FreeLoc - Network and Systems Lab

January 18, 2018 | Author: Anonymous | Category: Math, Statistics And Probability, Statistics
Share Embed Donate


Short Description

Download FreeLoc - Network and Systems Lab...

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
View more...

Comments

Copyright � 2017 NANOPDF Inc.
SUPPORT NANOPDF