [lat, long]

... [lat, long]

N [lat, long]

1. Deploy beacons

http://www.clipartkid.com/images/679/pedestrian-20clipart-clipart-panda-free-clipart-images-gz3PIn-clipart.png



1. Deploy beacons

RSSI: Received Signal Strength Indication

1. Deploy beacons

● Simplest solution:○ The desired location is the one of the closest beacon

1. Deploy beacons

● Better solution:○ Apply trilateration

1. Deploy beacons

● Better solution:○ Apply trilateration

1. Deploy beacons

● The position of the beacons is known○ Trilateration techniques can be applied

● High accuracy can be obtained

https://pixabay.com/static/uploads/photo/2013/07/13/13/24/fist-160957_640.png https://pixabay.com/static/uploads/photo/2013/07/13/13/24/fist-160958_640.png

● Expensive● A lot of beacons can be needed in big scenarios

https://pixabay.com/static/uploads/photo/2013/07/13/13/24/fist-160957_640.png




2. Use existing beacons (knowing the position)

● Apply trilateration


● Apply trilateration

Note that the existing beacons could be deployed for providing

some services, but not localization.


● The position of the beacons is known○ Trilateration techniques can be applied

● High accuracy can be obtained● Cheap, since we are using already deployed devices

● High dependence of the existing beacons○ Most of the times, without localization purpose

● Low accuracy if there are a few number of beacons

3. Use existing beacons without knowing the position

● Fingerprinting


● Fingerprinting


● Cheap, since we are using already deployed devices

● High dependence of the existing beacons● Low accuracy if there are a few number of beacons● Less accuracy than previous cases● The position of the beacons is unknown

○ Trilateration techniques can not be applied

4. Without using beacons

● Magnetic field based

4. Without using beacons

● Magnetic field disturbances are constant● No devices are needed● The cheapest solution

● Less discriminative power than WIFI fingerprinting● Not easy solution

○ Algorithms are in “work in progress” state

A brief introduction to Fingerprinting based methods● Two main steps:

○ Training step○ Operational step

● Beacons are WIFI AP○ Position is unknown

The training step

The training step

MAC RSII

xx-xx-xx-xx-xx-xx -30db

... -80db


Latitude Longitude

The training step

MAC RSII


... -80db


Latitude Longitude

MAC RSII


... -30db


Latitude Longitude

The training step

Training Fingerprintsdatabase

The training step● UJIIndorloc database:

○ https://archive.ics.uci.edu/ml/datasets/UJIIndoorLoc

● Some data:○ 21048 fingerprints○ 520 different MACs○ 4 multifloor building

● Platform to share results

https://archive.ics.uci.edu/ml/datasets/UJIIndoorLoc

https://archive.ics.uci.edu/ml/datasets/UJIIndoorLoc

The training step● UJIIndorloc database:

○ Joaquín Torres-Sospedra, Raúl Montoliu, Adolfo Martínez-Usó, Tomar J. Arnau, Joan P.

Avariento, Mauri Benedito-Bordonau, Joaquín Huerta “UJIIndoorLoc: A New Multi-building and

Multi-floor Database for WLAN Fingerprint-based Indoor Localization Problems” In

Proceedings of the Fifth International Conference on Indoor Positioning and Indoor Navigation, 2014.

MAC001 MAC002 ... MAC520 Longitude Latitude Floor Building User Phone Timestamp

The operational step


MAC RSII


... -80db



Training Fingerprintsdatabase

Indoor Localization

System

MAC RSII


... -80db


The operational step● A kNN based algorithm is used to obtain the localization

● It will be explained after in this tutorial

Discrete vs continuous positioning● This tutorial only cover discrete localization

○ The system only use the information captured in a particular time moment to estimate the location.

● Continuos positioning:○ The system use the last information captured and some historic data.○ Tracking.

Part 2: Theoretical background

Waves● Wifi are electromagnetic waves and behave like them● They are affected by the following phenomena:

○ Reflection○ Refraction○ Diffraction○ Absorption

Electromagnetic waves

By Витольд Муратов (Own work) [Public domain], via Wikimedia Commons

Electromagnetic Spectrum

Per Inductiveload, NASA [GFDL (http://www.gnu.org/copyleft/fdl.html) o CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0/)], via la Wikimedia Commons





WiFi:2,4 GHz5 GHz

Reflection and refraction

θi

θr

θt Separation

interface

n1

n2

Reflection and refraction

θi

θr

θt Separation

interface

n1

n2

Fresnel coefficients

Fresnel coefficients(for parallel polarization)

Stationary waves

At the end will always be a minimum.

Geometrical attenuation

r1

r2

Difraction

Per Bbkkk (Treball propi) [GFDL (http://www.gnu.org/copyleft/fdl.html) o CC BY-SA 4.0-3.0-2.5-2.0-1.0 (http://creativecommons.org/licenses/by-sa/4.0-3.0-2.5-2.0-1.0)], via la Wikimedia Commons

AbsorptionIntensity

Attenuation coefficient

Penetration depth

frequency

magnetic permeability

conductivity

Example of real situation

Absorption

A.P.

�


Difraction

A.P.

�


Reflection

A.P.

�

Number of people within a room

Number of people within a room

Bodies absorb WiFi radiation

Specific Absorption Rate (SAR)Power absorved

mass

Also exists the Whole Body SAR (WBSAR)

Absorption cross section

Absorption Cross Section Power absorved

Power density in the incident wave

Silhouette area of a perfectly-absorbing surface thatwould absorb the same power as the loading object under discussion

Dependence on tissue

Source: S. Gabriel, R. W. Lau, and C. Gabriel, “The dielectric properties of biological tissues:III. parametric models for the dielectric spectrum of tissues,” Physics in Medicine andBiology, vol. 41, no. 11, p. 2271, 1996.

Frequency of 1 cm penetration

Dry Skin 5.2 GHz

Infiltrated Fat 9.5 GHz

Muscle 4.7 GHz

Source: Melia, Gregory (2013) Electromagnetic Absorption by the Human Body from 1 - 15 GHz. PhD thesis, University of York.

Dependence on position


Dependence on clothes


Results


All body mean approximation

Source: S. Garcia-Villalonga and A. Perez-Navarro, "Influence of human absorption of Wi-Fi signal in indoor positioning with Wi-Fi fingerprinting," Indoor Positioning and Indoor Navigation (IPIN), 2015 International Conference on, Banff, AB, 2015, pp. 1-10.doi: 10.1109/IPIN.2015.7346778

Example

● 83 points● 8.23 m of distance

between them● Few-bodies time (9 AM)

● 115 points● 42 inside shops● 73 in corridors● 5.56 m of minimum distance

RadioMap

Test

Example

9 A.M. 4 P.M.

Example

● Results with Airplace

● w-kNN algorithm

Summary● WiFi is an electromagnetic wave: microwaves.● It is affected by:

○ Reflection○ Refraction○ Absorption○ Diffraction

Changes in the environment can affect values of WiFi measured

● Life tissues absorb microwaves.● Different tissues have different absorptions

Changes in the number of people affect values of WiFi measured

● Different frequencies are affected in different ways.

Part 3: The training step

https://www.flickr.com/photos/shonk/57302289https://www.flickr.com/photos/shonk/57302289

Reference Data

Reference Data

Reference Data

Reference Data

RSSI1, RSSI2, RSSI3, RSSI4, RSSI5

Reference Data

Strong, Medium, Medium, Medium, Weak

Medium, Strong, Strong, Medium, Medium

Weak, Medium, Medium, Strong, Medium

Weak, Weak, Medium, Medium, Strong

Reference Data


Medium, Strong, Strong, Medium, N/A

Medium, Strong, Medium, Medium, Medium

Strong, Strong, Strong, Medium, Medium

Reference Data

https://pixabay.com/es/persona-icono-salida-de-emergencia-1332793/

Medium, Strong, Strong, Medium, N/A

Reference Data

N/A, Weak, Strong, N/A, N/AVery extreme case

Reference Data





Reference Data

https://commons.wikimedia.org/wiki/File:Mobile_phone_font_awesome.svghttps://www.goodfreephotos.com/vector-images/mobile-cellphone-vector-clipart.png.php

https://pixabay.com/es/smartphone-tel%C3%A9fono-m%C3%B3vil-tel%C3%A9fono-1132675/https://commons.wikimedia.org/wiki/File:Mobile_phone.svg


Medium, Medium, Strong, Medium, Medium

Medium, Strong, Strong, Medium, Medium-Weak

Medium-Weak, Strong, Strong, Medium, Medium

Strong ~ -40dBm

Strong ~ -50dBm

Strong ~ -30dBm

Strong ~ -35dBm

https://commons.wikimedia.org/wiki/File:Mobile_phone_font_awesome.svg

https://commons.wikimedia.org/wiki/File:Mobile_phone_font_awesome.svg

https://www.goodfreephotos.com/vector-images/mobile-cellphone-vector-clipart.png.php

https://www.goodfreephotos.com/vector-images/mobile-cellphone-vector-clipart.png.php

https://pixabay.com/es/smartphone-tel%C3%A9fono-m%C3%B3vil-tel%C3%A9fono-1132675/

https://pixabay.com/es/smartphone-tel%C3%A9fono-m%C3%B3vil-tel%C3%A9fono-1132675/

https://commons.wikimedia.org/wiki/File:Mobile_phone.svg

https://commons.wikimedia.org/wiki/File:Mobile_phone.svg

https://i.ytimg.com/vi/OyRW9uFSmh0/maxresdefault.jpg



Reference Data

Strong, Medium, Medium, Medium, Weak


Weak, Medium, Medium, Strong, Medium

Weak, Weak, Medium, Medium, Strong

Reference Data

https://pixabay.com/p-303768/?no_redirect

Cover all the environmentConsider spatial densityConsider temporal densityConsider device heterogeneityConsider dynamics of the environment

Reference Data

Now I have the training data...

I have a perfect

Indoor Location System

https://c2.staticflickr.com/4/3228/2373073659_d231a0cc65.jpg



Reference Data

You need independent data to fine tune and validate your systemhttp://www.relatably.com/m/img/valid-memes/78c0b38fecebd3c736c8123b34fc69059aedce91ada224ee82677ba7707e14f9.jpg

http://www.relatably.com/m/img/valid-memes/78c0b38fecebd3c736c8123b34fc69059aedce91ada224ee82677ba7707e14f9.jpg

http://www.relatably.com/m/img/valid-memes/78c0b38fecebd3c736c8123b34fc69059aedce91ada224ee82677ba7707e14f9.jpg

Validation data

Avoid using Training Data!

It may provide slanted information.N consecutive fps: (N-1) Training and 1 for validation ? No!

Validation data

Validation data

Validation data

Validation DataNecessary to:

1. Provide an estimation of the IPSs error: geometric error, hit detection rates,...2. Calibrate your System: kNN algorithms and variants3. Filter APs4. Among many other useful operations :-)

Validation DataWhat happens if there is no Validation Data ?

1. k-fold Cross-Validation of training data

2. Consider groups of samples: ref point, user, device, day, among others to increase diversity and independence of the sets

~ 60-80% training, 40-20% validation

Validation Data

Now I have the validation data...

I have a perfect

Indoor Location System




Validation Data

Your system may work fine All the contexts have not been considered

Operational Data vs. Testing Data

Depending on the main objective of the IPS, you may have

1. Operational DataFingerprints from working system + Feedback from users

2. Testing DataFingerprints explicitly collected for testing

Research: Training + Validation + TestBetter if Test Data is Blind

UJIIndoorLoc has T + V + Blind TS !!!

Mapping Strategies

Slow procedure!High precision in reference pointsHigh precision on the fingerprint measuresConsecutive / Cumulative valuesDense radio mapSimple

Mapping Strategies


Mapping Strategies


Mapping Strategies

Fast procedure!You have a few reference or calibration pointsDepends on user’s velocityFingerprint readings may be close or far to ref. pointsFingerprint attached to a segment of the pathThe reference point may be displaced to the real pathLight radio mapComplex

https://c1.staticflickr.com/9/8429/7755469546_61f6d51490_b.jpg



https://pixabay.com/p-1546436/



Storing Data

- Raw data- Database: mySQL, HADOOP, mongoDB,...

Record as maximum information as possible!

mac, rssi, channel, bssid, ....position (XYZ), room, area, floor, building, ...

Storing Data

- CSV Files + Document (UJIIndoorLoc)RSSI1 RSSI2 RSSI3 ... RSSIn X Y Z ... Others

-99 +100 -88 -55 0 0 0 ... 0

Use of default value for non-detected signal +100Use of a documented coordinate system XYZUse of additional location info: office, area, floor, building, campus, city, ….

https://cdn.meme.am/instances/500x/58606836.jpg



Part 4: Time to training

http://www.soulseeds.com/wp-content/uploads/2011/10/take-ownership.jpg



The App for training

Create new fileto record

fingerprints


Select numberof consecutive

fingerprintsto record


Just Select the reference point,

...


Just Select the reference point,

....and Capture Data

….


....and Capture More Data

….


DON’T FORGET TO UPDATE THE

REFERENCE POINT !!!


Capture Even More Data

DON’T FORGET TO UPDATE THE

REFERENCE POINT !!!


Finally, send the database to us

by e-mail

Part 5: The operational step

The kNN algorithm

The kNN algorithm

The kNN algorithm

The kNN algorithm

The kNN algorithmAlgorithm with k=1

● INPUT:○ Training database (samples and labels of each sample)○ Test sample

● OUTPUT:○ Label of the test sample

● BEGIN○ Estimate the distances between the test sample and all training ones○ Return the label of the training sample with less distance

● END

The kNN algorithmAlgorithm with k>1

● INPUT:○ Training database (samples and labels of each sample)○ Test sample

● OUTPUT:○ Label of the test sample

● BEGIN○ Estimate the distances between the test sample and all training ones○ Get the labels of the k-th training samples with less distance○ Return the majority label

● END

The kNN algorithm

The kNN algorithm

The weighted-kNN algorithm● k = 3

● It is blue by simple voting

● It is red by weighted voting

The kNN algorithm in indoor localization● In the classical classification problems, each sample has a label

● In indoor localization each sample (fingerprint) has two continuous values as label: longitude and latitude.

The kNN algorithm in indoor localization● With k=1

○ The localization of the test sample, is the localization of the closest sample in the training dataset.

■ “closest” in the feature space

The kNN algorithm in indoor localization● Feature space is not the same than real space



The kNN algorithm in indoor localization● With k>1

○ The localization of the test sample, is the centroid of the localizations of the k-th closest samples in the training dataset.

The kNN algorithm in indoor localization● With k>1

○ The localization of the test sample, is the centroid of the localizations of the k-th closest samples in the training dataset.

Source code explanationfunction IPIN2016_Tutorial_TrainYourSystem conf = SetMyConfiguration(); data = ReadAllData(conf); data = ChangeDataRepresentation(data);

for i=1:conf.experiment_repetitions folds = DivideInFolds(number_of_samples,conf); vmean_error_in_meters(i) = KnnWithCrossValidation(data, folds, conf); end mean_error_in_meters = mean(vmean_error_in_meters); fprintf('\n The method has obtained an error of %f meters.\n',mean_error_in_meters);end

Source code explanationfunction mean_error_in_meters = KnnWithCrossValidation(data, folds, conf) M = conf.number_of_macs; for i=1:conf.number_of_folds for j=1:conf.number_of_folds if (i==j) test_data = data(folds{i},1:M); test_labels = data(folds{i},M+1:M+2); else train_data = [train_data; data(folds{j},1:M)]; train_labels = [train_labels; data(folds{j},M+1:M+2)]; end end est_labels = ApplyKnn(train_data,train_labels,test_data,conf); verror(i) = EstimateMeanErrorMeters(est_labels,test_labels); end mean_error_in_meters = mean(verror);end

Source code explanationfunction est_labels = ApplyKnn(train_data,train_labels,test_data,conf) distance_matrix = GetDistanceMatrix(train_data,test_data,conf); for i=1:N_test vpos = GetPositionsOfTheMinimums(distance_matrix,conf); lat = 0; long = 0; for j=1:conf.k lat = lat + train_labels(vpos(j),2); long = long + train_labels(vpos(j),1); end est_labels(i,:) = [ long/conf.k, lat/conf.k ]; endend

Part 6: Time to play with operation source code

Competition rules (first phase)● Read SetMyConfiguration().

● Test with using different parameter configuration.

● Write in the competition paper the best solution.

Competition rules (first phase)

● You have only 20 minutes.

● If you do not hand the paper in time, your results will not be estimated.

Part 7: How to improve the ILS

How to improve the ILSPresence of Noisy WAPs

1. SSID ‘iPhone of …’2. Very weak signal3. Located at distant places4. High variability in the same reference point

FEATURE SELECTION

How to improve the ILSHuge workload of the kNN algorithm:

1. Simplify reference dataseta. Calculate meansb. Remove noisy fingerprints in ref. pointc. Remove repeated fingerprints

2. Apply clustering pre-stagea. Group similar fingerprints - Representative FP

3. Reduce reference dataset on-the-flya. Common macsb. Strongest signal

CONDENSE & FILTER

How to improve the ILSkNN Centroid


FS = 10

FS = 40

FS = 120


FS = 10

FS = 40

FS = 120


FS = 10

FS = 40

FS = 120

Weight = 17

Weight = 4

Weight = 1


FS = 10

FS = 40

FS = 120

Weight = 12

Weight = 3

Weight = 1


FS = 10

FS = 40

FS = 120

Weight = 144

Weight = 9

Weight = 1


FS = 10

FS = 40

FS = 120

How to improve the ILS● Use knowledge on Signal Propagation to develop better distance metrics

10 dBm of difference in both casesVery different meaning!!!

-45 -80 X

-55 -80 X-55 -90 X

vs


Perfect match in both cases The second case is less representative!

-50 -40 -60 -50 -40 -60

N/A -55 N/A N/A -55 N/A

vs

vs


Continuityproblems!

-50 -45 -70

N/A -50 -70 N/A -45 N/Avs

-50 -45 N/A

-55 -45 -60

-50 -40 N/A

Part 8: Time to improve the ILS

The best configuration is:

Competition rules (second phase)● You are allowed to modified the function:

○ ApplyKnn○ ChangeDataRepresentation

● But, you can not modified the function:○ TestKnn

Part 9: Awards

The “Best training award” goes to:

The “Best ILS award” goes to:

Fingerprinting-based Indoor Positioning

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