FreeLoc - Network and Systems Lab
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Transcript FreeLoc - Network and Systems Lab
FreeLoc: Calibration-Free
Crowdsourced Indoor Localization
Sungwon Yang, Pralav Dessai, Mansi Verma and Mario Gerla
UCLA
Neight @ NSlab Study group
5/10/2013
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Outline
Introduction
Fingerprint value extraction
Localization algorithm
Evaluation
Neight @ NSlab Study group
5/10/2013
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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 performance
Neight @ NSlab Study group
5/10/2013
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System overview
Send measured RSSI and
request location info.
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
Neight @ NSlab Study group
5/10/2013
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System Challenges
RSS Measurement for short duration
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
Multi-path fading in indoor environment cause RSSI to fluctuate overtime
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 effecient
Neight @ NSlab Study group
5/10/2013
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Outline
Introduction
Fingerprint value extraction
Localization algorithm
Evaluation
Neight @ NSlab Study group
5/10/2013
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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
Neight @ NSlab Study group
5/10/2013
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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 idea
Fig.(a) an example, mean value work well
Fig.(b) an example, long time & short time variation
could degrades the localization accuracy.
Neight @ NSlab Study group
5/10/2013
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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 𝑾𝑳𝑻 and 𝑾𝑹𝑻
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 𝑾𝑳𝑻 and 𝑾𝑹𝑻 and RSSpeak move slightly left or
right each time depend on environment factors
Neight @ NSlab Study group
5/10/2013
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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
Neight @ NSlab Study group
5/10/2013
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Outline
Introduction
Fingerprint value extraction
Localization algorithm
Evaluation
Neight @ NSlab Study group
5/10/2013
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Localization Algorithm
BSSID vector,
𝑅𝑆𝑆𝐼 < 𝑅𝑆𝑆𝐼𝑘𝑒𝑦 − 𝛿
Fingerprint of
location lx
Relative RSS comparison
Keyi is the BSSID
with ith
strongest RSSI
Surveyors
Users
Neight @ NSlab Study group
5/10/2013
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Localization Algorithm
Let us see the
example…
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Localization Algorithm
8pts
Location result
would be in 101
Relative RSS comparison
Surveyors
Users
1pts
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5/10/2013
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Localization Algorithm
9pts
Location result
would be in 101
Relative RSS comparison
Surveyors
Users
2pts
Neight @ NSlab Study group
5/10/2013
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Localization Algorithm
High rank key
If no high rank key
match, label
location as unknown
Relative RSS comparison
Surveyors
Users
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5/10/2013
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Heterogeneous Devices
Radio map work well, even though heterogeneous devices involved.
Due to not use absolute RSS value, but utilize relationship among RSSI
The 𝛿 relieves the degradation of localization accuracy.
AP not detected
Neight @ NSlab Study group
5/10/2013
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Multiple Surveyors
More than one user can upload their own fingerprints
Maintain only one fingerprint
Update fingerprint become possible, by merge fingerprint
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5/10/2013
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Evaluation
adjacent of point 1.5m
Corridor width 2.5m
Environment Setup
70 different locations at the engineering building in university
Fingerprint comprised information
Timestamp
BSSID (MAC address)
RSSI
Four different devices
adjacent of point 6m
Motorola Bionic, HTC Nexus One, Samsung GalaxyS and GalaxyS2
Two main scenario result would be show in this work
Neight @ NSlab Study group
5/10/2013
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Pairwise Devise Evaluation
Overall, best delta
value is 12
In laboratory, best delta value is around 12, Cross device error<2m
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
Neight @ NSlab Study group
5/10/2013
In 3rd Floor, best delta value is about 9, Cross device error<4m
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Merge Fingerprint
Different device
mechanism might help
fingerprint not affect
to increase localization
localization accuracy
accuracy
Impact of Device Heterogeneity
Wi-Fi fingerprinting data for
each location was taken
from multiple devices and
data from all other mobile
phone devices
Neight @ NSlab Study group
In 3rd Floor
In laboratory
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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
Neight @ NSlab Study group
5/10/2013
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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…
Neight @ NSlab Study group
5/10/2013
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