Evaluating Wi-Fi Location Evaluating Wi-Fi Location Estimation Technique for Estimation Technique for

Indoor NavigationIndoor Navigation

Roshmi BhaumikRoshmi Bhaumik

OutlineOutline

MotivationMotivation Problem DefinitionProblem Definition Related workRelated work Our ContributionOur Contribution ExperimentExperiment Case StudyCase Study Conclusion and Future workConclusion and Future work

MotivationMotivation Wireless communicationsWireless communications

• Chaska city wide Wi-Fi available from July 2004Chaska city wide Wi-Fi available from July 2004• Minneapolis city wide Wi-Fi proposal , 2006Minneapolis city wide Wi-Fi proposal , 2006

Indoor location aware applicationsIndoor location aware applications• Indoor Navigation aid for visually impairedIndoor Navigation aid for visually impaired• Navigation through Exhibition Halls and Navigation through Exhibition Halls and

MuseumsMuseums Challenges of indoor Wi-Fi positioningChallenges of indoor Wi-Fi positioning

• Noisy characteristics of wireless channelsNoisy characteristics of wireless channels• Multi-path fadingMulti-path fading

Motivation(2)Motivation(2) Evaluating Wi-Fi based Indoor Location Evaluating Wi-Fi based Indoor Location

Estimation techniques for indoor Estimation techniques for indoor navigationnavigation

Location aware solution (client)

Location based Server

Indoor Positioning Technology

System Architecture of Location Based Service

Core Common Technology

OutlineOutline

Motivation Motivation Problem DefinitionProblem Definition Related workRelated work Our ContributionOur Contribution ExperimentExperiment Case StudyCase Study Conclusion and Future workConclusion and Future work

Problem DefinitionProblem Definition

GivenGiven• Wi-Fi Localization schemeWi-Fi Localization scheme

EvaluateEvaluate• Given localization scheme for indoor Given localization scheme for indoor

navigation systemnavigation system ObjectiveObjective

• Fulfilling accuracy requirements of navigation Fulfilling accuracy requirements of navigation applicationapplication

ConstraintsConstraints• Using existing Wi-Fi Access Points (APs)Using existing Wi-Fi Access Points (APs)• Experiment carried out in typical buildingExperiment carried out in typical building

OutlineOutline

Motivation Motivation Problem DefinitionProblem Definition Related workRelated work Our ContributionOur Contribution ExperimentExperiment Case StudyCase Study Conclusion and Future workConclusion and Future work

Related Work Related Work Requirements of positioning for Requirements of positioning for

indoor navigation:indoor navigation:1.1. Accuracy Accuracy 2.2. Integrity- issue alarm in case Integrity- issue alarm in case

of large estimation errorsof large estimation errors3.3. Availability (Coverage)Availability (Coverage)4.4. Continuity of service (Location Continuity of service (Location

Estimation response time)Estimation response time)(Swiss Federal Institute of Technology (Swiss Federal Institute of Technology

2003) 2003)

Related Work (2)Related Work (2)Evaluation criteria:Evaluation criteria: Location labelingLocation labeling System performanceSystem performance ArchitectureArchitecture CostCost

Hightower et al, 2001 [14]Hightower et al, 2001 [14] Algorithm vs. accuracy Algorithm vs. accuracy Performance vs. accuracy tradeoffPerformance vs. accuracy tradeoff Fault-toleranceFault-tolerance

P. Prasithsangaree et al, 2002 [11]P. Prasithsangaree et al, 2002 [11]

OutlineOutline

Motivation Motivation Problem DefinitionProblem Definition Related workRelated work Our ContributionOur Contribution ExperimentExperiment Case StudyCase Study Conclusion and Future workConclusion and Future work

Our ContributionOur Contribution

Limitations of related work: Limitations of related work: Did not consider effect of different Did not consider effect of different

motion patterns on accuracymotion patterns on accuracyOur Contribution:Our Contribution: Evaluate the positioning accuracy Evaluate the positioning accuracy

with different types of movementwith different types of movement Other lessons learntOther lessons learnt Case Study: Indoor navigation aid Case Study: Indoor navigation aid

for the visually impairedfor the visually impaired

OutlineOutline

Motivation Motivation Problem DefinitionProblem Definition Related workRelated work Our ContributionOur Contribution ExperimentExperiment Case StudyCase Study Conclusion and Future workConclusion and Future work

Experiment DesignExperiment Design

Error measured= Euclidean distance between gold Error measured= Euclidean distance between gold standard and estimated locationstandard and estimated location

Floor Map

Ekahau Software

Calibration

Track controlled movement

LocationEstimates

Accuracy Calculations

1. Stationary2. Stop and Go3. Smooth about a point4. Smooth uniform motion5. Change in direction

Constant parameters:Constant parameters:•Device Scan Interval (500msec)Device Scan Interval (500msec)•Accurate ModeAccurate ModeVariable parameters:Variable parameters:•Location Update Interval Location Update Interval (500 - 6000msec)(500 - 6000msec)

Movements for a pointMovements for a point1) Stationary:1) Stationary:Gold standard= AGold standard= A10 Readings taken after 10 Readings taken after

2min2min2)Stop and Go 2)Stop and Go Gold standard= BGold standard= B10 Readings taken within 10 Readings taken within

30 sec30 sec3)Smooth about a point3)Smooth about a pointGold Standard= BGold Standard= BStart taking readings at A Start taking readings at A

and end at Cand end at C

< =3ft <= 3ft

Slow motion

Quick motion

Static

BA

A

B CA

Smooth Motion on Tracking Rail Smooth Motion on Tracking Rail

4) 4) Smooth uniform motionSmooth uniform motion

Gold standard= points on the tracking railGold standard= points on the tracking rail

Readings taken using the Ekahau Accuracy Readings taken using the Ekahau Accuracy ToolTool

Error calculation is done by the toolError calculation is done by the tool

Slow motion

Tracking rail

Direction ChangeDirection Change5) Changes in direction5) Changes in direction

a) Turning back 180 degrees on the path

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

A B

A B

Slow motion

Tracking rail

Turn back

Elliot Hall First FloorElliot Hall First Floor

Elliot Hall Third FloorElliot Hall Third Floor

Ekahau Positioning SoftwareEkahau Positioning Software Ekahau Client (on every mobile Ekahau Client (on every mobile

device)device)• Retrieves signals from visible Access Points

through the network cards

Ekahau ManagerEkahau Manager• For site calibration, adding logical areas,

tracking and accuracy analysis

Ekahau Positioning Engine (EPE)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

ResultsResults

Actual X,YActual X,Y StationaryStationary

- Error (ft)- Error (ft)Smooth slow about Smooth slow about point -Error (ft)point -Error (ft)

Averaged X,Y – Averaged X,Y – Error (ft)Error (ft)

Stop and go -Stop and go -Error (ft) Error (ft)

703,79703,79 0.780.78 7.07.0 6.976.97 9.799.79

703,848703,848 6.336.33 11.8211.82 6.946.94 11.0611.06

703,969703,969 11.5811.58 18.4318.43 15.5515.55 11.7311.73

703,1268703,1268 3.173.17 10.9210.92 9.369.36 10.6810.68

702,1452702,1452 5.535.53 5.645.64 4.504.50 5.985.98

457,1419457,1419 2.252.25 3.53.5 3.223.22 4.374.37

780,1485780,1485 6.896.89 6.416.41 5.345.34 26.5026.50

Average 5.225.22 9.109.10 7.417.41 11.4411.44

Comparing Types of Motion: Floor 1Comparing Types of Motion: Floor 1

Comparing Types of Motion: Floor 1Comparing Types of Motion: Floor 1

ActualStop goSmooth AvgStationary

ActualSmooth -5.8Stationary -5.5Stop n Go- 10.2Turn around – 6.8

Comparing Types of Motion: Floor 3Comparing Types of Motion: Floor 3

Average Accuracy variation with Average Accuracy variation with type of motion: (1, 2, 3 &5a)type of motion: (1, 2, 3 &5a)

9.7,9.1,10,9.27.2,7.2,17.5,7.7

2.3

,2.3

,5.6

8.5

,6.5

,21

.2

0.7,3.3,0.7,4.16.5,4.8,6.2,6.2

Smooth uniform motion: Floor 1Smooth uniform motion: Floor 1

FloorFloor Min Min MaxMax AverageAverage 90%90% MedMed

Elliot-1Elliot-1stst units in ftunits in ft

0.40.4 26.226.2 8.88.8 16.816.8 7.67.6

•Use Ekahau Accuracy Tool

•Smooth uniform motion in a straight line along the tracking rail

•Error vector shown in red color

Smooth uniform motion: Floor 3Smooth uniform motion: Floor 3

FloorFloor Min Min MaxMax AverageAverage 90%90% MedMed

Elliot -3Elliot -3rdrd units in ftunits in ft

0.80.8 16.816.8 7.27.2 12.412.4 8.18.1

Readings taken using Ekahau Accuracy Tool

Ambiguous IntersectionAmbiguous IntersectionAll types of movement for a point are considered

Average Error worse than 12 ft for this specific location

Actual X,YActual X,Y StationaryStationary

- Error (ft)- Error (ft)Smooth slow about Smooth slow about point -Error (ft)point -Error (ft)

Averaged X,Y – Averaged X,Y – Error (ft)Error (ft)

Stop and go -Error Stop and go -Error (ft) (ft)

703,969703,969 11.5811.58 18.4318.43 15.5515.55 11.7311.73

AnalysisAnalysis

Accuracy requirements are met for the Accuracy requirements are met for the listed types of movements except :listed types of movements except :• Stop and goStop and go• Direction change at ambiguous intersectionDirection change at ambiguous intersection

System does not respond to quick changes System does not respond to quick changes and error increases is due to and error increases is due to overestimationoverestimation

Ambiguous signal patterns cause large Ambiguous signal patterns cause large errorserrors

Other lessons learnedOther lessons learned

Calibration Calibration • Stability: Stability: Stable over 4 monthsStable over 4 months

• Transferability: Transferability: Calibration done with one Calibration done with one client, used to track any other supported client, used to track any other supported clients with same accuracyclients with same accuracy

• Tested with Cisco, Orinoco and Dell’s built-in Tested with Cisco, Orinoco and Dell’s built-in WLAN cards and Toshiba PDA WLAN cards and Toshiba PDA

• Calibration is transferable as EPE performs Calibration is transferable as EPE performs normalization of RSSI* values from different normalization of RSSI* values from different network cards and devicesnetwork cards and devices

*Radio Signal Strength Indicator*Radio Signal Strength Indicator

Other lessons learnedOther lessons learned Tracking on multiple floorsTracking on multiple floors

• Adjacent floor maps linked at certain Adjacent floor maps linked at certain positions (e.g. start of staircase)positions (e.g. start of staircase)

Latency (5-15 sec) depends on device Latency (5-15 sec) depends on device speed.speed.

• Connection points between floors are Connection points between floors are not specifiednot specified

Correct floor detected with greater latency Correct floor detected with greater latency (2 min)(2 min)

• Floors are significant barriers to signal Floors are significant barriers to signal propagationpropagation

• Points in adjacent floors have different Points in adjacent floors have different signal patternssignal patterns

Other Lessons learnedOther Lessons learnedAccurate mode meets Accurate mode meets accuracy requirement and accuracy requirement and latency is 5 sec latency is 5 sec Variation of LUI* does not Variation of LUI* does not completely control the completely control the effect of historyeffect of historyAt low RSSI, changes in At low RSSI, changes in signal strength with signal strength with distance is very small. distance is very small. This causes signal aliasing This causes signal aliasing and reduces accuracyand reduces accuracy

*Location Update IntervalLocation Update Interval

Average Error with LUI

0

2

4

6

8

10

12

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

5500

6000

Location Update Interval (ms)

Avera

ge E

rro

r (f

t)

Accurate

Real Time

OutlineOutline

Motivation Motivation Problem DefinitionProblem Definition Related workRelated work Our ContributionOur Contribution ExperimentExperiment Case StudyCase Study Conclusion and Future workConclusion and Future work

Case Study: Case Study: Indoor Navigation Aid for the Visually ImpairedIndoor 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

Information Flow Diagram

USER

CLIENT

Location Aware Application

Audio/ Visual Output

Wi-Fi Sensors

BUILDING DATABASE

Spatial data: X,Y

Attribute data: Physical & Logical spaces

LOCATION SERVER (EPE)

Positioning Model: floor maps and calibration data

Existing WorkExisting Work

Building Database Building Database Data Entry interface: building and floor Data Entry interface: building and floor

datadata Audio/Visual output : a list of Audio/Visual output : a list of

surrounding features, orientation and surrounding features, orientation and distance from current user positiondistance from current user position

User input needed to get current user User input needed to get current user positionposition

My AdditionsMy Additions Infer current user location in building Infer current user location in building

database coordinatesdatabase coordinates Multiple floor tracking and transferable Multiple floor tracking and transferable

calibrationcalibration Improve accuracy and latency of location Improve accuracy and latency of location

estimateestimate• Background thread to collect location & error Background thread to collect location & error

estimatesestimates• Average location estimates over specified time Average location estimates over specified time

intervalsintervals Add location buoys spaced 15 ftAdd location buoys spaced 15 ft

• Reduce computationReduce computation• Describe the surrounding with finer granularityDescribe the surrounding with finer granularity

IGS screen shotIGS screen shot

Application Design GuidelinesApplication Design Guidelines

During calibration, sample points should During calibration, sample points should be rejected if RSSI is below certain be rejected if RSSI is below certain thresholdthreshold

Averaging location estimates over Averaging location estimates over optimum time interval is better than optimum time interval is better than getting a single instantaneous estimategetting a single instantaneous estimate

Accurate mode gives better location Accurate mode gives better location estimates for normal walking speedsestimates for normal walking speeds

Accuracy will vary depending on the type Accuracy will vary depending on the type of movementof movement

Conclusions and Future WorkConclusions and Future Work

EPE location estimation scheme performs EPE location estimation scheme performs well for an indoor navigation application well for an indoor navigation application but some areas can be improved:but some areas can be improved:

Calibration stability and transferability is Calibration stability and transferability is goodgood

Tracking over multiple floors works wellTracking over multiple floors works well Accuracy is good for smooth movement in Accuracy is good for smooth movement in

a straight line and stationary state a straight line and stationary state Accuracy is poor when signal strengths are Accuracy is poor when signal strengths are

lowlow

Conclusions and Future work(2)Conclusions and Future work(2) Accuracy is poor if the path of movement has a Accuracy is poor if the path of movement has a

number of intersections, specially with ambiguous number of intersections, specially with ambiguous signal patternsignal pattern• Using directional APs for asymmetric coverageUsing directional APs for asymmetric coverage

Accuracy is poor for stop and go motion Accuracy is poor for stop and go motion • Using some method to detect still and moving state Using some method to detect still and moving state • Use above to choose the transition probabilitiesUse above to choose the transition probabilities• Related work –LOCADIO[7]Related work –LOCADIO[7]

Further tuning of the application can achieve Further tuning of the application can achieve better accuracy for indoor navigationbetter accuracy for indoor navigation

Future work:Future work: Implement own location detection scheme.Implement own location detection scheme. Incorporate suggested improvementsIncorporate suggested improvements

KeywordsKeywords Wi-Fi : Wi-Fi : Wireless fidelity, IEEE802.11bWireless fidelity, IEEE802.11b APs: APs: Access Points, Within the range (~50ft) of an AP, the wireless Access Points, Within the range (~50ft) of an AP, the wireless

end-user has a full network connection with the benefit of end-user has a full network connection with the benefit of mobility.mobility.

RSSI : RSSI : Received Signal Strength IndicatorReceived Signal Strength Indicator Bayesian networks :Bayesian networks :A probabilistic graphical model . The nodes A probabilistic graphical model . The nodes

represent variables and directed arcs represent conditional represent variables and directed arcs represent conditional dependencies between variables.dependencies between variables.

Stochastic model :Stochastic model :A mathematical model which contains random A mathematical model which contains random (stochastic) components or inputs; consequently, for any specified (stochastic) components or inputs; consequently, for any specified input scenario, the corresponding model output variables are input scenario, the corresponding model output variables are known only in terms of probability distributions in contrast to a known only in terms of probability distributions in contrast to a deterministic modeldeterministic model

Machine learning :AMachine learning :A method for creating computer programs by method for creating computer programs by the analysis of data sets. the analysis of data sets.

IGS: Indoor Guidance system, a navigation aid developed IGS: Indoor Guidance system, a navigation aid developed for the Low Vision Lab, Psychology department, UMNfor the Low Vision Lab, Psychology department, UMN

ReferencesReferencesPapers:Papers:

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P1.”Graphical Models on Manhattan: A probabilistic approach to mobile device positioning” presented by Petri Myllymäki and Henry TirriP2. faculty.cs.tamu.edu/dzsong/teaching/ fall2004/netbot/Yutu_Liu_Robot.ppt