RF-based Internet-of-Thing (IoT) Techniques Kate C.-J. Lin Academia Sinica

Transcription

1 RF-based Internet-of-Thing (IoT) Techniques Kate C.-J. Lin Academia Sinica!

2 Localization! Object Tracking! Gesture Detection! Human Computer Interaction! Health Care

3 See through walls [sigcomm 13]

4 Applications Police'avoids'ambush' Firefighters'check'for'humans' Personal'security' Gesture'interface'from' behind'a'wall'

5 Key Idea Track people from WiFi signals reflected by human body

6 Challenges Wall refection is 10,000x stronger than reflections coming from behind the wall!

7 How to Eliminate the Wall s Reflection? Idea: transmit two waves that cancel each other when they reflect off static objects, but not moving object Wall is static disappears People tent to move detectable

8 Leveraging MIMO techniques Receive)Antenna:) x # Transmit) Antennas) αx #

9 Eliminating Walls Reflection Receive#Antenna:# y#=#h 1 #x#+#h 2 αx # 0 " x " h 1# α"="%h 1 "/"h 2 " " αx " h 2#

10 Eliminating All Static Reflection

11 Motion tracking via Radio Reflection [NSDI 14]

12 WiTrack Centimeter-scale motion tracking using only radio reflections off the human body! Works behind walls and does not require person to hold any device

13 How WiTrack Works? Tx Rx Distance = Reflection Time x Speed of Light

14 Naive Solution: Measuring Reflection time Transmit short pulse and listen for echo Tx#pulse# Rx#pulse# Reflec)on%Time% Time %

15 Naive Solution: Measuring Reflection time Transmit short pulse and listen for echo Tx#pulse# Rx#pulse# Reflec)on%Time% Time % Capturing the pulse needs sub-nanosecond sampling Multi-GHz samplers are expensive and have high noise Impractical

16 Frequency Modulated Carrier Wave (FMCW) Transmi/ed % Frequency % Reflec%on(Time( ΔF( =( ΔF( slope( Received % t t+δt% Time %! ΔF Reflection Time Distance

17 How to Measure ΔF Power & Transmi,ed & Received & Mixer& FFT& ΔF# Subtracting frequency is easy! Done using a mixer signals whose frequency is ΔF

18 Removing Static Multi-Path Distance (meters) Distance (meters) Time (seconds) (a) Spectrogram Time (seconds) (b) After Background Subtraction Time of Flight (ToF) reflected from static objects stays constant! Subtract the background signals and extract moving objects

19 From Distance to Localization d # Rx# Tx# Can be anywhere on an ellipse whose foci are (Tx, Rx)! One ellipse is not enough for localization

20 Using Multiple Receive Antennas in#beam# d # d # Rx# Tx# Rx # Extend to 3D by using 3 Rx antennas and taking the intersection of ellipsoids

21 } (a) Inhale Motion (b) Exhale Motion Figure 1 Chest Motion Changes the Signal Reflection Time. (a) Monitoring Breathing and Heart Rate [CHI 15]

22 How? Measuring Variation Heartbeats' Phase (in radians) 1.5 Exhale' Inhale' Time (in minutes) gure 3 Phase variation due to vital signs. The figure sho First localize the user and identify its reflection! Zoom in on the reflected signal and analyze variations

23 Gesture Recognition Using Wireless Signals [MobiCom 13]

24 Idea: Doppler shift Frequency change of a wave occurs as its source moves relative to the observer! Different moving patterns result in various Doppler shifts! Waves propagating at the speed of light (C m/s)! Assume a person moves at a speed of v! Doppler shift Δf=2fv/C (e.g., 0.5m/s corresponding to 17Hz Doppler shift on a 5GHz band)

25 Challenges 312.5KHz per subcarrier 0.4 Amplitude OFDM Sub-channel Small Doppler shift (e.g., 10-20Hz shift) is not detectable in wide-band OFDM channels (e.g., 20MHz/64 ~313KHz per subcarrier)

26 How to Identify Small Shift even in Wideband Channels? Idea: Transform the WiFi signals to narrowband pulses via large FFT! Amplitude Amplitude OFDM Sub-channel FFT over one symbol OFDM Sub-channel FFT over two identical symbol

27 Capturing movement via Large FFT FFT over 10,000 symbols 31.25Hz per subcarrier Amplitude OFDM Sub channels without movement x Amplitude OFDM Sub channels with movement x 10 4

28 Capturing over Time frequency (Hz) dB time (second) Frequency-time Doppler profile of an example gesture (push)

29 Detection by Classification (a) (d) (g) (b) (e) (h) (c) (f) Different gestures correspond to various frequency-time Doppler profiles (i)

30 Other Applications Sleep Apnea Detection! Hand Writing / Drawing! WiFi Imaging! Keystroke!