FreeLoc: Calibration-Free
Crowdsourced Indoor Localization
Sungwon Yang, Pralav Dessai, Mansi Verma and Mario Gerla
UCLA
Neight @ NSlab Study group
5/10/2013
1
Outline

Introduction

Fingerprint value extraction

Localization algorithm

Evaluation
Neight @ NSlab Study group
5/10/2013
2
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
3
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
5
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
7
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
8
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
9
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
10
Outline

Introduction

Fingerprint value extraction

Localization algorithm

Evaluation
Neight @ NSlab Study group
5/10/2013
11
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…
Neight @ NSlab Study group
5/10/2013
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Localization Algorithm
8pts

Location result
would be in 101
Relative RSS comparison
Surveyors
Users
1pts
Neight @ NSlab Study group
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
Neight @ NSlab Study group
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
17
Multiple Surveyors

More than one user can upload their own fingerprints

Maintain only one fingerprint

Update fingerprint become possible, by merge fingerprint
Neight @ NSlab Study group
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
20
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
5/10/2013
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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
22
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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