FreeLoc - Network and Systems Lab
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Description
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
4
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
6
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
12
Localization Algorithm Let us see the example…
Neight @ NSlab Study group
5/10/2013
13
Localization Algorithm 8pts
Location result would be in 101
Relative RSS comparison Surveyors
Users 1pts Neight @ NSlab Study group
5/10/2013
14
Localization Algorithm 9pts
Location result would be in 101
Relative RSS comparison Surveyors
Users 2pts Neight @ NSlab Study group
5/10/2013
15
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
16
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
18
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
19
Pairwise Devise Evaluation
Overall, best delta value is 12
In laboratory, best delta value is around 12, Cross device error
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