spotfi: decimeter level localization using wifinetseminar.stanford.edu/seminars/10_15_15.pdf ·...
Post on 14-Aug-2020
12 Views
Preview:
TRANSCRIPT
SpotFi: Decimeter Level Localization using WiFi
Manikanta Kotaru, Kiran Joshi, Dinesh Bharadia, Sachin KattiStanford University
Indoor localization platform providing decimeter-level accuracy could enable a host of applications
Targeted Location Based Advertising
Indoor Navigation (e.g. Airport Terminals)
Real Life Analytics(Gym, Office, etc..)
Applications of Indoor Localization
2
Easily Deployable
3
• Commercial WiFi chips
Easily Deployable• Commercial
WiFi chips• No hardware
or firmware change
4
Easily Deployable• Commercial
WiFi chips• No hardware
or firmware change
• No User Intervention
5
Easily Deployable, Universal
6
• Localize any WiFi device
• No specialized sensors
Easily Deployable, Universal, Accurate
7
1 m
• Error of few tens of centimeters
State-of-the-art
8
System Deployable Universal AccurateRADAR, Bahl et al, ’00 9 9 8
HORUS, Youssef et al, ’05 8 9 9
ArrayTrack, Xiong et al, ’13 8 9 9
PinPoint, Joshi et al, ’13 8 9 9
CUPID, Sen et al, ’13 9 8 8
LTEye, Kumar et al, ’14 8 9 9
Phaser, Gjengset et al, ’14 8 9 9
Ubicarse, Kumar et al, ’14 9 8 9
State-of-the-art
9
System Deployable Universal AccurateRADAR, Bahl et al, ’00 9 9 8
HORUS, Youssef et al, ’05 8 9 9
ArrayTrack, Xiong et al, ’13 8 9 9
PinPoint, Joshi et al, ’13 8 9 9
CUPID, Sen et al, ’13 9 8 8
LTEye, Kumar et al, ’14 8 9 9
Phaser, Gjengset et al, ’14 8 9 9
Ubicarse, Kumar et al, ’14 9 8 9
SpotFi, Kotaru et al, ’15 9 9 9
System Overview
10
Localization - Overview
11
Localization - Overview
12
Challenge - Multipath
13
Solving The Multipath ProblemState-of-the-art
14
Model signal on antennas alone Model signal on both antennas and subcarriers
SpotFi
Subc
arrie
rsAntennas
𝒇𝟏𝒇𝟐𝒇𝟑𝒇𝟒
Step 1: Resolve Multipath
15
𝜽𝟏
𝜽𝟐
Signal Modeling
Equal Distance
Line
16
Phase
Distance travelled by the WiFi signal
Phas
e 1 / frequency
0
17
18
Equal PhaseLine
Signal Modeling – AoA (Angle of Arrival)
Signal Modeling - AoA
Define Φ = e
𝜽𝟏
1
Γ is complex attenuation of the path.Φ depends on AoA
Phase at the antenna 1: 𝑥 = Γ
Phase at the antenna 2: 𝑥 = Γ Φ
Phase at the antenna 3: 𝑥 = Γ Φ
19
23
Say There Are Two Paths…
20
Say There Are Two Paths…
21
𝑥 = Γ
𝑥 = Γ Φ
𝑥 = Γ Φ
Say There Are Two Paths…
𝑥 = Γ + Γ
𝑥 = Γ Φ + Γ Φ
𝑥 = Γ Φ + Γ Φ
22
Problem Statement
23
CSI - Known
𝑥 = Γ + Γ
𝑥 = Γ Φ + Γ Φ
𝑥 = Γ Φ + Γ Φ
Problem Statement
24
𝑥 = Γ + Γ
𝑥 = Γ Φ + Γ Φ
𝑥 = Γ Φ + Γ Φ
Parameters - Unknown
Problem Statement
25
Number of paths (or AoAs) < Number of antennas (or equations)
𝑥 = Γ + Γ
𝑥 = Γ Φ + Γ Φ
𝑥 = Γ Φ + Γ Φ
Typical Indoor Multipath
26
That’s A Problem
State-of-the-art Commodity WiFi chips
Number of antennas/equations should be atleast 5
27
How To Obtain More Equations?
28
Model signal on both antennas and subcarriers
Subc
arrie
rs
Antennas
𝒇𝟏𝒇𝟐𝒇𝟑𝒇𝟒
𝒇𝟏
𝒇𝟐
29
Each Subcarrier Gives New Equations
30
Define Ω = e
Γ is complex attenuation of the path.Ω depends on incoming signal ToF
Phase at first subcarrier: 𝑥 = Γ
Phase at second subcarrier: 𝑥 = Γ Ω
Signal Modeling – ToF (Time of Flight)
Estimate both AoA and ToF
31
More number of equations in terms of parameter of our interest
Say There Are Two Paths…
32
At first subcarrier, for 3 antennas
𝑥 = Γ
𝑥 = Γ Φ
𝑥 = Γ Φ
At second subcarrier, for 3 antennas
𝑦 = Γ Ω
𝑦 = Γ Φ Ω
𝑦 = Γ Φ Ω
Say There Are Two Paths…At first subcarrier, for 3 antennas
𝑥 = Γ + Γ
𝑥 = Γ Φ + Γ Φ
𝑥 = Γ Φ + Γ Φ
At second subcarrier, for 3 antennas
𝑦 = Γ Ω + Γ
𝑦 = Γ Φ Ω + Γ Φ Ω
𝑦 = Γ Φ Ω + Γ Φ Ω33
𝑥 = Γ + Γ
𝑥 = Γ Φ + Γ Φ
𝑥 = Γ Φ + Γ Φ
𝑦 = Γ Ω + Γ
𝑦 = Γ Φ Ω + Γ Φ Ω
𝑦 = Γ Φ Ω + Γ Φ Ω
Problem Statement
34
Subc
arrie
r 1Su
bcar
rier 2
CSI - Known
Problem Statement
35
Subc
arrie
r 1Su
bcar
rier 2
Parameters - Unknown
𝑦 = Γ + Γ
𝑦 = Γ Φ + Γ Φ
𝑦 = Γ Φ + Γ Φ
𝑦 = Γ Ω + Γ
𝑦 = Γ Φ Ω + Γ Φ Ω
𝑦 = Γ Φ Ω + Γ Φ Ω
𝑥 = Γ + Γ
𝑥 = Γ Φ + Γ Φ
𝑥 = Γ Φ + Γ Φ
𝑦 = Γ Ω + Γ
𝑦 = Γ Φ Ω + Γ Φ Ω
𝑦 = Γ Φ Ω + Γ Φ Ω
Problem Statement
36
Subc
arrie
r 1Su
bcar
rier 2
Number of equations =
Number of Subcarriersx
Number of Antennas
AoA, ToF Estimates
37
𝜽𝟏, 𝝉𝟏
𝜽𝟐, 𝝉𝟐
Step 2: Identify Direct Path
38
𝜽𝟏, 𝝉𝟏
𝜽𝟐, 𝝉𝟐
𝜽𝟏, 𝝉𝟏
AoA, ToF Estimates
39
𝜽𝟏, 𝝉𝟏
𝜽𝟐, 𝝉𝟐
Use Multiple Packets
40
𝜽𝟏, 𝝉𝟏
𝜽𝟐, 𝝉𝟐
Use Multiple Packets
41
Use Multiple Packets
42
Use Multiple Packets
43
Direct Path Likelihood
Higher weight
Higher weight
Higher weight
Lower weight
Lower weight
44
• Smaller ToF
Direct Path Likelihood
Higher weight
Lower weight
45
Lower weight
Lower weight
• Smaller ToF
• Tighter Cluster
Lower weight
Direct Path Likelihood
Higher weight
46
Higher weight
Lower weight
Lower weight
• Smaller ToF
• Tighter Cluster
• More Packets
Lower weight
Highest Direct Path Likelihood
47
9
Step 3: Localize The Target
48
𝜽𝟏, 𝝉𝟏
𝜽𝟐, 𝝉𝟐
𝜽𝟏, 𝝉𝟏
Use Multiple APs
Direct Path AoA = 45 degreesSignal Strength = 10 dB
Direct Path AoA = 10 degreesSignal Strength = 30 dB Find location that best explains
the AoA and Signal Strengthat all the APs
49
Direct Path AoA = -45 degreesSignal Strength = 20 dB
Use Different Weights
Use different weights for different APs
50
Direct Path AoA = 45 degreesSignal Strength = 10 dBDirect Path Likelihood
Direct Path AoA = 10 degreesSignal Strength = 30 dBDirect Path Likelihood
Direct Path AoA = -45 degreesSignal Strength = 20 dBDirect Path Likelihood
Evaluation
51
52 m
Testbed
52
Access point Target
AP Locations Target Locations
0
0.2
0.4
0.6
0.8
1
0.05 0.5 5
Empi
rical
CDF
Localization Error (m)
Indoor Office Deployment
52 m
16 m
0.4 m
53
ArrayTrack Ubicarse SpotFi
0.3 m 0.4 m 0.4 m
AP Locations Target Locations
Stress Test – Obstacles Blocking The Direct Path
54AP Locations Target Locations
52 m
0
0.2
0.4
0.6
0.8
1
0.05 0.5 5
Empi
rical
CDF
Localization Error (m)
Stress Test – Obstacles Blocking The Direct Path
55
1.3 m
AP Locations Target Locations52 m
Effect of WiFi AP Deployment Density
56
0
0.2
0.4
0.6
0.8
1
0.05 0.5 5
Empi
rical
CDF
Localization Error (m)
3 APs4 APs5 APs
0.8 m
Conclusion
• Deployable: Indoor Localization with commercial WiFi chips
• Accurate: Accuracy comparable to state-of-the-art localization systems which are not suitable for wide deployments
• Universal: Simple localization targets with only a WiFi chip
57
References• J. Xiong and K. Jamieson, “Arraytrack: A fine-grained indoor location system,” NSDI ’13. • S. Kumar, S. Gil, D. Katabi, and D. Rus, “Accurate indoor localization with zero start-up cost,” MobiCom ’14. • P. Bahl and V. N. Padmanabhan, “Radar: An in-building rf-based user location and tracking system,”
INFOCOM 2000. • S. Kumar, E. Hamed, D. Katabi, and L. Erran Li, “Lte radio analytics made easy and accessible,” SIGCOMM ’14. • J. Gjengset, J. Xiong, G. McPhillips, and K. Jamieson, “Phaser: Enabling phased array signal processing on
commodity wifi access points,” MobiCom ’14. • M. Youssef and A. Agrawala, “The horus wlan location determination system,” MobiSys ’05. • S. Sen, J. Lee, K.-H. Kim, and P. Congdon, “Avoiding multipath to revive inbuilding wifi localization,” MobiSys
’13. • K. Joshi, S. Hong, and S. Katti, “Pinpoint: localizing interfering radios,” NSDI ’13. • M. Kotaru, K. Joshi, D. Bharadia, S. Katti, "SpotFi: Decimeter Level Localization Using WiFi," ACM SIGCOMM
2015.• All the icons are from the Noun Project https://thenounproject.com/
BackFi: High Throughput WiFi Backscatter for IoT
Dinesh Bharadia*, Kiran Joshi*, Manikanta Kotaru, Sachin KattiStanford University
*co-primary authors
The Internet of Things (IoT) Vision
2
The Internet of Things (IoT) Vision
Sense
2
Collect & Analyze
The Internet of Things (IoT) Vision
Sense
2
Collect & Analyze Control
The Internet of Things (IoT) Vision
Sense
2
Collect & Analyze Control
The Internet of Things (IoT) Vision
Sense
BackFi
2
Ubiquitous connectivity
Low power
High uplink rate
Sufficient range
What do we need for IoT Connectivity?
Sense
3
Ubiquitous connectivity
Low power
High uplink rate
Sufficient range
What do we need for IoT Connectivity?
Sense
3
Ubiquitous connectivity
Low power
High uplink rate
Sufficient range
What do we need for IoT Connectivity?
Sense
3
Ubiquitous connectivity
Low power
High uplink rate
Sufficient range
What do we need for IoT Connectivity?
Sense
3
Ubiquitous connectivity
Low power
High uplink rate
Sufficient range
What do we need for IoT Connectivity?
Sense
3
BackFi: Ubiquitous, low power, high throughput connectivity for IoT sensors using ambient WiFi
4
Sense
BackFi: Ubiquitous, low power, high throughput connectivity for IoT sensors using ambient WiFi
4
Sense
BackFi: Ubiquitous, low power, high throughput connectivity for IoT sensors using ambient WiFi
4
Sense
BackFi: Ubiquitous, low power, high throughput connectivity for IoT sensors using ambient WiFi
Decodedata
4
Technical spec Key enabling technique
Same as WiFi Backscatter ubiquitous ambient signals
Less than 50 uW Passive backscatter radios
Up to 6.67 Mbps Maximal ratio combining
Up to 7m Self-interference cancelation
Ubiquitousconnectivity
Low power
High uplink rate
Sufficient range
BackFi’s Contributions
5
Technical spec Key enabling technique
Same as WiFi Backscatter ubiquitous ambient signals
Less than 50 uW Passive backscatter radios
Up to 6.67 Mbps Maximal ratio combining
Up to 7m Self-interference cancelation
Ubiquitousconnectivity
Low power
High uplink rate
Sufficient range
BackFi’s Contributions
5
Technical spec Key enabling technique
Same as WiFi Backscatter ubiquitous ambient signals
Less than 50 uW Passive backscatter radios
Up to 6.67 Mbps Maximal ratio combining
Up to 7m Self-interference cancelation
Ubiquitousconnectivity
Low power
High uplink rate
Sufficient range
BackFi’s Contributions
5
Technical spec Key enabling technique
Same as WiFi Backscatter ubiquitous ambient signals
Less than 50 uW Passive backscatter radios
Up to 6.67 Mbps Maximal ratio combining
Up to 7m Self-interference cancelation
Ubiquitousconnectivity
Low power
High uplink rate
Sufficient range
BackFi’s Contributions
5
Technical spec Key enabling technique
Same as WiFi Backscatter ubiquitous ambient signals
Less than 50 uW Passive backscatter radios
Up to 6.67 Mbps Maximal ratio combining
Up to 7m Self-interference cancelation
Ubiquitousconnectivity
Low power
High uplink rate
Sufficient range
BackFi’s Contributions
5
Related Work
6
Related Work
Ubiquitousconnectivity
Low power
High uplink rate
Sufficient range
6
WiFi Backscatter: H. Ishizaki, et. al. “A Battery-less WiFi-BER modulated data transmitter with ambient radio-wave energy harvesting” B. Kellogg et. al. “Wi-Fi Backscatter: Internet Connectivity for RF-Powered Devices”
WiFi-Backscatter
Related Work
Ubiquitousconnectivity
Low power
High uplink rate
Sufficient range
6
WiFi Backscatter: H. Ishizaki, et. al. “A Battery-less WiFi-BER modulated data transmitter with ambient radio-wave energy harvesting” B. Kellogg et. al. “Wi-Fi Backscatter: Internet Connectivity for RF-Powered Devices”
WiFi-Backscatter
Related Work
Ubiquitousconnectivity
Low power
High uplink rate
Sufficient range
6
RFID-based
WiFi Backscatter: H. Ishizaki, et. al. “A Battery-less WiFi-BER modulated data transmitter with ambient radio-wave energy harvesting” B. Kellogg et. al. “Wi-Fi Backscatter: Internet Connectivity for RF-Powered Devices”RFID based: J.F. Ensworth et. al. “Every smart phone is a backscatter reader”, P. Zhang et. al. “Ekhonet”
WiFi-Backscatter
Related Work
Ubiquitousconnectivity
Low power
High uplink rate
Sufficient range
6
RFID-based
WiFi Backscatter: H. Ishizaki, et. al. “A Battery-less WiFi-BER modulated data transmitter with ambient radio-wave energy harvesting” B. Kellogg et. al. “Wi-Fi Backscatter: Internet Connectivity for RF-Powered Devices”RFID based: J.F. Ensworth et. al. “Every smart phone is a backscatter reader”, P. Zhang et. al. “Ekhonet”
WiFi-Backscatter
Related Work
Ubiquitousconnectivity
Low power
High uplink rate
Sufficient range
6
RFID-based BackFi’15
WiFi Backscatter: H. Ishizaki, et. al. “A Battery-less WiFi-BER modulated data transmitter with ambient radio-wave energy harvesting” B. Kellogg et. al. “Wi-Fi Backscatter: Internet Connectivity for RF-Powered Devices”RFID based: J.F. Ensworth et. al. “Every smart phone is a backscatter reader”, P. Zhang et. al. “Ekhonet”
WiFi-Backscatter
Related Work
Ubiquitousconnectivity
Low power
High uplink rate
Sufficient range
6
RFID-based BackFi’15
WiFi Backscatter: H. Ishizaki, et. al. “A Battery-less WiFi-BER modulated data transmitter with ambient radio-wave energy harvesting” B. Kellogg et. al. “Wi-Fi Backscatter: Internet Connectivity for RF-Powered Devices”RFID based: J.F. Ensworth et. al. “Every smart phone is a backscatter reader”, P. Zhang et. al. “Ekhonet”
WiFi-Backscatter
Related Work
Ubiquitousconnectivity
Low power
High uplink rate
Sufficient range
6
BackFi’s Overview
Sense
7
BackFi’s Overview
Sense
7
BackFi’s Overview
Sense
7
BackFi’s Overview
Sense
7
BackFi’s Overview
IoT Sensor
Sense
7
BackFi’s Overview
IoT Sensor BackFi AP
Sense
7
BackFi’s Overview
IoT Sensor BackFi AP
Sense
7
Sense
IoT Sensor Design
8
Sense
IoT Sensor Design
8
Sense
IoT Sensor Design
…10101010…
Sensor data
8
Sense
IoT Sensor Design
…10101010…
Sensor data 0 to -11 to +1
8
Sense
IoT Sensor Design
…10101010…
Sensor data Data modulation0 to -11 to +1
8
Sense
IoT Sensor Design
…10101010…
Sensor data Data modulation0 to -11 to +1
8
Sense
IoT Sensor Design
…10101010…
Sensor data Data modulation0 to -11 to +1
8
BackFi AP Design
Sense
9
BackFi AP Design
Sense
9
BackFi AP Design
Sense
9
BackFi AP Design
Sensor&backscatter
Received&signal =&& +Environmental&reflections
Sense
9
Challenge 1: Strong Environmental Reflections
10
Challenge 1: Strong Environmental Reflections
-100
-80
-60
-40
-20
0
20
2.45
Pow
er in
dBm
Frequency in GHz
10
40 MHz&
Challenge 1: Strong Environmental Reflections
-100
-80
-60
-40
-20
0
20
2.45
Pow
er in
dBm
Frequency in GHz
transmitted&signal
10
40 MHz&
Challenge 1: Strong Environmental Reflections
-100
-80
-60
-40
-20
0
20
2.45
Pow
er in
dBm
Frequency in GHz
sensor&backscatter
transmitted&signal
10
40 MHz&
Challenge 1: Strong Environmental Reflections
-100
-80
-60
-40
-20
0
20
2.45
Pow
er in
dBm
Frequency in GHz
sensor&backscatter
transmitted&signal
environmental&reflections
10
Sensor&backscatter
Received&signal =&& +Environmental&reflections
40 MHz&
Challenge 1: Strong Environmental Reflections
-100
-80
-60
-40
-20
0
20
2.45
Pow
er in
dBm
Frequency in GHz
sensor&backscatter
transmitted&signal
environmental&reflections
10
Sensor&backscatter
Received&signal =&& +Environmental&reflections
40 MHz&
Why not use Self-Interference Cancelation?
11
Why not use Self-Interference Cancelation?
Transmitted signal
Received signal
11
Why not use Self-Interference Cancelation?
Transmitted signal
Received signal
Sense
11
Why not use Self-Interference Cancelation?
Transmitted signal
Received signal
Σ
After&cancelation = 0
Cancelation filter
Sense
11
Why not use Self-Interference Cancelation?
Transmitted signal
Received signal
Σ
After&cancelation = 0
Cancelation filter
Sensorbackscatter
Received&signal = + Environmental&reflections
Sense
11
Why not use Self-Interference Cancelation?
Transmitted signal
Received signal
Σ
After&cancelation = 0
Cancelation filter
Sensorbackscatter
Received&signal = + Environmental&reflections
After&cancelation = 0
Sense
11
Eliminating environmental reflections
Transmitted signal
Received signal
Σ
After&cancelation = 0
Cancelation filter
Sense
Sensorbackscatter
Received&signal = + Environmental&reflections
12
Eliminating environmental reflections
Turn off the backscatter
Transmitted signal
Received signal
Σ
After&cancelation = 0
Cancelation filter
Sense
Sensorbackscatter
Received&signal = + Environmental&reflections
12
Eliminating environmental reflections
Turn off the backscatter
Estimate the environmental reflections
Transmitted signal
Received signal
Σ
After&cancelation = 0
Cancelation filter
Sense
Sensorbackscatter
Received&signal = + Environmental&reflections
12
Eliminating environmental reflections
Turn off the backscatter
Estimate the environmental reflections
Transmitted signal
Received signal
Σ
After&cancelation = 0
Cancelation filter
Sense
Sensorbackscatter
Received&signal = + Environmental&reflections
12
Cancelation filter
Eliminating environmental reflections
After&cancelation = Sensor&backscatter
Estimate the environmental reflections
Turn on the backscatter
Transmitted signal
Received signal
Σ
After&cancelation = 0
Cancelation filter
Sense
Sensorbackscatter
Received&signal = + Environmental&reflections
12
Cancelation filter
Challenge 2: Inferring IoT Sensor Data
!!!
13
Challenge 2: Inferring IoT Sensor Data
Sensor backscatter is function of:!!!
Sense
13
Challenge 2: Inferring IoT Sensor Data
Sensor backscatter is function of:• Transmitted signal!!
Sense
13
Challenge 2: Inferring IoT Sensor Data
Sensor backscatter is function of:• Transmitted signal• IoT sensor data!
Sense
13
Challenge 2: Inferring IoT Sensor Data
Sensor backscatter is function of:• Transmitted signal• IoT sensor data• Wireless channel distortions
Sense
13
Challenge 2: Inferring IoT Sensor Data
Sensor backscatter is function of:• Transmitted signal• IoT sensor data• Wireless channel distortions
Sense
13
Modeling Sensor Backscatter
Sense
14
Modeling Sensor Backscatter
Transmitted signal&= :
Sense
14
Modeling Sensor Backscatter
Transmitted signal&= :
Sense: ∗ ℎ
14
Modeling Sensor Backscatter
: ∗ ℎTransmitted signal&= :
15
Modeling Sensor Backscatter
: ∗ ℎTransmitted signal&= :
Sensor data = =
15
Modeling Sensor Backscatter
: ∗ ℎ . = : ∗ ℎTransmitted signal&= :
Sensor data = =
15
Modeling Sensor Backscatter
: ∗ ℎ . = : ∗ ℎTransmitted signal&= :
Sensor data = =
15
Modeling Sensor Backscatter
Rx = sensor&backscatter =
: ∗ ℎ . = : ∗ ℎTransmitted signal&= :
Sensor data
)∗ ℎ: ∗ ℎ . =Rx=(
)∗ ℎ: ∗ ℎ . =(
= =
15
Modeling Sensor Backscatter
Rx = sensor&backscatter =
: ∗ ℎ . = : ∗ ℎTransmitted signal&= :
Sensor data
)∗ ℎ: ∗ ℎ . =Rx=(
�)∗ ℎ: ∗ ℎ . =(
= =
15
Estimating Backscatter Channel
: ∗ ℎTransmitted signal&= :
16
Estimating Backscatter Channel
: ∗ ℎTransmitted signal&= :
16
Use predefined sequence of sensor data&= to estimate channel
Estimating Backscatter Channel
: ∗ ℎTransmitted signal&= :
Sensor data = 1
16
Use predefined sequence of sensor data&= to estimate channel
Estimating Backscatter Channel
: ∗ ℎ . 1 : ∗ ℎTransmitted signal&= :
Sensor data = 1 )∗ ℎ: ∗ ℎRx=(
16
Use predefined sequence of sensor data&= to estimate channel
Estimating Backscatter Channel
: ∗ ℎ . 1 : ∗ ℎTransmitted signal&= :
Sensor data = 1
Rx = sensor&backscatter =�: ∗ (ℎ ∗ ℎ)
)∗ ℎ: ∗ ℎRx=(
16
Use predefined sequence of sensor data&= to estimate channel
Estimating Backscatter Channel
: ∗ ℎ . 1 : ∗ ℎTransmitted signal&= :
Sensor data = 1
Rx = sensor&backscatter =�: ∗ (ℎ ∗ ℎ)
Estimate h
)∗ ℎ: ∗ ℎRx=(
16
Use predefined sequence of sensor data&= to estimate channel
Modeling Sensor Backscatter
Rx = sensor&backscatter =
: ∗ ℎ . = : ∗ ℎTransmitted signal&= :
Sensor data
)∗ ℎ: ∗ ℎ . =Rx=(= =
17
Modeling Sensor Backscatter
Rx = sensor&backscatter =
: ∗ ℎ . = : ∗ ℎTransmitted signal&= :
Sensor data
)∗ ℎ: ∗ ℎ . =Rx=(
�)∗ ℎ: ∗ ℎ . =(
= =
17
Modeling Sensor Backscatter
Rx = sensor&backscatter =
: ∗ ℎ . = : ∗ ℎTransmitted signal&= :
Sensor data
)∗ ℎ: ∗ ℎ . =Rx=(
�)∗ ℎ: ∗ ℎ . =(
= =
� �
17
Modeling Sensor Backscatter
Rx = sensor&backscatter =
: ∗ ℎ . = : ∗ ℎTransmitted signal&= :
Sensor data
)∗ ℎ: ∗ ℎ . =Rx=(
�)∗ ℎ: ∗ ℎ . =(
= =
� �Incoming signal z = : ∗ ℎ
17
Modeling Sensor Backscatter
Rx = sensor&backscatter =
: ∗ ℎ . = : ∗ ℎTransmitted signal&= :
Sensor data
)∗ ℎ: ∗ ℎ . =Rx=(
�)∗ ℎ: ∗ ℎ . =(
= =
� �Incoming signal z = : ∗ ℎ
sensor&backscatter = )∗ ℎz. =(17
Demodulating = from Sensor BackscatterSensor Backscatter = {E. =} ∗ ℎ
19
Demodulating = from Sensor Backscatter
Incoming z
Sensor Backscatter = {E. =} ∗ ℎ
19
Demodulating = from Sensor Backscatter
sensor data&=
Incoming z
-1
1
Sensor Backscatter = {E. =} ∗ ℎ
19
Demodulating = from Sensor Backscatter
sensor data&=
Incoming z
sensorbackscatter
-1
1
Sensor Backscatter = {E. =} ∗ ℎ
19
Demodulating = from Sensor Backscatter
sensor data&=
Incoming z
sensorbackscatter
-1
1
Sensor Backscatter = {E. =} ∗ ℎ
19
WiFi AP sampling rate
40 Msps
IoT sensor Information switching rate
2 Msps
Demodulating = from Sensor Backscatter
sensor data&=
Incoming z
sensorbackscatter
-1
1
Sensor Backscatter = {E. =} ∗ ℎ
19
WiFi AP sampling rate
40 Msps
IoT sensor Information switching rate
2 Msps20
Demodulating = from Sensor Backscatter
sensor data&=
Incoming z
sensorbackscatter
-1
1
Sensor Backscatter = {E. =} ∗ ℎ
19
WiFi AP sampling rate
40 Msps
IoT sensor Information switching rate
2 Msps20
Demodulating = from Sensor Backscatter
sensor data&=
Incoming z
sensorbackscatter
-1
1
redundancy
Sensor Backscatter = {E. =} ∗ ℎ
19
Demodulating = from Sensor Backscatter
sensor data&=
Incoming z
sensorbackscatter
-1
1
redundancy
Sensor Backscatter = {E. =} ∗ ℎ
19
GH:IJHK&LHMIN&ONJPIQIQR =STUIRℎMV& ∗ VHJWKUV =STX &∗ VX&
Demodulating = from Sensor Backscatter
sensor data&=
Incoming z
sensorbackscatter
-1
1
redundancy
Sensor Backscatter = {E. =} ∗ ℎ
19
GH:IJHK&LHMIN&ONJPIQIQR =STUIRℎMV& ∗ VHJWKUV =STX &∗ VX&
Demodulating = from Sensor Backscatter
sensorbackscatter
redundancy
Sensor Backscatter = {E. =} ∗ ℎ
19
GH:IJHK&LHMIN&ONJPIQIQR =STUIRℎMV& ∗ VHJWKUV =STX &∗ VX&
BackFi Prototype
24
BackFi Prototype
24
BackFi Prototype
24
Modulator
Antenna
Digital control board
WiFi Backscatter radio with BPSK, QPSK & 16 PSK
BackFi Prototype
24
Modulator
Antenna
Digital control board
WiFi Backscatter radio with BPSK, QPSK & 16 PSK
PA
TX RX
Cancelation filter
Antenna
BackFi Prototype
24
Modulator
Antenna
Digital control board
WiFi Backscatter radio with BPSK, QPSK & 16 PSK
PA
TX RX
Cancelation filter
Antenna
Built using WARP SDR platform, designed for 802.11, BW 20MHz, 20dBm TX power
BackFi Prototype
24
Modulator
Antenna
Digital control board
WiFi Backscatter radio with BPSK, QPSK & 16 PSK
PA
TX RX
Cancelation filter
Antenna
Built using WARP SDR platform, designed for 802.11, BW 20MHz, 20dBm TX power
BackFi Prototype
24
Modulator
Antenna
Digital control board
BackFi Prototype
25
Testbed & Performance Metrics
!
!
!
!!
6m
8m
26
Testbed & Performance Metrics
Indoor office environment:• AP and IoT sensor are
placed in LOS!
!
!!
6m
8m
26
Testbed & Performance Metrics
Indoor office environment:• AP and IoT sensor are
placed in LOS• WiFi clients are placed
nearby!
!!
6m
8m
26
Testbed & Performance Metrics
Indoor office environment:• AP and IoT sensor are
placed in LOS• WiFi clients are placed
nearby• Varied the placement of IoT
device, client and WiFi AP.
!!
6m
8m
26
Testbed & Performance Metrics
Indoor office environment:• AP and IoT sensor are
placed in LOS• WiFi clients are placed
nearby• Varied the placement of IoT
device, client and WiFi AP.
Performance metrics• Throughput• Energy per bit
6m
8m
26
What is the range and throughput ?
012345678
0.5 1 2 4 5 6 7
Thro
ughp
ut in
Mbp
s
Range in meters
27
Throughput of IoT sensor with distance from AP
What is the range and throughput ?
012345678
0.5 1 2 4 5 6 7
Thro
ughp
ut in
Mbp
s
Range in meters
100Kbps
27
Throughput of IoT sensor with distance from AP
Three order of magnitude better throughput than prior WiFi backscatter
What is the range and throughput ?
012345678
0.5 1 2 4 5 6 7
Thro
ughp
ut in
Mbp
s
Range in meters
100Kbps
27
Throughput of IoT sensor with distance from AP
Throughput in Mbps
EPB inpJ/bit
Total Power Consumption in uWfor continuous mode
.1 12.66 1.27
.5 5.04 2.52
1 4.10 4.10
2 3.62 7.24
6.67 5.97 39.92
What is the power consumption of BackFi?
28
Throughput in Mbps
EPB inpJ/bit
Total Power Consumption in uWfor continuous mode
.1 12.66 1.27
.5 5.04 2.52
1 4.10 4.10
2 3.62 7.24
6.67 5.97 39.92
What is the power consumption of BackFi?
Two order magnitude better EPB than prior work 28
!
!
!
Conclusion
29
BackFi provides high throughput, low power, ubiquitous connectivity using ambient WiFi signals
!
!
!
Conclusion
29
BackFi provides high throughput, low power, ubiquitous connectivity using ambient WiFi signals
• Not restricted to WiFi, can use other ambient signals such as LTE, Bluetooth
!
!
Conclusion
29
BackFi provides high throughput, low power, ubiquitous connectivity using ambient WiFi signals
• Not restricted to WiFi, can use other ambient signals such as LTE, Bluetooth
• Vision: Build a pervading layer of connectivity over all ambient communication signals
!
Conclusion
29
BackFi provides high throughput, low power, ubiquitous connectivity using ambient WiFi signals
• Not restricted to WiFi, can use other ambient signals such as LTE, Bluetooth
• Vision: Build a pervading layer of connectivity over all ambient communication signals
• Next step: go from a link to a network
Conclusion
29
top related