SLIDE 1
RF-based DFAR and implicit ad-hoc usable security
Stephan Sigg
Aalto University, Communications and Networking
Group DFAR Conclusion 2 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
RF-based DFAR and implicit ad-hoc usable security Stephan Sigg - - PowerPoint PPT Presentation
RF-based DFAR and implicit ad-hoc usable security Stephan Sigg - - PowerPoint PPT Presentation
RF-based DFAR and implicit ad-hoc usable security Stephan Sigg Aalto University, Communications and Networking July 1, 2016 Group DFAR Conclusion 2 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security Group DFAR Conclusion
SLIDE 2
SLIDE 3
Group DFAR Conclusion 2 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 4
Group DFAR Conclusion 2 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 5
Group DFAR Conclusion 3 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 6
Group DFAR Conclusion 3 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 7
Group DFAR Conclusion 4 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 8
Group DFAR Conclusion
Project: RF-based device-free activity recognition
5 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 9
Group DFAR Conclusion
RF-based activity recognition
Sensewaves Video
6 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 10
Group DFAR Conclusion
RF-based device-free activity recognition
L y i n g empty S t a n d i n g Crawling W a l k i n g
7 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 11
Group DFAR Conclusion
RF-based device-free activity recognition
L y i n g empty S t a n d i n g Crawling W a l k i n g
7 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 12
Group DFAR Conclusion
Features
Assume that samples are taken on the signal strenth of an incoming signal for a window Mean signal strength
The mean signal strength over a window of measurements represents static characteristic changes in the received signal strength. It provides means to distinguish a standing person as well as her approximate location.
Variance of the signal's strength
The variance of the signal strength represents the volatility of the received signal. It can provide some estimation on changes in a receiver's proximity such as movement of individuals
Count of zero crossings
The count of zero crossings over a sample interval is a measure of the fluctuation in a received signal's strength. It can be leveraged in order to estimate the count of individuals or movement in proximity
- f a receiver.
Signal peaks within 10% of a maximum
Reflections at nearby or remote objects impact the signal strength at a receive antenna. When all peaks are of the similar magnitude, this is an indication that movement is farther away. This feature can indicate near-far relations and activity of individuals.
Mean difference between subsequent maxima
When the maximum peaks within a sample window are of similar magnitude, this indicates low activity in an environment or static activities. The opposite might be found with dynamic activities
8 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 13
Group DFAR Conclusion
Features
Assume that samples are taken on the signal strenth of an incoming signal for a window Mean signal strength
The mean signal strength over a window of measurements represents static characteristic changes in the received signal strength. It provides means to distinguish a standing person as well as her approximate location.
Variance of the signal's strength
The variance of the signal strength represents the volatility of the received signal. It can provide some estimation on changes in a receiver's proximity such as movement of individuals
Count of zero crossings
The count of zero crossings over a sample interval is a measure of the fluctuation in a received signal's strength. It can be leveraged in order to estimate the count of individuals or movement in proximity
- f a receiver.
Signal peaks within 10% of a maximum
Reflections at nearby or remote objects impact the signal strength at a receive antenna. When all peaks are of the similar magnitude, this is an indication that movement is farther away. This feature can indicate near-far relations and activity of individuals.
Mean difference between subsequent maxima
When the maximum peaks within a sample window are of similar magnitude, this indicates low activity in an environment or static activities. The opposite might be found with dynamic activities
Mean
8 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 14
Group DFAR Conclusion
Features
Assume that samples are taken on the signal strenth of an incoming signal for a window Mean signal strength
The mean signal strength over a window of measurements represents static characteristic changes in the received signal strength. It provides means to distinguish a standing person as well as her approximate location.
Variance of the signal's strength
The variance of the signal strength represents the volatility of the received signal. It can provide some estimation on changes in a receiver's proximity such as movement of individuals
Count of zero crossings
The count of zero crossings over a sample interval is a measure of the fluctuation in a received signal's strength. It can be leveraged in order to estimate the count of individuals or movement in proximity
- f a receiver.
Signal peaks within 10% of a maximum
Reflections at nearby or remote objects impact the signal strength at a receive antenna. When all peaks are of the similar magnitude, this is an indication that movement is farther away. This feature can indicate near-far relations and activity of individuals.
Mean difference between subsequent maxima
When the maximum peaks within a sample window are of similar magnitude, this indicates low activity in an environment or static activities. The opposite might be found with dynamic activities
Mean Difference between maxima
8 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 15
Group DFAR Conclusion
Features
Assume that samples are taken on the signal strenth of an incoming signal for a window Mean signal strength
The mean signal strength over a window of measurements represents static characteristic changes in the received signal strength. It provides means to distinguish a standing person as well as her approximate location.
Variance of the signal's strength
The variance of the signal strength represents the volatility of the received signal. It can provide some estimation on changes in a receiver's proximity such as movement of individuals
Count of zero crossings
The count of zero crossings over a sample interval is a measure of the fluctuation in a received signal's strength. It can be leveraged in order to estimate the count of individuals or movement in proximity
- f a receiver.
Signal peaks within 10% of a maximum
Reflections at nearby or remote objects impact the signal strength at a receive antenna. When all peaks are of the similar magnitude, this is an indication that movement is farther away. This feature can indicate near-far relations and activity of individuals.
Mean difference between subsequent maxima
When the maximum peaks within a sample window are of similar magnitude, this indicates low activity in an environment or static activities. The opposite might be found with dynamic activities
Mean Variance Difference between maxima
8 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 16
Group DFAR Conclusion
Features
Assume that samples are taken on the signal strenth of an incoming signal for a window Mean signal strength
The mean signal strength over a window of measurements represents static characteristic changes in the received signal strength. It provides means to distinguish a standing person as well as her approximate location.
Variance of the signal's strength
The variance of the signal strength represents the volatility of the received signal. It can provide some estimation on changes in a receiver's proximity such as movement of individuals
Count of zero crossings
The count of zero crossings over a sample interval is a measure of the fluctuation in a received signal's strength. It can be leveraged in order to estimate the count of individuals or movement in proximity
- f a receiver.
Signal peaks within 10% of a maximum
Reflections at nearby or remote objects impact the signal strength at a receive antenna. When all peaks are of the similar magnitude, this is an indication that movement is farther away. This feature can indicate near-far relations and activity of individuals.
Mean difference between subsequent maxima
When the maximum peaks within a sample window are of similar magnitude, this indicates low activity in an environment or static activities. The opposite might be found with dynamic activities
Mean Variance Zero crossings Difference between maxima
8 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 17
Group DFAR Conclusion
Features
Assume that samples are taken on the signal strenth of an incoming signal for a window Mean signal strength
The mean signal strength over a window of measurements represents static characteristic changes in the received signal strength. It provides means to distinguish a standing person as well as her approximate location.
Variance of the signal's strength
The variance of the signal strength represents the volatility of the received signal. It can provide some estimation on changes in a receiver's proximity such as movement of individuals
Count of zero crossings
The count of zero crossings over a sample interval is a measure of the fluctuation in a received signal's strength. It can be leveraged in order to estimate the count of individuals or movement in proximity
- f a receiver.
Signal peaks within 10% of a maximum
Reflections at nearby or remote objects impact the signal strength at a receive antenna. When all peaks are of the similar magnitude, this is an indication that movement is farther away. This feature can indicate near-far relations and activity of individuals.
Mean difference between subsequent maxima
When the maximum peaks within a sample window are of similar magnitude, this indicates low activity in an environment or static activities. The opposite might be found with dynamic activities
Mean Variance Zero crossings Signal peaks Difference between maxima
8 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 18
Group DFAR Conclusion
Recognition of multiple activities simultaneously
s t a n d i n g w a l k i n g c r a w l i n g l y i n g e m p t y 5 x 5 = 2 5
9 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 19
Group DFAR Conclusion 10 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 20
Group DFAR Conclusion
Monitoring attention from RF
11 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 21
Group DFAR Conclusion
Monitoring attention from RF
11 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 22
Group DFAR Conclusion
Situation and gestures from passive RSSI-based DFAR
10cm 10cm
Towards Away Hold over Open/close Take up Swipe bottom Swipe top Swipe left Swipe right Wipe No gesture
12 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 23
Group DFAR Conclusion
Situation and gestures from passive RSSI-based DFAR
10cm 10cm
Towards Away Hold over Open/close Take up Swipe bottom Swipe top Swipe left Swipe right Wipe No gesture
12 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 24
Group DFAR Conclusion
Measure signal strength on a phone
How to obtain this data on a phone?
root access Firmware does not support such access
13 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 25
Group DFAR Conclusion
Measure signal strength on a phone
13 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 26
Group DFAR Conclusion
Measure signal strength on a phone
13 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 27
Group DFAR Conclusion
Measure signal strength on a phone
13 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 28
Group DFAR Conclusion
Measure signal strength on a phone
raw data
grouped by sender multidimensional
data points
Sample Sender +
raw data still access- ible for visualisation
.pcap Data Point
- timespan
- feature 1
- feature 2, ...
.tab .pic kle .pdf .png
interactive
plot
video
receiver
Radio signal
Capturing Processing Post processing
tcpdump
- windowing
- feature calculation
matplotlib video analysis
- range data
mining toolkit
http://www.stephansigg.de/DeviceFree/pcapTools.tar.gz
13 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 29
Group DFAR Conclusion
Sampled RSSI over time
81.4 81.6 81.8 82 82.2 82.4 82.6 82.8 83 83.2 83.4 −95 −94 −93 −92 −91 −90 −89 −88 −87 −86 Time [seconds] RSSI [dBm]
RSSI samples over time
Only use simple time-domain features Pre-processing?
14 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 30
Group DFAR Conclusion
Which sample rate can we expect?
Suburban flat channel
P a c k e t s / s e c 1 2 3 4 5 6 7 8 9 10 11 University (ETH) channel P a c k e t s / s e c 1 2 3 4 5 6 7 8 9 10 11 34.06 0.61 4.45 0.10 53.97 0.14 0.28 0.11 26.46 0.06 0.05
ground 3 r d fl . 14.88 5.02 0.19 0.04 192.29 0.04 0.03 0.07 2.88 0.09 0.02
Dormitory channel
P a c k e t s / s e c 1 2 3 4 5 6 7 8 9 10 11 10.28 10.03 12.13 9.92 9.30 1.77 0.09 0.19 6.92 0.47 0.36 10.45 9.18 9.01 21.91 23.70 22.31 21.34 21.58 0.55 0.62 14.51 Train station channel P a c k e t s / s e c 1 2 3 4 5 6 7 8 9 10 11 0.85 0.35 0.32 0.20 0.85 3.10 2.59 11.85 4.46 2.05 9.61 15.29 8.86 11.06 1.41 2.15 10.99 4.45 1.23 11.09 10.79 23.30 Café in center channel P a c k e t s / s e c 1 2 3 4 5 6 7 8 9 10 11
15 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 31
Group DFAR Conclusion
Modelling CSI vectors via multivariate gaussian distribution
We model the amplitude of every CSI reading at location ’y’ to approximately follow a multivariate Gaussian Distribution. Location is then predicted via the maximum likelihood estimate.
16 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 32
Group DFAR Conclusion
Modelling CSI vectors via multivariate gaussian distribution
We model the amplitude of every CSI reading at location ’y’ to approximately follow a multivariate Gaussian Distribution. Location is then predicted via the maximum likelihood estimate.
Nuzzer: Seifeldin, Saeed, Kosba, El-keyi, Youssef. Nuzzer: A large-scale device-free passive localization system for wireless environments. IEEE Transactions on Mobile Computing, 2013. Pilot: Xiao, Wu, Yi, Wang, Ni. Pilot: Passive device-free indoor localization using channel state information. ICDCS, 2013. PC-DfP: Xu, Firner, Zhang, Howard, Li, Lin. Improving rf- based device-free passive localization in cluttered indoor environments through probabilistic classification
- methods. IPSN, 2013.
17 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 33
Group DFAR Conclusion
Emotion recognition from RF
18 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 34
Group DFAR Conclusion
Emotion recognition from RF
19 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 35
Group DFAR Conclusion
Emotion recognition from RF
20 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 36
Group DFAR Conclusion
Emotion recognition from RF
21 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security
SLIDE 37
Group DFAR Conclusion
Thank you!
Stephan Sigg stephan.sigg@aalto.fi
22 / 22 Stephan Sigg RF-based DFAR and implicit ad-hoc usable security