Evaluating WiFi Location Estimation Technique for Indoor Navigation

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Evaluating Wi-Fi Location Estimation Technique
for Indoor Navigation

• Roshmi Bhaumik

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Outline

• Motivation
• Problem Definition
• Related work
• Our Contribution
• Experiment
• Case Study
• Conclusion and Future work

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Motivation

• Wireless communications
• Chaska city wide Wi-Fi available from July 2004
• Minneapolis city wide Wi-Fi proposal , 2006
• Indoor location aware applications
• Indoor Navigation aid for visually impaired
• Navigation through Exhibition Halls and Museums
• Challenges of indoor Wi-Fi positioning
• Noisy characteristics of wireless channels
• Multi-path fading

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Motivation(2)

• Evaluating Wi-Fi based Indoor Location Estimation
  techniques for indoor navigation

Location aware solution (client)
Location based Server
Indoor Positioning Technology
Core Common Technology
System Architecture of Location Based Service
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Outline

• Motivation
• Problem Definition
• Related work
• Our Contribution
• Experiment
• Case Study
• Conclusion and Future work

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Problem Definition

• Given
• Wi-Fi Localization scheme
• Evaluate
• Given localization scheme for indoor navigation
  system
• Objective
• Fulfilling accuracy requirements of navigation
  application
• Constraints
• Using existing Wi-Fi Access Points (APs)
• Experiment carried out in typical building

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Outline

• Motivation
• Problem Definition
• Related work
• Our Contribution
• Experiment
• Case Study
• Conclusion and Future work

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Related Work

• Requirements of positioning for indoor
  navigation
• Accuracy
• Integrity- issue alarm in case of large
  estimation errors
• Availability (Coverage)
• Continuity of service (Location Estimation
  response time)
• (Swiss Federal Institute of Technology 2003)

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Related Work (2)

• Evaluation criteria
• Location labeling
• System performance
• Architecture
• Cost
• Hightower et al, 2001 14
• Algorithm vs. accuracy
• Performance vs. accuracy tradeoff
• Fault-tolerance
• P. Prasithsangaree et al, 2002 11

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Outline

• Motivation
• Problem Definition
• Related work
• Our Contribution
• Experiment
• Case Study
• Conclusion and Future work

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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

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Outline

• Motivation
• Problem Definition
• Related work
• Our Contribution
• Experiment
• Case Study
• Conclusion and Future work

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Experiment Design
Floor Map

• Stationary
• Stop and Go
• Smooth about a point
• Smooth uniform motion
• Change in direction

Ekahau Software
Track controlled movement
Calibration

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

Location Estimates
Accuracy Calculations
Error measured Euclidean distance between gold
standard and estimated location
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Movements for a point

• 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

A
B
A


B
C
A
Slow motion Quick motion Static
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Smooth Motion on 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

Slow motion Tracking rail
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Direction Change

• 5) 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
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Elliot Hall First Floor
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Elliot Hall Third Floor
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Ekahau Positioning Software

• Ekahau Client (on every mobile device)
• Retrieves signals from visible Access Points
  through the network cards
• Ekahau Manager
• For site calibration, adding logical areas,
  tracking and accuracy analysis
• 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

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Results
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Comparing Types of Motion Floor 1
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Comparing Types of Motion Floor 1
Actual Stop go Smooth Avg Stationary
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Comparing Types of Motion Floor 3
Actual Smooth -5.8 Stationary -5.5 Stop n Go-
10.2 Turn around 6.8
Average Accuracy variation with type of motion
(1, 2, 3 5a)
9.7,9.1,10,9.2
7.2,7.2,17.5,7.7
2.3,2.3,5.6
8.5,6.5,21.2
0.7,3.3,0.7,4.1
6.5,4.8,6.2,6.2
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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

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Smooth uniform motion Floor 3
Readings taken using Ekahau Accuracy Tool
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Ambiguous Intersection

• All types of movement for a point are considered
• Average Error worse than 12 ft for this specific
  location

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Analysis

• Accuracy requirements are met for the listed
  types of movements except
• Stop and go
• Direction change at ambiguous intersection
• System does not respond to quick changes and
  error increases is due to overestimation
• Ambiguous signal patterns cause large errors

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Other lessons learned

• 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 Dells 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

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Other lessons learned

• Tracking on multiple floors
• Adjacent floor maps linked at certain positions
  (e.g. start of staircase)
• Latency (5-15 sec) depends on device speed.
• Connection points between floors are not
  specified
• Correct floor detected with greater latency (2
  min)
• Floors are significant barriers to signal
  propagation
• Points in adjacent floors have different signal
  patterns

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Other Lessons learned

• 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
• Location Update Interval

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Outline

• Motivation
• Problem Definition
• Related work
• Our Contribution
• Experiment
• Case Study
• Conclusion and Future work

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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 Attribute
data Physical Logical spaces
CLIENT Location Aware Application Audio/ Visual
Output Wi-Fi Sensors

USER
LOCATION SERVER (EPE) Positioning Model floor
maps and calibration data
Information Flow Diagram
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Existing Work

• 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

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My Additions

• 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
• Add location buoys spaced 15 ft
• Reduce computation
• Describe the surrounding with finer granularity

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IGS screen shot
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Application Design Guidelines

• 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

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Conclusions and Future Work

• 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

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Conclusions and Future work(2)

• Accuracy is poor if the path of movement has a
  number of intersections, specially with ambiguous
  signal pattern
• Using directional APs for asymmetric coverage
• 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 LOCADIO7
• 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

• 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

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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. 775784
• 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. 5969.
• 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.
• SlidesP1.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

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Commercial Solutions
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Wi-Fi Positioning Theory

• Static Location Estimation with Wi-Fi
• Nearest Neighbor 1
• Minimum Euclidean distance in signal space
  between real time and offline entries (x,y,z,ssi
  (i1..N))
• weighted average of k nearest neighbors
• Average accuracy 10 ft
• Neural Networks 3
• Dependencies between signal and location are not
  easily modeled by an Multi-Layer Perception
  neural network
• Bayesian Approach Maximum A Posteriori (MAP)
  Estimate 2,4,5
• The probabilistic model assigns a probability for
  each possible location l, given the observation
  o.

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Wi-Fi Positioning Theory (2)

• Tracking motion (recursive estimate)
• Kalman Filter 6,8
• linear relation between measurement and state
  vector
• Gaussian noise
• Extended Kalman Filter 6,8
• nonlinearity dealt by updating linearization
• not suitable for large disturbances
• Hidden Markov Model (HMM) 6,9
• all states must be explicitly represented
• separate HMM for each subset of state variable
• apply Viterbi Algorithm to find most probable
  path
• Particle Filter 6,8
• current state N weighted state samples
• samples updated using SIR after each new
  measurement
• focus on state space regions with high probability

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Ekahau Theory

• Location estimation is posed here as a machine
  learning problem
• Calibration data is used to build a model to
  predict location given real-time signal
  measurements using probabilistic methods
• Tracking uses a Hidden Markov model (HMM) where
  the locations lt are the hidden unobserved
  states. We compute maximum probability path l1ln
  , given a sequence (history) of observations o1,
  on using the Viterbi algorithm

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Theory (2)

• p(ol) likelihood function/conditional
  probability of obtaining observations o at
  location l. (from calibration)
• p(l) prior probability of location l (can add
  user profiles, tracking rails etc. to improve
  accuracy)
• p(o) normalizing constant
• Using a loss function and posterior distribution,
  p(lo), we can get a optimal estimator t for the
  location variable.
• Average accuracy obtained varies between 3ft to
  10ft depending on actual implementation

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Hidden Markov Model

• HMM ?
• N the number of states (S1 S2 .. SN)
• M the number of possible observations
• O O1 O2 .. OT is the sequence of observations
• Q q1 q2 .. qT is the notation for a path of
  states
• P1, P2, .. PN The initial probabilities
• P(q0 Si) Pi
• The state transition probabilities
• P(qt1Sj qtSi)aij
• The observation / emission probabilities
• P(Otk qtSi)bi(k)

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Viterbi Algorithm
Find most probable path given a sequence of
observations O1, On i.e. argmax P(Q O1 O2 OT)
? By induction
The most probable path to Sj has Si as its
penultimate state where iargmax dt(i) aij bj
(Ot1) i
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