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FreeLoc : Calibration-Free Crowdsourced Indoor Localization. Sungwon Yang, Pralav Dessai , Mansi Verma and Mario Gerla UCLA. Outline. Introduction Fingerprint value extraction Localization algorithm Evaluation. Introduction. - PowerPoint PPT Presentation
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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…
5/10/2013
Neight @ NSlab Study group 14
Localization Algorithm
Relative RSS comparisonSurveyors
Users
8pts
1pts
Location result would be in 101
5/10/2013
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