SlideShare a Scribd company logo
1 of 61
Download to read offline
Implementation and Evaluation
of Indoor Localization System
using WiFi Channel State
Information
Chang-Ning Tsai
Prof. Hsin-Mu Tsai
1
Outline
• Introduction
o Background & Motivation
o Challenge & Related work
• System Model
o Signal Preprocessing
o Doughnut
o System Architecture
o Trilateration
• Methodology
• Evaluation
• Conclusion & Future Work
2
Introduction
3
Background & Motivation
4
• Introduction
o Background &
Motivation
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Background & Motivation
• Offline Phase
• Online Phase
5
• Introduction
o Background &
Motivation
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Background & Motivation
• Offline Phase
6
• Introduction
o Background &
Motivation
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Background & Motivation
• Offline Phase
o Collect training data for model building
7
• Introduction
o Background &
Motivation
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Background & Motivation
• Offline Phase
o Collect training data for model building
o Build a system model
• Propagation model: path loss exponent
• Fingerprinting: radio map
8
• Introduction
o Background &
Motivation
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Background & Motivation
• Online Phase
9
• Introduction
o Background &
Motivation
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Background & Motivation
• Online Phase
o Using the positioning model to estimate the location
10
• Introduction
o Background &
Motivation
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Background & Motivation
• Online Phase
o Using positioning model to estimate location
o Positioning Model:
• Propagation model: trilateration
• Fingerprinting: compare RSS with radio map
11
• Introduction
o Background &
Motivation
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Background & Motivation
12
• Introduction
o Background &
Motivation
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Challenge & Related work
• Reduce the deployment cost
• Reduce the impact of
environmental change
• Provide sufficient accuracy
13
• Introduction
o Challenge & Related
work
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Challenge & Related work
• Ultrasound – additional cost
• Infrared – additional cost
• Visible Light – additional cost
• G-sensor + Accelerometer - landmark
• Magnetic – high deployment cost
• Radio Frequency
14
• Introduction
o Challenge & Related
work
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
System Model
15
Signal Preprocessing
16
• Introduction
• System Model
o Signal Preprocessing
• Methodology
• Evaluation
• Conclusion & Future Work
• Measurement:
o 802.11n
• OFDM-multiple subcarriers
• MIMO-multiple antennas
• Channel State Information(CSI)
Signal Preprocessing
17
• Introduction
• System Model
o Signal Preprocessing
• Methodology
• Evaluation
• Conclusion & Future Work
• Other CSI based positioning system
• Our positioning system
Signal Preprocessing
• Remove outliers
18
• Introduction
• System Model
o Signal Preprocessing
• Methodology
• Evaluation
• Conclusion & Future Work
Why?
1. Human movement
2. Shadowing
3. Small-scale fading
Signal Preprocessing
19
• Introduction
• System Model
o Signal Preprocessing
• Methodology
• Evaluation
• Conclusion & Future Work
• Remove outliers
Signal Preprocessing
20
• Introduction
• System Model
o Signal Preprocessing
• Methodology
• Evaluation
• Conclusion & Future Work
• Remove outliers
Signal Preprocessing
21
• Introduction
• System Model
o Signal Preprocessing
• Methodology
• Evaluation
• Conclusion & Future Work
• Remove outliers
Signal Preprocessing
22
• Introduction
• System Model
o Signal Preprocessing
• Methodology
• Evaluation
• Conclusion & Future Work
• Remove outliers
Signal Preprocessing
23
• Introduction
• System Model
o Signal Preprocessing
• Methodology
• Evaluation
• Conclusion & Future Work
• IFFT
• Choose LOS component
Doughnut
24
• Introduction
• System Model
o Doughnut
• Methodology
• Evaluation
• Conclusion & Future Work
• Estimate the most
probable position.
• Remove all unlikely
positions
Doughnut
• Propagation Model
o Whether Regression is piecewise or not
25
• Introduction
• System Model
o Doughnut
• Methodology
• Evaluation
• Conclusion & Future Work
Doughnut
26
• Introduction
• System Model
o Doughnut
• Methodology
• Evaluation
• Conclusion & Future Work
1.8 11.8
Doughnut
27
• Introduction
• System Model
o Doughnut
• Methodology
• Evaluation
• Conclusion & Future Work
1.8 11.8
Doughnut
28
• Introduction
• System Model
o Doughnut
• Methodology
• Evaluation
• Conclusion & Future Work
1.8 11.8
Doughnut
29
• Introduction
• System Model
o Doughnut
• Methodology
• Evaluation
• Conclusion & Future Work
0 5 10 15
0
5
10
15
AP1
AP
2
AP3
AP4
Doughnut
30
• Introduction
• System Model
o Doughnut
• Methodology
• Evaluation
• Conclusion & Future Work
0 5 10 15
0
5
10
15
AP1
AP
2
AP3
AP4
Doughnut
31
• Introduction
• System Model
o Doughnut
• Methodology
• Evaluation
• Conclusion & Future Work
0 5 10 15
0
5
10
15
AP1
AP
2
AP3
AP4
Doughnut
32
• Introduction
• System Model
o Doughnut
• Methodology
• Evaluation
• Conclusion & Future Work
0 5 10 15
0
5
10
15
AP1
AP
2
AP3
AP4
Doughnut
33
• Introduction
• System Model
o Doughnut
• Methodology
• Evaluation
• Conclusion & Future Work
0 5 10 15
0
5
10
15
AP1
AP
2
AP3
AP4
Doughnut
34
• Introduction
• System Model
o Doughnut
• Methodology
• Evaluation
• Conclusion & Future Work
0 5 10 15
0
5
10
15
AP1
AP
2
AP3
AP4
Estimated location
Ground truth
System Architecture
• Offline phase
35
• Introduction
• System Model
o System Architecture
• Methodology
• Evaluation
• Conclusion & Future Work
Outlier
Removal
Multipath
mitigation
Build RSS and
distance table
Online Phase
System Architecture
• Online phase
36
• Introduction
• System Model
o System Architecture
• Methodology
• Evaluation
• Conclusion & Future Work
Online Phase
Determine possible
distance range for a
particular AP
37
Find intersection of
possible locations of all
APs
Has
intersection ?
Remove
one of the
APs
Find intersection centroid Estimated location
Yes
No
Trilateration
• Trilateration
o Propagation model based
o Calculate distance to a particular AP
o Estimate the user’s location with the coordinates
of and the distances to the APs
38
• Introduction
• System Model
o Trilateration
• Methodology
• Evaluation
• Conclusion & Future Work
Methodology
39
Methodology
• CSI tool
o 30 subcarriers
o CSI is measured from a received packet under the
following conditions:
• The packet is received correctly
• The packet is sent to a hardcoded, fixed mac
address
40
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Intel-5300 NIC
Methodology
41
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Methodology
42
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
0 5 10 15
0
5
10
15
AP1
AP2
AP
3
AP4
CSIE B1
Reference point
Methodology
43
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Methodology
44
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Server request
send packets
Methodology
45
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Send 256 packets
Receive data
and log to file
Methodology
46
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Send ACK to server
Methodology
47
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Wait 3 second
and send request to another AP
Evaluation
48
Evaluation
• Multi-path Error Analysis
49
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Average Error without mitigation: 1.93 m
Average Error with mitigation:1.76 m
Average Error withuot mitigation: 2.14 m
Average Error with mitigation:1.56 m
Trilateration Doughnut
0 2 4 6 8
0
0.2
0.4
0.6
0.8
1
Distance error (m)
Probability
Without multipath mitigation
With multipath mitigation
0 2 4 6 8
0
0.2
0.4
0.6
0.8
1
Distance error (m)
Probability
With multipath mitigation
Without multipath mitigation
Evaluation
• Trilateration
o Cost Analysis
50
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
0 2 4 6 8 10
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Distance error (m)
Probability 165 data
110 data
55 data
35 data
25 data
Evaluation
• Cost Analysis
51
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
0 2 4 6 8 10
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Distance error (m)
Probability
165 data
110 data
55 data
35 data
25 data
0 1 2 3 4 5 6 7 8 9 10
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Distance error (m)
Probability 165 data
110 data
55 data
35 data
25 data
Trilateration Doughnut
Evaluation
• Cost Analysis
52
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
1.4
1.6
1.8
2
2.2
2.4
2.6
2.8
25 45 65 85 105 125 145 165 185
Number of training data
Averageerror
1.4
1.6
1.8
2
2.2
2.4
2.6
2.8
25 45 65 85 105 125 145 165 185
Number of training data
Averageerror
Trilateration Doughnut
Evaluation
• Accuracy of Trilateration and Doughnut
53
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
4AP
0 1 2 3 4 5 6 7
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Distance error (m)
Probability
Doughnut
Trilateration
Trilateration with diff exponent
Average error:
Doughnut: 1.56 m
Trilateration: 1.76 m
Tri piecewise: 1.64 m
Evaluation
• Accuracy of Trilateration and Doughnut
54
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Average error:
Doughnut: 2.08 m
Trilateration: 3.01 m
Tri piecewise: 3.22 m
0 2 4 6 8 10 12 14
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Distance error (m)
Probability
Doughnut
Trilateration
Trilateration with diff exponent
3AP
Evaluation
• Accuracy of Trilateration and Doughnut
55
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
0 1 2 3 4 5
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Distance error (m)
Probability
Doughnut
Trilateration
Trilateration with diff exponent
Average error:
Doughnut: 1.74 m
Trilateration: 1.65 m
Tri piecewise: 1.56 m
3AP
Evaluation
• Accuracy of Trilateration and Doughnut
56
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Average error:
Doughnut: 1.76 m
Trilateration: 2.27 m
Tri piecewise: 2.28 m
0 1 2 3 4 5 6 7
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Distance error (m)
Probability
Doughnut
Trilateration
Trilateration with diff exponent
3AP
Evaluation
• Accuracy of Trilateration and Doughnut
57
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Average error:
Doughnut: 1.90 m
Trilateration: 1.75 m
Tri piecewise: 2.04 m
0 2 4 6 8 10
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Distance error (m)
Probability
Doughnut
Trilateration
Trilateration with diff exponent
3AP
Conclusion & Future Work
58
Conclusion & Future Work
• Doughnut
o Using CSI rather than RSSI
o Remove unlikely locations rather than estimate
the most probable location
o Based on our data, the accuracy is improved by
11.6%
• Trilateration: average error =1.764 m
• Doughnut: average error = 1.560 m
o Reduce the impact of environmental change
59
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Conclusion & Future Work
• Develop a mobile navigation system
o Include time series filter (Kalman or particle filter)
• Large room
o Parking garage
o Supermarket
60
• Introduction
• System Model
• Methodology
• Evaluation
• Conclusion & Future Work
Q & A
61

More Related Content

Viewers also liked

SCIS 2016: An efficient slab encryption using extended SASL protocol
SCIS 2016: An efficient slab encryption using extended SASL protocolSCIS 2016: An efficient slab encryption using extended SASL protocol
SCIS 2016: An efficient slab encryption using extended SASL protocolRuo Ando
 
Wireless Localization: Positioning
Wireless Localization: PositioningWireless Localization: Positioning
Wireless Localization: PositioningStefano Severi
 
H2020 HIGHTS Project Description
H2020 HIGHTS Project DescriptionH2020 HIGHTS Project Description
H2020 HIGHTS Project DescriptionStefano Severi
 
MoLe: Motion Leaks through Smartwatch Sensors
MoLe: Motion Leaks through Smartwatch SensorsMoLe: Motion Leaks through Smartwatch Sensors
MoLe: Motion Leaks through Smartwatch SensorsJoon Young Park
 
Passive Displacement Sensing Using Backscatter RFID with Multiple Loads
Passive Displacement Sensing Using Backscatter RFID with Multiple LoadsPassive Displacement Sensing Using Backscatter RFID with Multiple Loads
Passive Displacement Sensing Using Backscatter RFID with Multiple LoadsJonathan Becker
 
How to discover 1352 Wordpress plugin 0days in one hour (not really)
How to discover 1352 Wordpress plugin 0days in one hour (not really)How to discover 1352 Wordpress plugin 0days in one hour (not really)
How to discover 1352 Wordpress plugin 0days in one hour (not really)Larry Cashdollar
 
Fun with exploits old and new
Fun with exploits old and newFun with exploits old and new
Fun with exploits old and newLarry Cashdollar
 
DerbyCon2016 - Hacking SQL Server on Scale with PowerShell
DerbyCon2016 - Hacking SQL Server on Scale with PowerShellDerbyCon2016 - Hacking SQL Server on Scale with PowerShell
DerbyCon2016 - Hacking SQL Server on Scale with PowerShellScott Sutherland
 
Mining Ruby Gem vulnerabilities for Fun and No Profit.
Mining Ruby Gem vulnerabilities for Fun and No Profit.Mining Ruby Gem vulnerabilities for Fun and No Profit.
Mining Ruby Gem vulnerabilities for Fun and No Profit.Larry Cashdollar
 
2016 aRcTicCON - Hacking SQL Server on Scale with PowerShell (Slide Updates)
2016 aRcTicCON - Hacking SQL Server on Scale with PowerShell (Slide Updates)2016 aRcTicCON - Hacking SQL Server on Scale with PowerShell (Slide Updates)
2016 aRcTicCON - Hacking SQL Server on Scale with PowerShell (Slide Updates)Scott Sutherland
 
How to discover 1352 Wordpress plugin 0days in one hour (not really)
How to discover 1352 Wordpress plugin 0days in one hour (not really)How to discover 1352 Wordpress plugin 0days in one hour (not really)
How to discover 1352 Wordpress plugin 0days in one hour (not really)Larry Cashdollar
 
Securing memcache
Securing memcacheSecuring memcache
Securing memcachewolfSSL
 
Row-level Security
Row-level SecurityRow-level Security
Row-level SecurityKohei KaiGai
 
Statically detecting vulnerability under memory pressure using exhaustive search
Statically detecting vulnerability under memory pressure usingexhaustive searchStatically detecting vulnerability under memory pressure usingexhaustive search
Statically detecting vulnerability under memory pressure using exhaustive searchRuo Ando
 
An extension of cryptographic protocol in distributed in-memory caching syst...
An extension of cryptographic protocol in distributed in-memory caching syst...An extension of cryptographic protocol in distributed in-memory caching syst...
An extension of cryptographic protocol in distributed in-memory caching syst...Ruo Ando
 
Outlook and Exchange for the bad guys
Outlook and Exchange for the bad guysOutlook and Exchange for the bad guys
Outlook and Exchange for the bad guysNick Landers
 
Otter 2016-11-28-01-ss
Otter 2016-11-28-01-ssOtter 2016-11-28-01-ss
Otter 2016-11-28-01-ssRuo Ando
 
Localization in V2X Communication Networks
Localization in V2X Communication NetworksLocalization in V2X Communication Networks
Localization in V2X Communication NetworksStefano Severi
 
Dynamic Beamforming Optimization for Anti-Jamming and Hardware Fault Recovery
Dynamic Beamforming Optimization for Anti-Jamming and Hardware Fault RecoveryDynamic Beamforming Optimization for Anti-Jamming and Hardware Fault Recovery
Dynamic Beamforming Optimization for Anti-Jamming and Hardware Fault RecoveryJonathan Becker
 

Viewers also liked (20)

SCIS 2016: An efficient slab encryption using extended SASL protocol
SCIS 2016: An efficient slab encryption using extended SASL protocolSCIS 2016: An efficient slab encryption using extended SASL protocol
SCIS 2016: An efficient slab encryption using extended SASL protocol
 
Wireless Localization: Positioning
Wireless Localization: PositioningWireless Localization: Positioning
Wireless Localization: Positioning
 
H2020 HIGHTS Project Description
H2020 HIGHTS Project DescriptionH2020 HIGHTS Project Description
H2020 HIGHTS Project Description
 
MoLe: Motion Leaks through Smartwatch Sensors
MoLe: Motion Leaks through Smartwatch SensorsMoLe: Motion Leaks through Smartwatch Sensors
MoLe: Motion Leaks through Smartwatch Sensors
 
Passive Displacement Sensing Using Backscatter RFID with Multiple Loads
Passive Displacement Sensing Using Backscatter RFID with Multiple LoadsPassive Displacement Sensing Using Backscatter RFID with Multiple Loads
Passive Displacement Sensing Using Backscatter RFID with Multiple Loads
 
How to discover 1352 Wordpress plugin 0days in one hour (not really)
How to discover 1352 Wordpress plugin 0days in one hour (not really)How to discover 1352 Wordpress plugin 0days in one hour (not really)
How to discover 1352 Wordpress plugin 0days in one hour (not really)
 
Fun with exploits old and new
Fun with exploits old and newFun with exploits old and new
Fun with exploits old and new
 
DerbyCon2016 - Hacking SQL Server on Scale with PowerShell
DerbyCon2016 - Hacking SQL Server on Scale with PowerShellDerbyCon2016 - Hacking SQL Server on Scale with PowerShell
DerbyCon2016 - Hacking SQL Server on Scale with PowerShell
 
Mining Ruby Gem vulnerabilities for Fun and No Profit.
Mining Ruby Gem vulnerabilities for Fun and No Profit.Mining Ruby Gem vulnerabilities for Fun and No Profit.
Mining Ruby Gem vulnerabilities for Fun and No Profit.
 
2016 aRcTicCON - Hacking SQL Server on Scale with PowerShell (Slide Updates)
2016 aRcTicCON - Hacking SQL Server on Scale with PowerShell (Slide Updates)2016 aRcTicCON - Hacking SQL Server on Scale with PowerShell (Slide Updates)
2016 aRcTicCON - Hacking SQL Server on Scale with PowerShell (Slide Updates)
 
How to discover 1352 Wordpress plugin 0days in one hour (not really)
How to discover 1352 Wordpress plugin 0days in one hour (not really)How to discover 1352 Wordpress plugin 0days in one hour (not really)
How to discover 1352 Wordpress plugin 0days in one hour (not really)
 
Securing memcache
Securing memcacheSecuring memcache
Securing memcache
 
Row-level Security
Row-level SecurityRow-level Security
Row-level Security
 
Statically detecting vulnerability under memory pressure using exhaustive search
Statically detecting vulnerability under memory pressure usingexhaustive searchStatically detecting vulnerability under memory pressure usingexhaustive search
Statically detecting vulnerability under memory pressure using exhaustive search
 
An extension of cryptographic protocol in distributed in-memory caching syst...
An extension of cryptographic protocol in distributed in-memory caching syst...An extension of cryptographic protocol in distributed in-memory caching syst...
An extension of cryptographic protocol in distributed in-memory caching syst...
 
Outlook and Exchange for the bad guys
Outlook and Exchange for the bad guysOutlook and Exchange for the bad guys
Outlook and Exchange for the bad guys
 
Otter 2016-11-28-01-ss
Otter 2016-11-28-01-ssOtter 2016-11-28-01-ss
Otter 2016-11-28-01-ss
 
Localization in V2X Communication Networks
Localization in V2X Communication NetworksLocalization in V2X Communication Networks
Localization in V2X Communication Networks
 
Dynamic Beamforming Optimization for Anti-Jamming and Hardware Fault Recovery
Dynamic Beamforming Optimization for Anti-Jamming and Hardware Fault RecoveryDynamic Beamforming Optimization for Anti-Jamming and Hardware Fault Recovery
Dynamic Beamforming Optimization for Anti-Jamming and Hardware Fault Recovery
 
Hacking Wordpress Plugins
Hacking Wordpress PluginsHacking Wordpress Plugins
Hacking Wordpress Plugins
 

Similar to Final present

Data quality evaluation & orbit identification from scatterometer
Data quality evaluation & orbit identification from scatterometerData quality evaluation & orbit identification from scatterometer
Data quality evaluation & orbit identification from scatterometerMudit Dholakia
 
Kitchen Occupation Project Presentation
Kitchen Occupation Project PresentationKitchen Occupation Project Presentation
Kitchen Occupation Project PresentationMattiasTiger
 
Understanding printed board assembly using simulation with design of experime...
Understanding printed board assembly using simulation with design of experime...Understanding printed board assembly using simulation with design of experime...
Understanding printed board assembly using simulation with design of experime...Kiran Hanjar
 
Testing Dynamic Behavior in Executable Software Models - Making Cyber-physica...
Testing Dynamic Behavior in Executable Software Models - Making Cyber-physica...Testing Dynamic Behavior in Executable Software Models - Making Cyber-physica...
Testing Dynamic Behavior in Executable Software Models - Making Cyber-physica...Lionel Briand
 
pfe_final.pptx
pfe_final.pptxpfe_final.pptx
pfe_final.pptxhani911563
 
Motor vehicle emission checker danu-lap
Motor vehicle emission checker danu-lapMotor vehicle emission checker danu-lap
Motor vehicle emission checker danu-lapaidsdatahub
 
Computational visual system to reduce setup time in CNC vertical machining ce...
Computational visual system to reduce setup time in CNC vertical machining ce...Computational visual system to reduce setup time in CNC vertical machining ce...
Computational visual system to reduce setup time in CNC vertical machining ce...Paulo Araujo
 
RS in the context of Big Data-v4
RS in the context of Big Data-v4RS in the context of Big Data-v4
RS in the context of Big Data-v4Khadija Atiya
 
Graph-Tool in Practice
Graph-Tool in PracticeGraph-Tool in Practice
Graph-Tool in PracticeMosky Liu
 
ScilabTEC 2015 - Noesis Solutions
ScilabTEC 2015 - Noesis SolutionsScilabTEC 2015 - Noesis Solutions
ScilabTEC 2015 - Noesis SolutionsScilab
 
The art of system and solution testing
The art of system and solution testingThe art of system and solution testing
The art of system and solution testinggaoliang641
 
DataONE Education Module 09: Analysis and Workflows
DataONE Education Module 09: Analysis and WorkflowsDataONE Education Module 09: Analysis and Workflows
DataONE Education Module 09: Analysis and WorkflowsDataONE
 
Instrumenting Open vSwitch with Monitoring Capabilities: Designs and Challenges
Instrumenting Open vSwitch with Monitoring Capabilities: Designs and ChallengesInstrumenting Open vSwitch with Monitoring Capabilities: Designs and Challenges
Instrumenting Open vSwitch with Monitoring Capabilities: Designs and ChallengesAJAY KHARAT
 
Topology hiding Multipath Routing Protocol in MANET
Topology hiding Multipath Routing Protocol in MANETTopology hiding Multipath Routing Protocol in MANET
Topology hiding Multipath Routing Protocol in MANETAkshay Phalke
 
Outlier analysis for Temporal Datasets
Outlier analysis for Temporal DatasetsOutlier analysis for Temporal Datasets
Outlier analysis for Temporal DatasetsQuantUniversity
 
productionising-recommenders
productionising-recommendersproductionising-recommenders
productionising-recommendersLudovik Coba
 

Similar to Final present (20)

Data quality evaluation & orbit identification from scatterometer
Data quality evaluation & orbit identification from scatterometerData quality evaluation & orbit identification from scatterometer
Data quality evaluation & orbit identification from scatterometer
 
Kitchen Occupation Project Presentation
Kitchen Occupation Project PresentationKitchen Occupation Project Presentation
Kitchen Occupation Project Presentation
 
Complete (2)
Complete (2)Complete (2)
Complete (2)
 
Paper-review: A Parallel Test Pattern Generation Algorithm to Meet Multiple Q...
Paper-review: A Parallel Test Pattern Generation Algorithm to Meet Multiple Q...Paper-review: A Parallel Test Pattern Generation Algorithm to Meet Multiple Q...
Paper-review: A Parallel Test Pattern Generation Algorithm to Meet Multiple Q...
 
Understanding printed board assembly using simulation with design of experime...
Understanding printed board assembly using simulation with design of experime...Understanding printed board assembly using simulation with design of experime...
Understanding printed board assembly using simulation with design of experime...
 
Testing Dynamic Behavior in Executable Software Models - Making Cyber-physica...
Testing Dynamic Behavior in Executable Software Models - Making Cyber-physica...Testing Dynamic Behavior in Executable Software Models - Making Cyber-physica...
Testing Dynamic Behavior in Executable Software Models - Making Cyber-physica...
 
pfe_final.pptx
pfe_final.pptxpfe_final.pptx
pfe_final.pptx
 
Motor vehicle emission checker danu-lap
Motor vehicle emission checker danu-lapMotor vehicle emission checker danu-lap
Motor vehicle emission checker danu-lap
 
When Should I Use Simulation?
When Should I Use Simulation?When Should I Use Simulation?
When Should I Use Simulation?
 
Computational visual system to reduce setup time in CNC vertical machining ce...
Computational visual system to reduce setup time in CNC vertical machining ce...Computational visual system to reduce setup time in CNC vertical machining ce...
Computational visual system to reduce setup time in CNC vertical machining ce...
 
[Vu Van Nguyen] Test Estimation in Practice
[Vu Van Nguyen]  Test Estimation in Practice[Vu Van Nguyen]  Test Estimation in Practice
[Vu Van Nguyen] Test Estimation in Practice
 
RS in the context of Big Data-v4
RS in the context of Big Data-v4RS in the context of Big Data-v4
RS in the context of Big Data-v4
 
Graph-Tool in Practice
Graph-Tool in PracticeGraph-Tool in Practice
Graph-Tool in Practice
 
ScilabTEC 2015 - Noesis Solutions
ScilabTEC 2015 - Noesis SolutionsScilabTEC 2015 - Noesis Solutions
ScilabTEC 2015 - Noesis Solutions
 
The art of system and solution testing
The art of system and solution testingThe art of system and solution testing
The art of system and solution testing
 
DataONE Education Module 09: Analysis and Workflows
DataONE Education Module 09: Analysis and WorkflowsDataONE Education Module 09: Analysis and Workflows
DataONE Education Module 09: Analysis and Workflows
 
Instrumenting Open vSwitch with Monitoring Capabilities: Designs and Challenges
Instrumenting Open vSwitch with Monitoring Capabilities: Designs and ChallengesInstrumenting Open vSwitch with Monitoring Capabilities: Designs and Challenges
Instrumenting Open vSwitch with Monitoring Capabilities: Designs and Challenges
 
Topology hiding Multipath Routing Protocol in MANET
Topology hiding Multipath Routing Protocol in MANETTopology hiding Multipath Routing Protocol in MANET
Topology hiding Multipath Routing Protocol in MANET
 
Outlier analysis for Temporal Datasets
Outlier analysis for Temporal DatasetsOutlier analysis for Temporal Datasets
Outlier analysis for Temporal Datasets
 
productionising-recommenders
productionising-recommendersproductionising-recommenders
productionising-recommenders
 

Recently uploaded

Multicomponent Spiral Wound Membrane Separation Model.pdf
Multicomponent Spiral Wound Membrane Separation Model.pdfMulticomponent Spiral Wound Membrane Separation Model.pdf
Multicomponent Spiral Wound Membrane Separation Model.pdfGiovanaGhasary1
 
Test of Significance of Large Samples for Mean = µ.pptx
Test of Significance of Large Samples for Mean = µ.pptxTest of Significance of Large Samples for Mean = µ.pptx
Test of Significance of Large Samples for Mean = µ.pptxHome
 
Dev.bg DevOps March 2024 Monitoring & Logging
Dev.bg DevOps March 2024 Monitoring & LoggingDev.bg DevOps March 2024 Monitoring & Logging
Dev.bg DevOps March 2024 Monitoring & LoggingMarian Marinov
 
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdfSummer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdfNaveenVerma126
 
Lecture 1: Basics of trigonometry (surveying)
Lecture 1: Basics of trigonometry (surveying)Lecture 1: Basics of trigonometry (surveying)
Lecture 1: Basics of trigonometry (surveying)Bahzad5
 
Guardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSecGuardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSecTrupti Shiralkar, CISSP
 
Gender Bias in Engineer, Honors 203 Project
Gender Bias in Engineer, Honors 203 ProjectGender Bias in Engineer, Honors 203 Project
Gender Bias in Engineer, Honors 203 Projectreemakb03
 
Technology Features of Apollo HDD Machine, Its Technical Specification with C...
Technology Features of Apollo HDD Machine, Its Technical Specification with C...Technology Features of Apollo HDD Machine, Its Technical Specification with C...
Technology Features of Apollo HDD Machine, Its Technical Specification with C...Apollo Techno Industries Pvt Ltd
 
GENERAL CONDITIONS FOR CONTRACTS OF CIVIL ENGINEERING WORKS
GENERAL CONDITIONS  FOR  CONTRACTS OF CIVIL ENGINEERING WORKS GENERAL CONDITIONS  FOR  CONTRACTS OF CIVIL ENGINEERING WORKS
GENERAL CONDITIONS FOR CONTRACTS OF CIVIL ENGINEERING WORKS Bahzad5
 
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docxSUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docxNaveenVerma126
 
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptx
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptxVertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptx
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptxLMW Machine Tool Division
 
sdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdf
sdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdfsdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdf
sdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdfJulia Kaye
 
cloud computing notes for anna university syllabus
cloud computing notes for anna university syllabuscloud computing notes for anna university syllabus
cloud computing notes for anna university syllabusViolet Violet
 
Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...
Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...
Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...amrabdallah9
 
UNIT4_ESD_wfffffggggggggggggith_ARM.pptx
UNIT4_ESD_wfffffggggggggggggith_ARM.pptxUNIT4_ESD_wfffffggggggggggggith_ARM.pptx
UNIT4_ESD_wfffffggggggggggggith_ARM.pptxrealme6igamerr
 
Mohs Scale of Hardness, Hardness Scale.pptx
Mohs Scale of Hardness, Hardness Scale.pptxMohs Scale of Hardness, Hardness Scale.pptx
Mohs Scale of Hardness, Hardness Scale.pptxKISHAN KUMAR
 
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...Sean Meyn
 

Recently uploaded (20)

Multicomponent Spiral Wound Membrane Separation Model.pdf
Multicomponent Spiral Wound Membrane Separation Model.pdfMulticomponent Spiral Wound Membrane Separation Model.pdf
Multicomponent Spiral Wound Membrane Separation Model.pdf
 
Test of Significance of Large Samples for Mean = µ.pptx
Test of Significance of Large Samples for Mean = µ.pptxTest of Significance of Large Samples for Mean = µ.pptx
Test of Significance of Large Samples for Mean = µ.pptx
 
Dev.bg DevOps March 2024 Monitoring & Logging
Dev.bg DevOps March 2024 Monitoring & LoggingDev.bg DevOps March 2024 Monitoring & Logging
Dev.bg DevOps March 2024 Monitoring & Logging
 
Lecture 4 .pdf
Lecture 4                              .pdfLecture 4                              .pdf
Lecture 4 .pdf
 
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdfSummer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
 
Lecture 1: Basics of trigonometry (surveying)
Lecture 1: Basics of trigonometry (surveying)Lecture 1: Basics of trigonometry (surveying)
Lecture 1: Basics of trigonometry (surveying)
 
Guardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSecGuardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSec
 
Gender Bias in Engineer, Honors 203 Project
Gender Bias in Engineer, Honors 203 ProjectGender Bias in Engineer, Honors 203 Project
Gender Bias in Engineer, Honors 203 Project
 
Technology Features of Apollo HDD Machine, Its Technical Specification with C...
Technology Features of Apollo HDD Machine, Its Technical Specification with C...Technology Features of Apollo HDD Machine, Its Technical Specification with C...
Technology Features of Apollo HDD Machine, Its Technical Specification with C...
 
GENERAL CONDITIONS FOR CONTRACTS OF CIVIL ENGINEERING WORKS
GENERAL CONDITIONS  FOR  CONTRACTS OF CIVIL ENGINEERING WORKS GENERAL CONDITIONS  FOR  CONTRACTS OF CIVIL ENGINEERING WORKS
GENERAL CONDITIONS FOR CONTRACTS OF CIVIL ENGINEERING WORKS
 
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docxSUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
 
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptx
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptxVertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptx
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptx
 
sdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdf
sdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdfsdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdf
sdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdf
 
cloud computing notes for anna university syllabus
cloud computing notes for anna university syllabuscloud computing notes for anna university syllabus
cloud computing notes for anna university syllabus
 
Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...
Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...
Strategies of Urban Morphologyfor Improving Outdoor Thermal Comfort and Susta...
 
Lecture 2 .pdf
Lecture 2                           .pdfLecture 2                           .pdf
Lecture 2 .pdf
 
UNIT4_ESD_wfffffggggggggggggith_ARM.pptx
UNIT4_ESD_wfffffggggggggggggith_ARM.pptxUNIT4_ESD_wfffffggggggggggggith_ARM.pptx
UNIT4_ESD_wfffffggggggggggggith_ARM.pptx
 
Mohs Scale of Hardness, Hardness Scale.pptx
Mohs Scale of Hardness, Hardness Scale.pptxMohs Scale of Hardness, Hardness Scale.pptx
Mohs Scale of Hardness, Hardness Scale.pptx
 
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
 
Présentation IIRB 2024 Marine Cordonnier.pdf
Présentation IIRB 2024 Marine Cordonnier.pdfPrésentation IIRB 2024 Marine Cordonnier.pdf
Présentation IIRB 2024 Marine Cordonnier.pdf
 

Final present

  • 1. Implementation and Evaluation of Indoor Localization System using WiFi Channel State Information Chang-Ning Tsai Prof. Hsin-Mu Tsai 1
  • 2. Outline • Introduction o Background & Motivation o Challenge & Related work • System Model o Signal Preprocessing o Doughnut o System Architecture o Trilateration • Methodology • Evaluation • Conclusion & Future Work 2
  • 4. Background & Motivation 4 • Introduction o Background & Motivation • System Model • Methodology • Evaluation • Conclusion & Future Work
  • 5. Background & Motivation • Offline Phase • Online Phase 5 • Introduction o Background & Motivation • System Model • Methodology • Evaluation • Conclusion & Future Work
  • 6. Background & Motivation • Offline Phase 6 • Introduction o Background & Motivation • System Model • Methodology • Evaluation • Conclusion & Future Work
  • 7. Background & Motivation • Offline Phase o Collect training data for model building 7 • Introduction o Background & Motivation • System Model • Methodology • Evaluation • Conclusion & Future Work
  • 8. Background & Motivation • Offline Phase o Collect training data for model building o Build a system model • Propagation model: path loss exponent • Fingerprinting: radio map 8 • Introduction o Background & Motivation • System Model • Methodology • Evaluation • Conclusion & Future Work
  • 9. Background & Motivation • Online Phase 9 • Introduction o Background & Motivation • System Model • Methodology • Evaluation • Conclusion & Future Work
  • 10. Background & Motivation • Online Phase o Using the positioning model to estimate the location 10 • Introduction o Background & Motivation • System Model • Methodology • Evaluation • Conclusion & Future Work
  • 11. Background & Motivation • Online Phase o Using positioning model to estimate location o Positioning Model: • Propagation model: trilateration • Fingerprinting: compare RSS with radio map 11 • Introduction o Background & Motivation • System Model • Methodology • Evaluation • Conclusion & Future Work
  • 12. Background & Motivation 12 • Introduction o Background & Motivation • System Model • Methodology • Evaluation • Conclusion & Future Work
  • 13. Challenge & Related work • Reduce the deployment cost • Reduce the impact of environmental change • Provide sufficient accuracy 13 • Introduction o Challenge & Related work • System Model • Methodology • Evaluation • Conclusion & Future Work
  • 14. Challenge & Related work • Ultrasound – additional cost • Infrared – additional cost • Visible Light – additional cost • G-sensor + Accelerometer - landmark • Magnetic – high deployment cost • Radio Frequency 14 • Introduction o Challenge & Related work • System Model • Methodology • Evaluation • Conclusion & Future Work
  • 16. Signal Preprocessing 16 • Introduction • System Model o Signal Preprocessing • Methodology • Evaluation • Conclusion & Future Work • Measurement: o 802.11n • OFDM-multiple subcarriers • MIMO-multiple antennas • Channel State Information(CSI)
  • 17. Signal Preprocessing 17 • Introduction • System Model o Signal Preprocessing • Methodology • Evaluation • Conclusion & Future Work • Other CSI based positioning system • Our positioning system
  • 18. Signal Preprocessing • Remove outliers 18 • Introduction • System Model o Signal Preprocessing • Methodology • Evaluation • Conclusion & Future Work Why? 1. Human movement 2. Shadowing 3. Small-scale fading
  • 19. Signal Preprocessing 19 • Introduction • System Model o Signal Preprocessing • Methodology • Evaluation • Conclusion & Future Work • Remove outliers
  • 20. Signal Preprocessing 20 • Introduction • System Model o Signal Preprocessing • Methodology • Evaluation • Conclusion & Future Work • Remove outliers
  • 21. Signal Preprocessing 21 • Introduction • System Model o Signal Preprocessing • Methodology • Evaluation • Conclusion & Future Work • Remove outliers
  • 22. Signal Preprocessing 22 • Introduction • System Model o Signal Preprocessing • Methodology • Evaluation • Conclusion & Future Work • Remove outliers
  • 23. Signal Preprocessing 23 • Introduction • System Model o Signal Preprocessing • Methodology • Evaluation • Conclusion & Future Work • IFFT • Choose LOS component
  • 24. Doughnut 24 • Introduction • System Model o Doughnut • Methodology • Evaluation • Conclusion & Future Work • Estimate the most probable position. • Remove all unlikely positions
  • 25. Doughnut • Propagation Model o Whether Regression is piecewise or not 25 • Introduction • System Model o Doughnut • Methodology • Evaluation • Conclusion & Future Work
  • 26. Doughnut 26 • Introduction • System Model o Doughnut • Methodology • Evaluation • Conclusion & Future Work 1.8 11.8
  • 27. Doughnut 27 • Introduction • System Model o Doughnut • Methodology • Evaluation • Conclusion & Future Work 1.8 11.8
  • 28. Doughnut 28 • Introduction • System Model o Doughnut • Methodology • Evaluation • Conclusion & Future Work 1.8 11.8
  • 29. Doughnut 29 • Introduction • System Model o Doughnut • Methodology • Evaluation • Conclusion & Future Work 0 5 10 15 0 5 10 15 AP1 AP 2 AP3 AP4
  • 30. Doughnut 30 • Introduction • System Model o Doughnut • Methodology • Evaluation • Conclusion & Future Work 0 5 10 15 0 5 10 15 AP1 AP 2 AP3 AP4
  • 31. Doughnut 31 • Introduction • System Model o Doughnut • Methodology • Evaluation • Conclusion & Future Work 0 5 10 15 0 5 10 15 AP1 AP 2 AP3 AP4
  • 32. Doughnut 32 • Introduction • System Model o Doughnut • Methodology • Evaluation • Conclusion & Future Work 0 5 10 15 0 5 10 15 AP1 AP 2 AP3 AP4
  • 33. Doughnut 33 • Introduction • System Model o Doughnut • Methodology • Evaluation • Conclusion & Future Work 0 5 10 15 0 5 10 15 AP1 AP 2 AP3 AP4
  • 34. Doughnut 34 • Introduction • System Model o Doughnut • Methodology • Evaluation • Conclusion & Future Work 0 5 10 15 0 5 10 15 AP1 AP 2 AP3 AP4 Estimated location Ground truth
  • 35. System Architecture • Offline phase 35 • Introduction • System Model o System Architecture • Methodology • Evaluation • Conclusion & Future Work Outlier Removal Multipath mitigation Build RSS and distance table Online Phase
  • 36. System Architecture • Online phase 36 • Introduction • System Model o System Architecture • Methodology • Evaluation • Conclusion & Future Work Online Phase Determine possible distance range for a particular AP
  • 37. 37 Find intersection of possible locations of all APs Has intersection ? Remove one of the APs Find intersection centroid Estimated location Yes No
  • 38. Trilateration • Trilateration o Propagation model based o Calculate distance to a particular AP o Estimate the user’s location with the coordinates of and the distances to the APs 38 • Introduction • System Model o Trilateration • Methodology • Evaluation • Conclusion & Future Work
  • 40. Methodology • CSI tool o 30 subcarriers o CSI is measured from a received packet under the following conditions: • The packet is received correctly • The packet is sent to a hardcoded, fixed mac address 40 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work Intel-5300 NIC
  • 41. Methodology 41 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work
  • 42. Methodology 42 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work 0 5 10 15 0 5 10 15 AP1 AP2 AP 3 AP4 CSIE B1 Reference point
  • 43. Methodology 43 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work
  • 44. Methodology 44 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work Server request send packets
  • 45. Methodology 45 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work Send 256 packets Receive data and log to file
  • 46. Methodology 46 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work Send ACK to server
  • 47. Methodology 47 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work Wait 3 second and send request to another AP
  • 49. Evaluation • Multi-path Error Analysis 49 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work Average Error without mitigation: 1.93 m Average Error with mitigation:1.76 m Average Error withuot mitigation: 2.14 m Average Error with mitigation:1.56 m Trilateration Doughnut 0 2 4 6 8 0 0.2 0.4 0.6 0.8 1 Distance error (m) Probability Without multipath mitigation With multipath mitigation 0 2 4 6 8 0 0.2 0.4 0.6 0.8 1 Distance error (m) Probability With multipath mitigation Without multipath mitigation
  • 50. Evaluation • Trilateration o Cost Analysis 50 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work 0 2 4 6 8 10 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Distance error (m) Probability 165 data 110 data 55 data 35 data 25 data
  • 51. Evaluation • Cost Analysis 51 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work 0 2 4 6 8 10 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Distance error (m) Probability 165 data 110 data 55 data 35 data 25 data 0 1 2 3 4 5 6 7 8 9 10 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Distance error (m) Probability 165 data 110 data 55 data 35 data 25 data Trilateration Doughnut
  • 52. Evaluation • Cost Analysis 52 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 25 45 65 85 105 125 145 165 185 Number of training data Averageerror 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 25 45 65 85 105 125 145 165 185 Number of training data Averageerror Trilateration Doughnut
  • 53. Evaluation • Accuracy of Trilateration and Doughnut 53 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work 4AP 0 1 2 3 4 5 6 7 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Distance error (m) Probability Doughnut Trilateration Trilateration with diff exponent Average error: Doughnut: 1.56 m Trilateration: 1.76 m Tri piecewise: 1.64 m
  • 54. Evaluation • Accuracy of Trilateration and Doughnut 54 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work Average error: Doughnut: 2.08 m Trilateration: 3.01 m Tri piecewise: 3.22 m 0 2 4 6 8 10 12 14 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Distance error (m) Probability Doughnut Trilateration Trilateration with diff exponent 3AP
  • 55. Evaluation • Accuracy of Trilateration and Doughnut 55 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Distance error (m) Probability Doughnut Trilateration Trilateration with diff exponent Average error: Doughnut: 1.74 m Trilateration: 1.65 m Tri piecewise: 1.56 m 3AP
  • 56. Evaluation • Accuracy of Trilateration and Doughnut 56 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work Average error: Doughnut: 1.76 m Trilateration: 2.27 m Tri piecewise: 2.28 m 0 1 2 3 4 5 6 7 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Distance error (m) Probability Doughnut Trilateration Trilateration with diff exponent 3AP
  • 57. Evaluation • Accuracy of Trilateration and Doughnut 57 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work Average error: Doughnut: 1.90 m Trilateration: 1.75 m Tri piecewise: 2.04 m 0 2 4 6 8 10 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Distance error (m) Probability Doughnut Trilateration Trilateration with diff exponent 3AP
  • 59. Conclusion & Future Work • Doughnut o Using CSI rather than RSSI o Remove unlikely locations rather than estimate the most probable location o Based on our data, the accuracy is improved by 11.6% • Trilateration: average error =1.764 m • Doughnut: average error = 1.560 m o Reduce the impact of environmental change 59 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work
  • 60. Conclusion & Future Work • Develop a mobile navigation system o Include time series filter (Kalman or particle filter) • Large room o Parking garage o Supermarket 60 • Introduction • System Model • Methodology • Evaluation • Conclusion & Future Work

Editor's Notes

  1. 各位口委以及他在場的聽眾大家好!我是蔡欣穆老師的學生:蔡彰寧。今天我要講的題目是Implementation and Evaluation of Indoor Localization system using WiFi Channel State Information! (使用無線區域網路頻道狀態資訊的室內定位系統實作與評估)
  2. 設計系統的motivation 之後會overview我們的系統,講一下我們的system architecture Methodology: 實驗方法,器材,環境 Evaluation: 實驗結果的評估
  3. GPS:無法使用的原因 有3~5公尺誤差 建築物建材的影響,導致GPS誤差增加 利用其他的方法來做定位系統
  4. 一般室內導航主要分兩個部分 Offline phase Online phase
  5. 什麼事offline phase? 假設佈置好device 不知道devicey的signal在環境中的樣子
  6. 所以需要事先搜集一些data,來得知device signal在環境中的樣子
  7. 在測量過後可以得到radio map或是propagation model的參數 也就是說我們可以得到positioning model 而這個model會online phase所使用
  8. Online phase是什麼?
  9. 就是用在offline phase所得到的positioning model去估計使用者的位置
  10. 根據這些positioning model我們就可以使用一些定位的演算法 比較常見的就是用propagation model估計出來的距離使用三角定位 或是使用radio map來比較最有可能的位置
  11. 這是一張cost和accuracy的比較圖 剛剛提到定位的方式主要有裡用propagation model搭配三角定位或是fingerprint與radio map比較 過去大家認為fingerprint accuracy準度夠好 但是至命缺點是deployment cost太高 環境改變,或是device放置的位置不同,radio map就需要重新測量 所以在我們希望能夠去得平衡,在減低deployment cost的情況下又希望增加accuracy 以這動機當出發點去設計我們的定位系統
  12. 在這裡列出一些目前現有的其他定位系統 Ultrasound+infrared可能使用time of flight 的方式來估計距離然後使用三角定位來估計使用者的位置 但是需要額外購買設備 G-sensor+ Accelerometer使用dead reckoning 方式來估計使用者的位置,但dead reckoning的缺點是誤差會累加,因此需要landmark,而這些landmark通常不好找 Magnetic使用Fingerprinting方式,缺點是環境變動可能導致需要重新來佈置定位系統來維持精准度 因此最後我們決定選擇使用Radio Frequency也就是WiFi來實作我們的室內定位系統
  13. 首先我們的系統會先對訊號做一些preprocessing Channel state information: 是指目前channel的狀態,以complex number表示,代表各個subcarrier訊號衰減的程度,以及相位差的資訊
  14. LOS是什麼?訓好是筆直地從transmitter送往receiver,而不是經過反射折射的路徑到達 為什麼LOS會比較准?因為經過反射折射的路徑會引響我們估計AP的距離 為什麼會認為最強的訊號LOS ----- 會議記錄 (2014/7/19 21:12) ----- 為什麼要取LOS,但為什麼LOS會比較准,為什麼最強的事LOS 第三個點是怎麼去除掉,經過不同路徑在不同時間到達
  15. 核心!Doughnut不像傳統的方法是去估計最有可能的距離,而是把最不可能的距離給去掉!
  16. 我們的定位演算法是根據Propagation model來做改良的 解釋圖->橫軸是…,縱軸是…, 藍點是… 在圖中可以看到,室內環境的propagation model可能用piecewise 的function來估計可能會比較好 但是到底要用幾條piecewise function來fitting model其實不是很容易 因此在我們設計的演算法當中我們避開了這一個問題(然後下一張投影片)
  17. ----- 會議記錄 (2014/7/19 21:12) ----- 橫軸是距離,縱軸事power,但是這總估計可能會有一些因素,不準確,所以我們把測量到的強度汗距離顯示在途上 所有收到的power汗距離的對應全部標示出來,同樣距離用一根分不表示
  18. ----- 會議記錄 (2014/7/19 21:29) ----- outlier removeal -> multi-path mitigation -> build table 圖大一點 小字不用
  19. ----- 會議記錄 (2014/7/19 21:29) ----- 用兩頁
  20. 利用估計出來Propagation model的參數估計和特定AP最可能的距離 多個估計的距離再使用三角定位估計目前使用者的位置 在evaluation我們演算法的差異
  21. ----- 會議記錄 (2014/7/19 21:29) ----- 型號,mimi pci, intel 5300 筆電網卡 限制 我們在收資料是在市面上買的到網卡,這張網卡的firmware有做特別設計,可收CSI,如果未來網卡有做這樣的設計即可使用網卡來設計
  22. 我們測量的情況是比較不平均的 原因在於有些環境的某些地方比較難測量
  23. ----- 會議記錄 (2014/7/19 21:29) ----- 模擬出來的AP來送出beacon,如此我才能辨別出哪個AP所送出來的 WiFi AP會定期送出beacon,未來可能可以使用這些beacon來利用我們演算法做定位
  24. 因為三角定位是要去找error最小的path loss model,當資料量呈現分佈較頻君的時候,path loss model參數都差不多,所以導致都差不多
  25. ----- 會議記錄 (2014/7/19 21:48) ----- 放上一張頭應片右圖 資料從少變多,一旦多到一個程度以後,準確度就很好,放大,小圖, 改圖,找資料量多accuracy高
  26. box plot中, 若X落在 X>Q3+3*IQR 或 X<Q1-3*IQR 時,則稱為離群值(Outlier)
  27. 這是一個使用4隻AP的結果
  28. 有的時候室內的環境可能AP數量較少,以三角定位來說至少要三支AP才能做定位 因此我們在這裡分析三隻AP的結果 可以看到如果有AP壞掉的話,三角定位所定出來的結果會比較差一點 代表我們的演算法對於AP壞掉影響accuracy的敏感度較低
  29. 在這裡可以看到,如果在offline phase所收到的資料不錯 三角定位會和Doughnut差不多
  30. 這是另外一個Doughnut比三角定位好的結果
  31. 這是另外一個資料收集的比較好的結果可以看到和Doughnut差不多 所以可以知道Doughnut普遍比三角定位的結果好 如果比較差的會也會和三角定位差不多