Wireless Technology Recognition Based on RSSI Distribution at SubNyquist Sampling Rate for Constrained Devices
Abstract
:1. Introduction
2. Related Work and Contributions
2.1. TechnologySpecific Detection Solutions
2.2. Existing Studies of the Distribution of RSSI
2.3. Existing Application of RSSI in Technology Recognition
2.4. Contributions
3. MultiModal Distribution of RSSI
3.1. Discontinuous Transmission
3.2. Variation in Carrier Allocation
Experiment
3.3. Summary
4. Characterization of RealLife Signals
4.1. Signal Selection
 WiFi (IEEE 802.11a): signal transmitted in random bursts, modulated with a constant amount of carriers;
 LTE: signal transmitted in very fine and regular intervals, modulated with a variable amount of carriers;
 DVBT: signal transmitted continuously and modulated with a constant amount of carriers.
4.2. Experiments
4.2.1. Experiment with the Spectrum Analyzer
4.2.2. Experiment with SmallScale RF Devices
4.3. Feature Space Design
4.3.1. Features from the RSSI Distribution
 ${\sigma}_{\overrightarrow{R}}$: the standard deviation of the RSSI vector $\overrightarrow{R}$. It indicates the range of variation in the signal strength.
 ${N}_{p}$: the number of peaks in the histogram of RSSI is a simple way to describe the shape of the distribution. A point in the histogram is recognized as a peak when it is above its two neighboring points.
 $\overline{n}$: the average power level of the noise, which corresponds to the location of the leftmost peak in the histogram, and it should be situated to the left of a certain threshold, denoted as ${\theta}_{\overline{n}}$.
 $p(\overline{n})$: the probability that the measured RSSI is equal to the average noise power level $\overline{n}$, i.e., $p(\overline{n})=P(RSSI=\overline{n})$. This is the amplitude of the noise peak in the RSSI histogram, which is proportional to the amount of time the signal is interrupted. It is identified when the peak corresponding to $\overline{n}$ is above ${\theta}_{p}$.
4.3.2. Features from RSSI Time Series
5. Automatic Signal Recognition
5.1. Sample Algorithm
 ${\theta}_{\overline{n}}$ is the upper bound of the average noise level, obtained by the maximum of $\overline{n}$ in the collected WiFi traces plus the standard deviation of $\overline{n}$.
 ${\theta}_{p}$ determines the minimal amount of noise present in the WiFi’s RSSI measurements. It is calculated by the smallest ‘noise peak’ minus the standard deviation of the noise peaks in the WiFi’s RSSI measurements.
 $min(Wi$$Fi.{\sigma}_{\overrightarrow{R}})$ denotes the minimum standard deviation among the collected RSSI measurements of WiFi, which is used to differentiate WiFi from noise.
 $(min(LTE.{\sigma}_{\overrightarrow{R}})+max(DVB$$T.{\sigma}_{\overrightarrow{R}}))/2$ denotes the medium of the minimum and the maximum standard deviation of LTE and DVBT’s RSSI measurements. It is used to differentiate LTE and DVBT signal.
 $max(DVB$$T.{N}_{p})$ denotes the maximum number of peaks in the histograms of the RSSI measurements of DVBT. It is used to exclude unknown signals from the DVBT signals.
Algorithm 1 RSSI distributionbased technology recognition. 


5.2. Validation
5.2.1. Analysis of N = 20, T = 1 s
5.2.2. Analysis of Variable N
5.2.3. Analysis of Variable T
5.2.4. Analysis of Practicality
5.3. Extended Validation for Mixed Signals
5.3.1. Dataset Extension for Mixed LTEU and WiFi Signals
5.3.2. Algorithm and Feature Space Extension
 ${\sigma}_{\overrightarrow{z}}$: the standard deviation of the locations (indicated by grey flashes in Figure 8 and represented by vector $\overrightarrow{z}$), where the histogram elements located to the right side of ${\theta}_{\overline{n}}$ are equal to zero. It indicates the amount of discontinuities in the RSSI histogram.
 ${p}_{max}$: the amplitude of the highest peak in the histogram apart from the noise peak.
Algorithm 2 Extension to distinguish WiFi from MIX_LTE_WiFi. 

5.3.3. Result Analysis
6. Conclusions and Future Work
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Tech  WiFi  LTE  DVBT  

Features  
${\sigma}_{\overrightarrow{R}}$  Variable  High  Low  
${N}_{p}$  Variable  High  Low  
$p(\overline{n})$  High  Not visible  Not present 
Prediction  WiFi  LTE  DVBT  Noise  Unknown  

Actual  
WiFi  92.6%  1.85 %  0%  3.70%  1.85%  
LTE  0%  100%  0%  0%  0%  
DVBT  0%  1.85%  98.15%  0%  0%  
Noise  0%  0%  0%  0%  0%  
Unknown  0%  0%  0%  0%  0% 
Predicted  WiFi  LTE  DVBT  Mixed LTE WiFi  

Actual  
WiFi  94.4%  0 %  0%  5.60%  
LTE  0%  100%  0%  0%  
DVBT  0%  1.85%  98.15%  0%  
Mixed LTE WiFi  0%  1.85%  0%  98.15% 
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Liu, W.; Kulin, M.; Kazaz, T.; Shahid, A.; Moerman, I.; De Poorter, E. Wireless Technology Recognition Based on RSSI Distribution at SubNyquist Sampling Rate for Constrained Devices. Sensors 2017, 17, 2081. https://doi.org/10.3390/s17092081
Liu W, Kulin M, Kazaz T, Shahid A, Moerman I, De Poorter E. Wireless Technology Recognition Based on RSSI Distribution at SubNyquist Sampling Rate for Constrained Devices. Sensors. 2017; 17(9):2081. https://doi.org/10.3390/s17092081
Chicago/Turabian StyleLiu, Wei, Merima Kulin, Tarik Kazaz, Adnan Shahid, Ingrid Moerman, and Eli De Poorter. 2017. "Wireless Technology Recognition Based on RSSI Distribution at SubNyquist Sampling Rate for Constrained Devices" Sensors 17, no. 9: 2081. https://doi.org/10.3390/s17092081