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Empirical-based Analysis of a Cooperative Location-Sensing System

1 Institute of Computer Science, Foundation for Research & Technology-Hellas (FORTH)2 Department of Computer Science, University of Crete

http://www.ics.forth.gr/mobile/

K. Vandikas1 L. Kriara1,2 T. Papakonstantinou1 A. Katranidou1 H. Baltzakis1

Maria Papadopouli 1,2

This research was partially supported by EU with a Marie Curie IRG and the Greek General Secretariat for Research and Technology.

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Overview

Motivation Taxonomy of location-sensing systems Collaborative Location Sensing (CLS) Performance analysis Conclusions Future work

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Motivation

Emergence of location-based services in several areas transportation & entertainment industries emergency situations assistive technology

→ Location-sensing is critical for the support of location-based services

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Taxonomy of location-sensing systems

Modalities Dependence on & use of specialized infrastructure & hardware Position and coordination system description Cost, accuracy & precision requirements Localized or remote computations Device identification, classification or recognition Models & algorithms for estimating distances, orientation &

position

Radio (Radar, Ubisense, Ekahau), Infrared (Active Badge) Ultrasonic (Cricket) Bluetooth Vision (EasyLiving) Physical contact with pressure (smart floor) or touch sensors

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Cooperative Location-Sensing (CLS)

Enables a device to determine its location in self-organizing manner using the peer-to-peer paradigm

Employs a grid-based representation of the physical space

→ can incorporate contextual information to improve its estimates Uses a probabilistic-based framework

Each cell of the grid has a value that indicates likelihood that the local device is in that cell

These values are computed iteratively using distance between peers and position predictions

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Classifying CLS

Modalities Dependence on & use of specialized infrastructure & hardware Position and coordination system description Cost, accuracy & precision requirements Localized or remote computations Device identification, classification or recognition Models & algorithms for estimating distances, orientation &

position

Radio and/or BluetoothCan be extended to incorporate other type of modalities

Grid representation of the spaceTransformation to/from any coordination systemPosition: a cell in the grid

Objective: 0.5 to 2.5 m (90%)Computations can be performed remotely or at the device depending on the device capabilities Does not perform any of these functionalities

Statistical analysis and particle filters on signal strength measurements collected from packets exchanged with other peers

No need for specialized hardware or infrastructure Can use only IEEE802.11 APs, if necessary

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Example of voting process (1/2)

Accumulation of votes on grid cells of host at different time steps

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Example of voting process (2/2)

Host C votes

Most likely position

x

x

Peers A, B, C have positioned themselves

Host A

Host B votes

x

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Voting algorithm

1. Initialize the values of the cells in the grid of the local device

2. Gather position information from peers

3. Record measurements from these received messages

4. Transform this information to probability of being at a certain cell of its local grid

5. Add this probability to the existing value that this cell had from previous steps

6. Assess if the maximal value of the cells in the grid is sufficient high to indicate the position of the device

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Example of training & run-time signature comparison

AP1

AP2

Signal-strength measurements per AP

cell

weight of that cellRun-time signature

Training-phase signaturecomparison

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Position estimation (at peer A)

1. Initialize the values of the cells in the grid of the local device2. Training phase: Build a signal-strength map of the space (training-

phase signatures)3. Run-time phase: Build signal-strength signature of the current

position4. Compare the run-time and training phase signatures

5. For each new peer that sends its position estimation (e.g., peer B)I. Position B on the local grid of A based on B’s estimationII. Determine their distance based on signal-strength signatureIII. Infer likely positions of AIV. Update the value of the cells accordingly

6. Assess maximal weight of the cells, accept or reject the solution

Landmarks vote

Non-landmark peers vote

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Signature based on confidence interval of signal-strength valuesWeight of cell c assigned as:

total number of APs

run-time confidence interval of i-th AP

training confidence interval of i-th AP

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Example of confidence interval-based comparison

AP1

AP2

Signal-strength measurements per AP

cell

weight of that cell

Run-time signature

Training-phase signature

T2

T1- T1

+

T2+ -

T1- T1

+ T2 T2+ -

R R1+-

[ T-, T+ ] confidence interval based on signal strength measurements from an AP

1R2 R2

+ - …

…

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Distance estimation between two peers

entries of training set ith distance from training set

confidence interval of the run-time measurementsconfidence interval of the i-th entry in the training set

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Signature based on percentiles of the signal-strength values

samples in training set

number of percentiles

jth run-time percentile

jth percentile of ith cell in training set

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Particle filter-based frameworkstep 1

for L = 1, … , P

(L-th particle)

Transition:

Draw new sample xk(L) , P( xk

(L) | xk-1(L) )

Compute weight wk(L) of xk

(L), wk(L) = wk-1

(L)* P( yk | xk

(L) ),

where yk measurement vector: signal strength values

end loop

Normalize weights

Resample

Goto step 1

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Performance evaluation Performance analysis of CLS via simulations [percom’04] Empirical-based measurements in different areas

Various criteria for comparing the training phase and run-time signatures

Particle-filter model Impact of the number of signal strength measurements Impact of the number of APs and peers CLS vs. Ekahau

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Testbed description

Area 7m x 12m @ Telecommunication and Networks Lab (in FORTH) Each cell of 50cm x 50cm Total 11 IEEE802.11 APs in the area 3.5 APs, on average @ any cell

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CLS variations

features

variationsCriteria Only APs

votePeers vote

Distance computed

CLS confidence interval

CLS-p2p confidence interval

CLS-percentiles percentiles

CLS-particles particle-filter

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Similarities between CLS & Ekahau v3.1

Use IEEE802.11 infrastructure Create map with callibration data Compare trainning & run-time measurements

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Ekahau vs. CLS no peers only APs participate

additional measurements

Percentiles capture more informationabout the distribution of signal strength

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Impact of number of APs

One AP off

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Impact of peersOne extra peer

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Use of Bluetooth instead of IEEE802.11

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Conclusions

The density of landmarks and peers has a dominant impact on positioning

Experiments were repeated at the lab in FORTH and in a conference room @ ACM Mobicom median location error 1.8 m

Incorporation of Bluetooth measurements to improve performance median location error 1.4 m

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Discussion & future work (1/2)

Reduce training, management & calibration overhead Easily detect changes of the environment

density and movement of users or objects new/rogue APs Inaccurate information & measurements

Singular spectrum analysis of signal strength Distinguish the deterministic and noisy components Construct training and run-time signatures based on the

deterministic part

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Discussion & future work (2/2) Incorporate heuristics

about hotspot areas, user presence and mobility information, and topological information of the area (e.g., existence of walls)

Experiment with other wireless technologies Sensors, cameras, and RF tags

I

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UNC/FORTH Archive Online repository of models, tools, and traces

Packet header, SNMP, SYSLOG, signal quality

http://netserver.ics.forth.gr/datatraces/

Free login/ password to access it

Joint effort of Mobile Computing Groups @ FORTH & UNC

maria@csd.uoc.gr

Thank You! Any questions?

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Multimedia Travel Journal Tool Novel p2p location-based application for

visitors Allow multimedia file sharing among mobile

users

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Simulations

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Simulations

Simulation setting (ns-2) 10 landmarks 90 stationary nodes avg connectivity degree = 10 transmission range (R) = 20m

For low connectivity degree or few landmarks the location error is bad

For 10% or more landmarks and connectivity degree of at least 7 the location error is reduced considerably

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Bluetooth estimation experiments

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Bluetooth-only estimationvalidation experiments

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Joint IEEE802.11 & Bluetooth estimation experiments

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Joint IEEE802.11 & Bluetooth estimation experimentsimpact of modalities - performance analysis

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Modality comparison