SpotFi: Decimeter Level Localization using WiFi. Manikanta Kotaru, Kiran Joshi, Dinesh Bharadia, Sachin Katti Stanford University
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1 SpotFi: Decimeter Level Localization using WiFi Manikanta Kotaru, Kiran Joshi, Dinesh Bharadia, Sachin Katti Stanford University
2 Applications of Indoor Localization 2 Targeted Location Based Advertising Indoor Navigation (e.g. Airport Terminals) Real Life Analytics (Gym, Office, etc..) Indoor localization platform providing decimeter-level accuracy could enable a host of applications
3 Easily Deployable Commercial WiFi chips 3
4 Easily Deployable Commercial WiFi chips No hardware or firmware change 4
5 Easily Deployable Commercial WiFi chips No hardware or firmware change No User Intervention 5
6 Easily Deployable, Universal Localize any WiFi device No specialized sensors 6
7 Easily Deployable, Universal, Accurate Error of few tens of centimeters 1 m 7
8 State-of-the-art System Deployable Universal Accurate RADAR, Bahl et al, 00 HORUS, Youssef et al, 05 ArrayTrack, Xiong et al, 13 PinPoint, Joshi et al, 13 CUPID, Sen et al, 13 LTEye, Kumar et al, 14 Phaser, Gjengset et al, 14 Ubicarse, Kumar et al, 14 8
9 State-of-the-art 9 System Deployable Universal Accurate RADAR, Bahl et al, 00 HORUS, Youssef et al, 05 ArrayTrack, Xiong et al, 13 PinPoint, Joshi et al, 13 CUPID, Sen et al, 13 LTEye, Kumar et al, 14 Phaser, Gjengset et al, 14 Ubicarse, Kumar et al, 14 SpotFi, Kotaru et al, 15
10 10 System Overview
11 Localization - Overview 11
12 Localization - Overview 12
13 Challenge - Multipath 13
14 Subcarriers Solving The Multipath Problem State-of-the-art SpotFi Model signal on antennas alone Model signal on both antennas and subcarriers Antennas f 1 f 2 f 3 14 f 4
15 θ 1 θ 2 Step 1: Resolve Multipath 15
16 Signal Modeling Equal Distance Line 16
17 Phase Phase 1 / frequency 0 Distance travelled by the WiFi signal 17
18 Signal Modeling AoA (Angle of Arrival) Equal Phase Line 18
19 Signal Modeling - AoA Define Φ = e Phase at the antenna 1: x = Γ θ 1 Phase at the antenna 2: x = Γ Φ Phase at the antenna 3: x = Γ Φ 19 Γ is complex attenuation of the path. Φ depends on AoA
20 Say There Are Two Paths 20
21 Say There Are Two Paths x = Γ x x = Γ Φ = Γ Φ 21
22 Say There Are Two Paths x = Γ + Γ x = Γ Φ + Γ Φ x = Γ Φ + Γ Φ 22
23 Problem Statement x = Γ + Γ x = Γ Φ + Γ Φ x = Γ Φ + Γ Φ CSI - Known 23
24 Problem Statement x = Γ + Γ x = Γ Φ + Γ Φ x = Γ Φ + Γ Φ Parameters - Unknown 24
25 Problem Statement x = Γ + Γ x = Γ Φ + Γ Φ x = Γ Φ + Γ Φ Number of paths (or AoAs) < Number of antennas (or equations) 25
26 Typical Indoor Multipath 26
27 That s A Problem State-of-the-art Commodity WiFi chips Number of antennas/equations should be atleast 5 27
28 Subcarriers How To Obtain More Equations? Model signal on both antennas and subcarriers Antennas f 1 f 2 f 3 f 4 28
29 Each Subcarrier Gives New Equations f 2 f 1 29
30 Signal Modeling ToF (Time of Flight) Define Ω = e Phase at first subcarrier: x = Γ Phase at second subcarrier: x = Γ Ω 30 Γ is complex attenuation of the path. Ω depends on incoming signal ToF
31 Estimate both AoA and ToF More number of equations in terms of parameter of our interest 31
32 Say There Are Two Paths At first subcarrier, for 3 antennas x = Γ x x = Γ Φ = Γ Φ At second subcarrier, for 3 antennas y y = Γ Ω = Γ Φ Ω 32 y = Γ Φ Ω
33 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 33 y = Γ Φ Ω + Γ Φ Ω
34 Subcarrier 2 Subcarrier 1 Problem Statement CSI - Known x = Γ + Γ x = Γ Φ + Γ Φ x = Γ Φ + Γ Φ y = Γ Ω + Γ y = Γ Φ Ω + Γ Φ Ω 34 y = Γ Φ Ω + Γ Φ Ω
35 Subcarrier 2 Subcarrier 1 Problem Statement 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 35 y = Γ Φ Ω + Γ Φ Ω
36 Subcarrier 2 Subcarrier 1 Problem Statement x = Γ + Γ Number of equations = Number of Subcarriers x Number of Antennas x = Γ Φ + Γ Φ x = Γ Φ + Γ Φ y = Γ Ω + Γ y = Γ Φ Ω + Γ Φ Ω 36 y = Γ Φ Ω + Γ Φ Ω
37 AoA, ToF Estimates θ 1, τ 1 θ 2, τ 2 37
38 θ 1, τ 1 θ 1, τ 1 θ 2, τ 2 Step 2: Identify Direct Path 38
39 AoA, ToF Estimates θ 1, τ 1 θ 2, τ 2 39
40 Use Multiple Packets θ 1, τ 1 θ 2, τ 2 40
41 Use Multiple Packets 41
42 Use Multiple Packets 42
43 Use Multiple Packets 43
44 Direct Path Likelihood Higher weight Smaller ToF Higher weight Lower weight Lower weight Higher weight 44
45 Direct Path Likelihood Lower weight Higher weight Smaller ToF Tighter Cluster Lower weight Lower weight Lower weight 45
46 Direct Path Likelihood Lower weight Higher weight Higher weight Lower weight Smaller ToF Tighter Cluster More Packets Lower weight 46
47 Highest Direct Path Likelihood 47
48 θ 1, τ 1 θ 1, τ 1 θ 2, τ 2 Step 3: Localize The Target 48
49 Use Multiple APs Direct Path AoA = 45 degrees Signal Strength = 10 db Direct Path AoA = -45 degrees Signal Strength = 20 db Direct Path AoA = 10 degrees Signal Strength = 30 db 49 Find location that best explains the AoA and Signal Strength at all the APs
50 Use Different Weights Direct Path AoA = 45 degrees Signal Strength = 10 db Direct Path Likelihood Direct Path AoA = -45 degrees Signal Strength = 20 db Direct Path Likelihood Direct Path AoA = 10 degrees Signal Strength = 30 db Direct Path Likelihood Use different weights for different APs 50
51 51 Evaluation
52 Testbed Access point m Target Locations AP Locations Target
53 Empirical CDF Indoor Office Deployment 16 m ArrayTrack Ubicarse SpotFi 0.3 m 0.4 m 0.4 m 52 m Target Locations 53 AP Locations m Localization Error (m)
54 Stress Test Obstacles Blocking The Direct Path m Target Locations AP Locations
55 Empirical CDF Stress Test Obstacles Blocking The Direct Path m Target Locations AP Locations m Localization Error (m)
56 Empirical CDF Effect of WiFi AP Deployment Density APs 4 APs 5 APs 0.8 m Localization Error (m) 56
57 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
58 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 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 All the icons are from the Noun Project
59 BackFi: High Throughput WiFi Backscatter for IoT Dinesh Bharadia *, Kiran Joshi *, Manikanta Kotaru, Sachin Katti Stanford University *co-primary authors
60 The Internet of Things (IoT) Vision 2
61 The Internet of Things (IoT) Vision Sense 2
62 The Internet of Things (IoT) Vision Sense Collect & Analyze 2
63 The Internet of Things (IoT) Vision Sense Collect & Analyze Control 2
64 The Internet of Things (IoT) Vision Sense Collect & Analyze Control BackFi 2
65 What do we need for IoT Connectivity? Sense Ubiquitous connectivity Low power High uplink rate Sufficient range 3
66 What do we need for IoT Connectivity? Sense Ubiquitous connectivity Low power High uplink rate Sufficient range 3
67 What do we need for IoT Connectivity? Sense Ubiquitous connectivity Low power High uplink rate Sufficient range 3
68 What do we need for IoT Connectivity? Sense Ubiquitous connectivity Low power High uplink rate Sufficient range 3
69 What do we need for IoT Connectivity? Sense Ubiquitous connectivity Low power High uplink rate Sufficient range 3
70 BackFi: Ubiquitous, low power, high throughput connectivity for IoT sensors using ambient WiFi 4
71 BackFi: Ubiquitous, low power, high throughput connectivity for IoT sensors using ambient WiFi Sense 4
72 BackFi: Ubiquitous, low power, high throughput connectivity for IoT sensors using ambient WiFi Sense 4
73 BackFi: Ubiquitous, low power, high throughput connectivity for IoT sensors using ambient WiFi Sense Decode data 4
74 BackFi s Contributions Technical spec Key enabling technique Ubiquitous connectivity Low power Same as WiFi Less than 50 uw Backscatter ubiquitous ambient signals Passive backscatter radios High uplink rate Sufficient range Up to 6.67 Mbps Up to 7m Maximal ratio combining Self-interference cancelation 5
75 BackFi s Contributions Technical spec Key enabling technique Ubiquitous connectivity Low power Same as WiFi Less than 50 uw Backscatter ubiquitous ambient signals Passive backscatter radios High uplink rate Sufficient range Up to 6.67 Mbps Up to 7m Maximal ratio combining Self-interference cancelation 5
76 BackFi s Contributions Technical spec Key enabling technique Ubiquitous connectivity Low power Same as WiFi Less than 50 uw Backscatter ubiquitous ambient signals Passive backscatter radios High uplink rate Sufficient range Up to 6.67 Mbps Up to 7m Maximal ratio combining Self-interference cancelation 5
77 BackFi s Contributions Technical spec Key enabling technique Ubiquitous connectivity Low power Same as WiFi Less than 50 uw Backscatter ubiquitous ambient signals Passive backscatter radios High uplink rate Sufficient range Up to 6.67 Mbps Up to 7m Maximal ratio combining Self-interference cancelation 5
78 BackFi s Contributions Technical spec Key enabling technique Ubiquitous connectivity Low power Same as WiFi Less than 50 uw Backscatter ubiquitous ambient signals Passive backscatter radios High uplink rate Sufficient range Up to 6.67 Mbps Up to 7m Maximal ratio combining Self-interference cancelation 5
79 Related Work 6
80 Related Work Ubiquitous connectivity Low power High uplink rate Sufficient range 6
81 Related Work WiFi- Backscatter Ubiquitous connectivity Low power High uplink rate Sufficient range 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 6
82 Related Work WiFi- Backscatter Ubiquitous connectivity Low power High uplink rate Sufficient range 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 6
83 Related Work WiFi- Backscatter RFID-based Ubiquitous connectivity Low power High uplink rate Sufficient range 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 6
84 Related Work WiFi- Backscatter RFID-based Ubiquitous connectivity Low power High uplink rate Sufficient range 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 6
85 Related Work WiFi- Backscatter RFID-based BackFi 15 Ubiquitous connectivity Low power High uplink rate Sufficient range 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 6
86 Related Work WiFi- Backscatter RFID-based BackFi 15 Ubiquitous connectivity Low power High uplink rate Sufficient range 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 6
87 BackFi s Overview Sense 7
88 BackFi s Overview Sense 7
89 BackFi s Overview Sense 7
90 BackFi s Overview Sense 7
91 BackFi s Overview Sense IoT Sensor 7
92 BackFi s Overview Sense IoT Sensor BackFi AP 7
93 BackFi s Overview Sense IoT Sensor BackFi AP 7
94 IoT Sensor Design Sense 8
95 IoT Sensor Design Sense 8
96 IoT Sensor Design Sense Sensor data
97 IoT Sensor Design Sense Sensor data 0 to -1 1 to
98 IoT Sensor Design Sense Sensor data 0 to -1 1 to Data modulation 8
99 IoT Sensor Design Sense Sensor data 0 to -1 1 to Data modulation 8
100 IoT Sensor Design Sense Sensor data 0 to -1 1 to Data modulation 8
101 BackFi AP Design Sense 9
102 BackFi AP Design Sense 9
103 BackFi AP Design Sense 9
104 BackFi AP Design Sense Received&signal =&& Sensor& backscatter + Environmental& reflections 9
105 Challenge 1: Strong Environmental Reflections 10
106 Challenge 1: Strong Environmental Reflections 20 0 Power in dbm MHz& 2.45 Frequency in GHz 10
107 Challenge 1: Strong Environmental Reflections 20 0 transmitted &signal Power in dbm MHz& 2.45 Frequency in GHz 10
108 Challenge 1: Strong Environmental Reflections Power in dbm MHz& 2.45 Frequency in GHz transmitted &signal sensor& backscatter 10
109 Challenge 1: Strong Environmental Reflections Power in dbm Received&signal =&& 40 MHz& 2.45 Frequency in GHz Sensor& backscatter transmitted &signal sensor& backscatter environmental& reflections + Environmental& reflections 10
110 Challenge 1: Strong Environmental Reflections Power in dbm Received&signal =&& 40 MHz& 2.45 Frequency in GHz Sensor& backscatter transmitted &signal sensor& backscatter environmental& reflections + Environmental& reflections 10
111 Why not use Self-Interference Cancelation? 11
112 Why not use Self-Interference Cancelation? Received signal Transmitted signal 11
113 Why not use Self-Interference Cancelation? Sense Received signal Transmitted signal 11
114 Why not use Self-Interference Cancelation? Sense Received signal Cancelation filter Σ Transmitted signal After&cancelation = 0 11
115 Why not use Self-Interference Cancelation? Sense Received signal Cancelation filter Σ Transmitted signal Received&signal = Sensor backscatter After&cancelation = 0 + Environmental& reflections 11
116 Why not use Self-Interference Cancelation? Sense Received signal Cancelation filter Σ Transmitted signal Sensor Received&signal = backscatter After&cancelation = 0 After&cancelation = 0 + Environmental& reflections 11
117 Eliminating environmental reflections Sense Received signal Cancelation filter Σ Transmitted signal Received&signal = Sensor backscatter After&cancelation = 0 + Environmental& reflections 12
118 Eliminating environmental reflections Sense Received signal Turn off the backscatter Cancelation filter Σ Transmitted signal Received&signal = Sensor backscatter After&cancelation = 0 + Environmental& reflections 12
119 Eliminating environmental reflections Sense Estimate the environmental reflections Received signal Turn off the backscatter Cancelation filter Σ Transmitted signal Received&signal = Sensor backscatter After&cancelation = 0 + Environmental& reflections 12
120 Eliminating environmental reflections Sense Estimate the environmental reflections Received signal Turn off the backscatter Cancelation filter Σ Transmitted signal Received&signal = Sensor backscatter After&cancelation = 0 + Environmental& reflections 12
121 Eliminating environmental reflections Sense Estimate the environmental reflections Received signal Turn on the backscatter Cancelation filter Σ Transmitted signal After&cancelation = 0 Sensor Received&signal = + Environmental& backscatter reflections After&cancelation = Sensor&backscatter 12
122 Challenge 2: Inferring IoT Sensor Data!!! 13
123 Challenge 2: Inferring IoT Sensor Data Sense Sensor backscatter is function of:!!! 13
124 Challenge 2: Inferring IoT Sensor Data Sense Sensor backscatter is function of: Transmitted signal!! 13
125 Challenge 2: Inferring IoT Sensor Data Sense Sensor backscatter is function of: Transmitted signal IoT sensor data! 13
126 Challenge 2: Inferring IoT Sensor Data Sense Sensor backscatter is function of: Transmitted signal IoT sensor data Wireless channel distortions 13
127 Challenge 2: Inferring IoT Sensor Data Sense Sensor backscatter is function of: Transmitted signal IoT sensor data Wireless channel distortions 13
128 Modeling Sensor Backscatter Sense 14
129 Modeling Sensor Backscatter Sense Transmitted signal&= : 14
130 Modeling Sensor Backscatter Sense : h Transmitted signal&= : 14
131 Modeling Sensor Backscatter : h Transmitted signal&= : 15
132 Modeling Sensor Backscatter : h Transmitted signal&= : Sensor data = = 15
133 Modeling Sensor Backscatter : h. = : h Transmitted signal&= : Sensor data = = 15
134 Modeling Sensor Backscatter : h. = : h Transmitted signal&= : Sensor data = = 15
135 Modeling Sensor Backscatter : h. = : h Transmitted signal&= : Sensor data = = Rx=( : h. =) h Rx = sensor&backscatter = ( : h. =) h 15
136 Modeling Sensor Backscatter : h. = : h Transmitted signal&= : Sensor data = = Rx=( : h. =) h Rx = sensor&backscatter = ( : h. =) h 15
137 Estimating Backscatter Channel : h Transmitted signal&= : 16
138 Estimating Backscatter Channel Use predefined sequence of sensor data&= to estimate channel : h Transmitted signal&= : 16
139 Estimating Backscatter Channel Use predefined sequence of sensor data&= to estimate channel : h Transmitted signal&= : Sensor data = 1 16
140 Estimating Backscatter Channel Use predefined sequence of sensor data&= to estimate channel : h. 1 : h Transmitted signal&= : Sensor data = 1 Rx=(: h ) h 16
141 Estimating Backscatter Channel Use predefined sequence of sensor data&= to estimate channel : h. 1 : h Transmitted signal&= : Sensor data = 1 Rx=(: h ) h Rx = sensor&backscatter = : (h h) 16
142 Estimating Backscatter Channel Use predefined sequence of sensor data&= to estimate channel : h. 1 : h Transmitted signal&= : Sensor data = 1 Rx=(: h ) h Rx = sensor&backscatter = : (h h) Estimate h 16
143 Modeling Sensor Backscatter : h. = : h Transmitted signal&= : Sensor data = = Rx=( : h. =) h Rx = sensor&backscatter = 17
144 Modeling Sensor Backscatter : h. = : h Transmitted signal&= : Sensor data = = Rx=( : h. =) h Rx = sensor&backscatter = ( : h. =) h 17
145 Modeling Sensor Backscatter : h. = : h Transmitted signal&= : Sensor data = = Rx=( : h. =) h Rx = sensor&backscatter = ( : h. =) h 17
146 Modeling Sensor Backscatter : h. = : h Transmitted signal&= : Sensor data = = Rx=( : h. =) h Rx = sensor&backscatter = : h. =) h Incoming signal z = : h ( 17
147 Modeling Sensor Backscatter : h. = : h Transmitted signal&= : Sensor data = = Rx=( : h. =) h Rx = sensor&backscatter = : h. =) h Incoming signal z = : h sensor&backscatter = ( ( z. = ) h 17
148 Demodulating = from Sensor Backscatter Sensor Backscatter = {E. =} h 19
149 Demodulating = from Sensor Backscatter Sensor Backscatter = {E. =} h Incoming z 19
150 Demodulating = from Sensor Backscatter Sensor Backscatter = {E. =} h Incoming z 1 sensor data&= -1 19
151 Demodulating = from Sensor Backscatter Sensor Backscatter = {E. =} h Incoming z 1 sensor data&= -1 sensor backscatter 19
152 Demodulating = from Sensor Backscatter Sensor Backscatter = {E. =} h Incoming z 1 sensor data&= -1 sensor backscatter WiFi AP sampling rate 40 Msps IoT sensor Information switching rate 2 Msps 19
153 Demodulating = from Sensor Backscatter Sensor Backscatter = {E. =} h Incoming z 1 sensor data&= -1 sensor backscatter WiFi AP sampling rate 40 Msps 20 IoT sensor Information switching rate 2 Msps 19
154 Demodulating = from Sensor Backscatter Sensor Backscatter = {E. =} h Incoming z 1 sensor data&= -1 sensor backscatter WiFi AP sampling rate 40 Msps 20 IoT sensor Information switching rate 2 Msps 19
155 Demodulating = from Sensor Backscatter Sensor Backscatter = {E. =} h Incoming z 1 sensor data&= -1 sensor backscatter redundancy 19
156 Demodulating = from Sensor Backscatter Sensor Backscatter = {E. =} h Incoming z 1 sensor data&= -1 sensor backscatter redundancy GH:IJHK&LHMIN&ONJPIQIQR = S TUIRhMV& VHJWKUV = S T X & V X & 19
157 Demodulating = from Sensor Backscatter Sensor Backscatter = {E. =} h Incoming z 1 sensor data&= -1 sensor backscatter redundancy GH:IJHK&LHMIN&ONJPIQIQR = S TUIRhMV& VHJWKUV = S T X & V X & 19
158 Demodulating = from Sensor Backscatter Sensor Backscatter = {E. =} h sensor backscatter redundancy GH:IJHK&LHMIN&ONJPIQIQR = S TUIRhMV& VHJWKUV = S T X & V X & 19
159 BackFi Prototype 24
160 BackFi Prototype 24
161 BackFi Prototype Antenna Modulator Digital control board 24
162 BackFi Prototype Antenna Modulator Digital control board WiFi Backscatter radio with BPSK, QPSK & 16 PSK 24
163 BackFi Prototype Antenna Antenna Cancelation filter PA Modulator Digital control board TX RX WiFi Backscatter radio with BPSK, QPSK & 16 PSK 24
164 BackFi Prototype Antenna Antenna Cancelation filter PA Modulator Digital control board WiFi Backscatter radio with BPSK, QPSK & 16 PSK TX RX Built using WARP SDR platform, designed for , BW 20MHz, 20dBm TX power 24
165 BackFi Prototype Antenna Antenna Cancelation filter PA Modulator Digital control board WiFi Backscatter radio with BPSK, QPSK & 16 PSK TX RX Built using WARP SDR platform, designed for , BW 20MHz, 20dBm TX power 24
166 BackFi Prototype 25
167 Testbed & Performance Metrics 6m!!! 8m!! 26
168 Testbed & Performance Metrics Indoor office environment: AP and IoT sensor are placed in LOS! 6m! 8m!! 26
169 Testbed & Performance Metrics Indoor office environment: AP and IoT sensor are placed in LOS WiFi clients are placed nearby! 8m 6m!! 26
170 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. 8m 6m!! 26
171 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 8m 6m 26
172 What is the range and throughput? Throughput in Mbps Throughput of IoT sensor with distance from AP Range in meters 27
173 What is the range and throughput? Throughput in Mbps Throughput of IoT sensor with distance from AP Range in meters 100 Kbps 27
174 What is the range and throughput? Throughput in Mbps Throughput of IoT sensor with distance from AP Range in meters 100 Kbps Three order of magnitude better throughput than prior WiFi backscatter 27
175 What is the power consumption of BackFi? Throughput in Mbps EPB in pj/bit Total Power Consumption in uw for continuous mode
176 What is the power consumption of BackFi? Throughput in Mbps EPB in pj/bit Total Power Consumption in uw for continuous mode Two order magnitude better EPB than prior work 28
177 Conclusion!!! 29
178 Conclusion BackFi provides high throughput, low power, ubiquitous connectivity using ambient WiFi signals!!! 29
179 Conclusion 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!! 29
180 Conclusion 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 amb ient communication signals! 29
181 Conclusion 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 amb ient communication signals Next step: go from a link to a network 29
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