Laboratoire d'InfoRmatique en Images et Systèmes d'information

UMR 5205

18/04/23

Vasile-Marian Scuturici and Dejene EjiguLIRIS-UMR 5205 CNRS, INSA de Lyon

Positioning Support in Pervasive Environments

Presented at ICPS'06 IEEE International Conference on Pervasive Services 2006

26-29 June 2006, Lyon, France

04/18/23

Topics

BackgroundPervasive computingPositioning needsRelated works

BackgroundPervasive computingPositioning needsRelated works

Model for indoor location detection Learning phase Prediction Phase

Model for indoor location detection Learning phase Prediction Phase

Experimental results and usage scenarioExperimental results and usage scenario

Conclusions and future workConclusions and future work

04/18/23

What is pervasive computing?

Typical view of a pervasive environment crowded with varieties of ubiquitous devices surrounding a user.

A computing trend towards using increasingly ubiquitous and

interconnected computing devices in the environment.

A computing trend towards using increasingly ubiquitous and

interconnected computing devices in the environment.

Enhanced by a convergence of advanced electronic, wireless

technologies, and the Internet.

Enhanced by a convergence of advanced electronic, wireless

technologies, and the Internet.

Devices involved are very tiny, sometimes invisible, either mobile or

embedded in almost any type of object imaginable.

Devices involved are very tiny, sometimes invisible, either mobile or

embedded in almost any type of object imaginable.

04/18/23

Positioning needs …PSAQL in our platform PerSE to express user intensions:USE sunrise.ppt ON BASE notebook WITH SERVICE projector

PSAQL in our platform PerSE to express user intensions:USE sunrise.ppt ON BASE notebook WITH SERVICE projector

To ensure neighbourhood constraint, the query can be rewritten as:USE sunrise.ppt ON BASE notebook WITH SERVICE projector IN NEIGHBOURHOOD

To ensure neighbourhood constraint, the query can be rewritten as:USE sunrise.ppt ON BASE notebook WITH SERVICE projector IN NEIGHBOURHOOD

Here the user assumes that the video projector is situated in the same visually and physically accessible space

Here the user assumes that the video projector is situated in the same visually and physically accessible space

04/18/23

Positioning needs …

The question is: How can the NEIGHBOURHOOD space be identified in PerSE?

The question is: How can the NEIGHBOURHOOD space be identified in PerSE?

Neighbourhood relation here is expressed not by the physical proximity like in coordinate positioning system but by perception of the presence in the same bounded space/room.

Neighbourhood relation here is expressed not by the physical proximity like in coordinate positioning system but by perception of the presence in the same bounded space/room.

04/18/23

Related works

Among localization and distance measuring methods are:Global Positioning Systems Radio Frequency (RF) delay measurementAssociation to nearest Access Point Received RF signal strength

Among localization and distance measuring methods are:Global Positioning Systems Radio Frequency (RF) delay measurementAssociation to nearest Access Point Received RF signal strength

GPS systems are good for outdoor positioning servicesThe others are based on triangulation or TIX methods and they assume prior knowledge of position of the access point infrastructure

GPS systems are good for outdoor positioning servicesThe others are based on triangulation or TIX methods and they assume prior knowledge of position of the access point infrastructure

04/18/23

Topics

BackgroundPervasive computingpositioning needsRelated works

BackgroundPervasive computingpositioning needsRelated works

Model for indoor location detection Learning phase Prediction Phase

Model for indoor location detection Learning phase Prediction Phase

Experimental results and usage scenarioExperimental results and usage scenario

Conclusions and future workConclusions and future work

04/18/23

Modeling indoor positioning

Learning phase: data is collected, classified to create the prediction modelPrediction phase: Location prediction based on the real-time data values

Learning phase: data is collected, classified to create the prediction modelPrediction phase: Location prediction based on the real-time data values

Architecture of our learning and prediction model

Does not assume prior knowledge of position of APsBased on database methods

Does not assume prior knowledge of position of APsBased on database methods

Learning phase (Offline)

Signal

Tracking

Prediction phase (Real time)

Distributed on Capable Peers

Context –Aware

Service

Data Calibration and

Treatment

Data Mining (Decision Tree)

Signal Tracking

Data Calibration

Prediction

Calibration and Treatment Rules

Prediction Rules (PMML Format)

(eg. PDA_David locatedIn Common_Room)

04/18/23

Learning phase …

340 342

337335

326 330 332 334 336 338

333

328

Demars

Ou-halimaBoumedieneSecrétariat

EDIISBrunie

Varet

SecrétariatFormationcontinue

Abbas

Jouve

Directionformationcontinue

Salle TD Salle TD

Salle TD Amphi FCSalle TD Salle TP - FCSalle TD

Coquard

Chaari

SecrétariatEDIIS

Lebel

329 331

Passerelle

311

30

WC

Asc

ense

ur

315 317325

316314

Benel

Berhe

Seitz

Besson

Rigotti

Leschi Robardet

Masson

Piersno

Ahmed-Ouamer

Saoula Samir

Arias

Servigne

Miquel

Laforest

Tchounikine

SécrétariatDirectionSalle de Réunion

Taher AhmedArara Keita

Jossan Pozzoli

FloryChatti

Salledétente

Egyed

Réseau

Andrianarisoa

Balhoul

Berkane OularbiKouloumdjian

Coulondre Calabretto Alvarez

Rifaieh Coquil

Pivano

301305 307 309 313 319 321 323

324322320

Amghar

318308306304302

Tribollet

Réseau

Badr

Dejene

PechTaqafi

Boulicaut

Ridh HadjM’tir

AccaryManzi

Ouziri

Verdier

Cardenas

Servin

Prensa

Hunor

Meger

Leleu

Suela Bohé

Passerelle

201 203 205 207 209

202 204

WC

Asce

nseu

r

211 213

210208

206

215

Salle Serveurs

Détente Elèves

AEDI TP 5IF1 TP PC4 Réseau

TP PC0TP PC1TP PC2 TP 5IF2TP PC3

222 224

227225

B501225A

N

L.Frécon

217

219

221 223

220

212

214 216 218

PRISMA

PRISMA

D. Magnin

G.Neubert

Y.Ouzrout

PRISMA PRISMA

M. Martinez

Salle réunion étudiants

J.FAVREL

M.MATARsecretariat

TP PC5Atelier

Salle TD

I. El Kalkali

P.A. Millet

Topology of the floors used in our experiments

A person holding a PDA moves around the rooms in the building including meeting halls, offices, common rooms, printing rooms and corridors

A person holding a PDA moves around the rooms in the building including meeting halls, offices, common rooms, printing rooms and corridorsOur WiFi-Spotter and management program is used to track, process and store received signal strength from all n visible access points at each tracking location.

Our WiFi-Spotter and management program is used to track, process and store received signal strength from all n visible access points at each tracking location.

04/18/23

… Learning phase …

For each tracking point i in room k, we have a vector with the signal strength values from the APs and a label corresponding to the literal name of the place (room) where the point is situated.

For each tracking point i in room k, we have a vector with the signal strength values from the APs and a label corresponding to the literal name of the place (room) where the point is situated.

Room 00:06:5A:40:0D:C6 00:06:5A:40:0D:D7 00:06:5A:10:0D:C6 00:06:5A:10:0D:D7

501.317 -60 -60 -60 -57

501.317 -60 -60 -60 -57

501.317 -68 -63 -59 -65

501.319 -60 -62 -64 -100

501.319 -57 -57 -60 -100

501.319 -57 -66 -57 -100

Sample attribute-value table showing tracked values.

,....2,1,,

2,

1, ,,...,,

iiknikikik roomapapap

04/18/23

… Learning phase

Signal strength values are classified for pattern identification using data mining tool (MCubiX implementation of the decision tree algorithm).

Signal strength values are classified for pattern identification using data mining tool (MCubiX implementation of the decision tree algorithm).

The result from this process is our working model that can later be used for real-time location detection.

The result from this process is our working model that can later be used for real-time location detection.

The model is represented in the predictive model mark-up language – PMML - format.

The model is represented in the predictive model mark-up language – PMML - format.

04/18/23

Prediction phaseThe two important input parameters for prediction are:

Decision rules obtained from the prediction modelReal-time signal strength values collected at a specific

location

The two important input parameters for prediction are:

Decision rules obtained from the prediction modelReal-time signal strength values collected at a specific

location

If Value is in this region Predict

Room-501_319 with 90% Accuracy

If Value is in this region Predict

Room-501_315 with 88% Accuracy

Sample prediction model

using two APs and three rooms.

04/18/23

BackgroundPervasive computingpositioning needsRelated works

BackgroundPervasive computingpositioning needsRelated works

BackgroundPervasive computingpositioning needsRelated works

BackgroundPervasive computingpositioning needsRelated works

Topics

BackgroundPervasive computingpositioning needsRelated works

BackgroundPervasive computingpositioning needsRelated works

Model for indoor location detection Learning phase Prediction Phase

Model for indoor location detection Learning phase Prediction Phase

Experimental results and usage scenarioExperimental results and usage scenario

Conclusions and future workConclusions and future work

04/18/23

Experimental results …The size of the PMML file containing the model generated after about 4 hours of tracking experiment using three devices is about 320 KB (200rules) and it is within the storage range of mobile devices.Using a cross validation, the results are very encouraging with the error rate below 5%, corresponding to a 95% hit rate.

The size of the PMML file containing the model generated after about 4 hours of tracking experiment using three devices is about 320 KB (200rules) and it is within the storage range of mobile devices.Using a cross validation, the results are very encouraging with the error rate below 5%, corresponding to a 95% hit rate.IF 00_06_5A_E0_0D_FA < -74,00 and 00_06_5A_80_0D_C9 >=-

87,00 THEN LOCATION in [501.342] with accuracy 1,0000 IF 00_06_5A_E0_0D_FA < -74,00 and 00_06_5A_80_0D_C9 < -87,00 and 00_06_5A_60_0D_C6 < -68,50 and 00_06_5A_20_0D_DB < -77,50 and 00_06_5A_80_0C_BD >=-73,50 and 00_06_5A_C0_0D_D7 >=-92,50 and 00_06_5A_E0_0D_D7 < -85,00 THEN LOCATION in [501.329] with accuracy 0,9877 IF 00_06_5A_E0_0D_FA < -74,00 and 00_06_5A_80_0D_C9 < -87,00 and 00_06_5A_60_0D_C6 < -68,50 and 00_06_5A_20_0D_DB < -77,50 and 00_06_5A_80_0C_BD >=-73,50 and 00_06_5A_C0_0D_D7 >=-92,50 and 00_06_5A_E0_0D_D7 >=-85,00 THEN LOCATION in [501.210] with accuracy 1,0000

Sam

ple

ru

les

gen

erat

ed

as a

pre

dic

tio

n m

od

el

04/18/23

… Experimental results

Using the Principal Component Analysis (PCA) algorithm, projection of multidimensional data from all visible APIs into 2 dimensions space shows that the data is well separable.

04/18/23

Usage scenario …

Consider a scenario where Dave is given a multimedia entertainment service on his PDA while he is in the common room for the tea break.

Consider a scenario where Dave is given a multimedia entertainment service on his PDA while he is in the common room for the tea break.

SHARE SERVICE multimedia_player ON BASE LOCALHOST WITH SERVICE multimedia_player ON BASE ALL IN NEIGHBORHOOD

SHARE SERVICE multimedia_player ON BASE LOCALHOST WITH SERVICE multimedia_player ON BASE ALL IN NEIGHBORHOOD

The common room is also used by some friends of David. They too are also equipped with PDAs.

The common room is also used by some friends of David. They too are also equipped with PDAs.

David wants to share the seen of his video with his friends. In this case he will use the middleware PerSE to express his intention in PSAQL.

David wants to share the seen of his video with his friends. In this case he will use the middleware PerSE to express his intention in PSAQL.

04/18/23

… Usage scenarioLocation prediction combined with context information to determine David’s intension in proactively.

Location prediction combined with context information to determine David’s intension in proactively.

IF BASE = LOCALHOST AND BASE_NAME = “PDA_DAVID” AND LOCATION = “CommonRoom” AND RunningAction = “USE * WITH SERVICE multimedia_player ON BASE LOCALHOST” THEN TriggerAction = “SHARE SERVICE multimedia_player ON BASE LOCALHOST WITH SERVICE multimedia_player ON BASE ALL IN NEIGHBORHOOD”

IF BASE = LOCALHOST AND BASE_NAME = “PDA_DAVID” AND LOCATION = “CommonRoom” AND RunningAction = “USE * WITH SERVICE multimedia_player ON BASE LOCALHOST” THEN TriggerAction = “SHARE SERVICE multimedia_player ON BASE LOCALHOST WITH SERVICE multimedia_player ON BASE ALL IN NEIGHBORHOOD”

The primary role of the prediction model is in this example is to detect that PDA_DAVID is in the Common_Room. It continues detecting who else is present in the room.

The primary role of the prediction model is in this example is to detect that PDA_DAVID is in the Common_Room. It continues detecting who else is present in the room.

04/18/23

BackgroundPervasive computingpositioning needsRelated works

BackgroundPervasive computingpositioning needsRelated works

BackgroundPervasive computingpositioning needsRelated works

BackgroundPervasive computingpositioning needsRelated works

Topics

BackgroundPervasive computingpositioning needsRelated works

BackgroundPervasive computingpositioning needsRelated works

Model for indoor location detection Learning phase Prediction Phase

Model for indoor location detection Learning phase Prediction Phase

Experimental results and usage scenarioExperimental results and usage scenario

Conclusions and future workConclusions and future work

04/18/23

Future works

Integrating this module in to the PerSE middlewareIntegrating this module in to the PerSE middleware

Comparison by WiFi card type

-75

-70

-65

-60

-55

-50

-45

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

WIFi Access Points

RS

SI

Valu

e

Axim3 Axim50 Hx4700

16.44

13.41

Further study on how to avoid hardware influence on the prediction model. Preliminary investigations show that type of signal tracking devices has significant effect on the data.

Further study on how to avoid hardware influence on the prediction model. Preliminary investigations show that type of signal tracking devices has significant effect on the data.

Ave

rag

e va

lues

of

sig

nal

s m

easu

red

by

thre

e d

iffe

ren

t d

evic

es a

t th

e sa

me

loca

tio

n

(Dell-Axim3, Dell-Axim50, HP-Hx4700)

04/18/23

Conclusions

Indoor neighbourhood relation between users is represented:Not by the physical proximity, but by the

perception of the presence in the same physically or visually bounded place

Indoor neighbourhood relation between users is represented:Not by the physical proximity, but by the

perception of the presence in the same physically or visually bounded place

We have presented our positioning model for pervasive neighbourhood relationship using the room/office positioning information using database methods

We have presented our positioning model for pervasive neighbourhood relationship using the room/office positioning information using database methodsThe result is found encouraging wit 95% hit rateThe result is found encouraging wit 95% hit rate

04/18/23

TopicsBackgroundPervasive computingpositioning needsRelated works

Model for indoor location detectionLearning phasePrediction Phase

Experimental results and usage scenario

Conclusions and future work

BackgroundPervasive computingpositioning needsRelated works

Model for indoor location detectionLearning phasePrediction Phase

Experimental results and usage scenario

Conclusions and future work

Thank You !!