Neight @ NSlab Study group 1

FreeLoc: Calibration-Free Crowdsourced Indoor Localization

Sungwon Yang, Pralav Dessai, Mansi Verma and Mario GerlaUCLA

5/10/2013

Neight @ NSlab Study group 2

Outline

Introduction Fingerprint value extraction Localization algorithm Evaluation

5/10/2013

Neight @ NSlab Study group 3

Introduction

Investigate 3 major technical issues in crowd sourced indoor localization system:

1. No dedicated surveyor. Can’t afford long-enough time for survey and Can’t sacrifice their device resources

2. No constraint on type & number of device.3. There are no designated fingerprint collection points. Different user can

upload their own fingerprint with same location label. Contributions:

1. Present a method that extracts a reliable single fingerprint value per AP from the short-duration RSS measurements

2. Proposed novel indoor localization method, requires no calibration among heterogeneous devices, resolves the multiple surveyor problem

3. Evaluate system performance5/10/2013

Neight @ NSlab Study group 4

System overview

Multiple-surveyor-Multiple-user System Every one is contributor & user Fast radio map building & update Similar system exists, but still some

challenges not being addressed in the related work

A,B upload Fingerprint data with location label

Send measured RSSI and request location

info.

5/10/2013

Neight @ NSlab Study group 5

System Challenges

RSS Measurement for short duration Multi-path fading in indoor environment cause RSSI to fluctuate overtime

To construction a robust and accurate radio map, more RSSI samples is better Update map / large area is time consuming Short-time measurement is necessary

Device Diversity Different designed hardware ( Wi-Fi chipset, antenna,…etc ), RSSI varies even

though collect at the same location Multiple Measurements for one location in crowd sourced system

Different surveyor might reply different RSSI fingerprint even though they are in the same location area.

Multiple fingerprints for a location is not effecient5/10/2013

Neight @ NSlab Study group 6

Outline

Introduction Fingerprint value extraction Localization algorithm Evaluation

5/10/2013

Neight @ NSlab Study group 7

Fingerprint value extraction

AP response rate AP were not recorded in some fraction of the entire Wi-Fi scanning duration Their preliminary result:

RSSI > -70dbm provides over 90% response rate -70dbm < RSSI < -85dbm provides 50% response rate RSSI < -90dbm provides very poor response rate

Given lower weight to weak RSSI, discount the AP response rate for fingerprint information

5/10/2013

Neight @ NSlab Study group 8

Fingerprint value extraction

RSS variance over time RSSI value observation result in their testbed

Top figure : collect RSSI for 1 HR Middle/Bottom : collect for 1 minute Collect frequency: 0.5-1Hz, depend on different

device Related works often suggests using the mean

value of RSSI or using Gaussian distribution model Fig.(a) an example, the RSSI histograms are

strongly left-skewed. Gaussian model can’t fit well.

Also, mean value is not always the best ideaFig.(a) an example, mean value work wellFig.(b) an example, long time & short time variation could degrades the localization accuracy. 5/10/2013

Neight @ NSlab Study group 9

Extraction Method

Observation Findings: The most-recorded RSSI in the case of the short duration measurements is

very close to the most recorded RSSI in long-duration cases

fpValue is the fingerprint value for an AP RSSpeak is the RSS value with highest frequency The width of the range being averaged is set by and Select stronger RSS value as the fpValue if more than one RSS value has the same

frequency in a histogram However, it’s difficult to adjust and and RSSpeak move slightly left or right

each time depend on environment factors 5/10/2013

Neight @ NSlab Study group 10

Extraction Method Modified

Modified Fingerprint model Use one width w and set it enough large

Euclidean distances between Fpvalue from one-hour measurement and one-minute measurement with respect to log scale

Averaging 50 measurements and more than 10 AP recorded in each measurement and find w

5/10/2013

Neight @ NSlab Study group 11

Outline

Introduction Fingerprint value extraction Localization algorithm Evaluation

5/10/2013

Neight @ NSlab Study group 12

Localization Algorithm

Relative RSS comparison

Surveyors

Users

BSSID vector, Fingerprint of

location lxKeyi is the

BSSID with ith strongest RSSI

5/10/2013

Neight @ NSlab Study group 13

Localization Algorithm

Let us see the example…

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Neight @ NSlab Study group 14

Localization Algorithm

Relative RSS comparisonSurveyors

Users

8pts

1pts

Location result would be in 101

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Neight @ NSlab Study group 15

Localization Algorithm

Relative RSS comparisonSurveyors

Users

9pts

2pts

Location result would be in 101

5/10/2013

Neight @ NSlab Study group 16

Localization Algorithm

Relative RSS comparisonSurveyors

Users

High rank key If no high rank

key match, label location as unknown

5/10/2013

Neight @ NSlab Study group 17

Heterogeneous Devices

Radio map work well, even though heterogeneous devices involved. Due to not use absolute RSS value, but utilize relationship among RSSI Therelieves the degradation of localization accuracy.

AP not detected

5/10/2013

Neight @ NSlab Study group 18

Multiple Surveyors

More than one user can upload their own fingerprints Maintain only one fingerprint Update fingerprint become possible, by merge fingerprint

5/10/2013

Neight @ NSlab Study group 19

Evaluation

Environment Setup 70 different locations at the engineering building in university Fingerprint comprised information

Timestamp BSSID (MAC address) RSSI

Four different devices Motorola Bionic, HTC Nexus One, Samsung GalaxyS and GalaxyS2

Two main scenario result would be show in this work

Corridor width 2.5m

adjacent of point 6m

adjacent of point 1.5m

5/10/2013

Neight @ NSlab Study group 20

Pairwise Devise Evaluation

Find out whether the proposed method of building fingerprint and using it for indoor localization works well with heterogeneous devices

Find out the optimal δ value, to be used for subsequent experiments

Collect data over 3 days

Overall, best delta value is 12

In 3rd Floor, best delta value is about 9, Cross device error<4m

In laboratory, best delta value is around 12, Cross device error<2m

5/10/2013

Neight @ NSlab Study group 21

Impact of Device Heterogeneity

Wi-Fi fingerprinting data for each location was taken from multiple devices and data from all other mobile phone devices

Different device fingerprint not affect localization accuracy

In 3rd Floor In laboratory

Merge Fingerprint mechanism might help to increase

localization accuracy

5/10/2013

Neight @ NSlab Study group 22

Impact of Multiple surveyors

Constructed the fingerprint map for a particular room using heterogeneous devices placed at different parts (levels) of the room.

The user requesting for location information was assumed to be standing at the center of the room.

Every level had three devices, that were different from the user’s device. The higher level would farer from the center.

limits the error in accuracy to less than 3 meters

5/10/2013

Neight @ NSlab Study group 23

Discussion

Magic point: About utilizing the relationship not value for localization Future work:

Filtering erroneous fingerprint data is essential in crowd-sourced systems Since the entire system is based on participation of untrained normal users

Outdated fingerprint data may significantly degrade the localization accuracy

Merge algorithm would failed…

5/10/2013