10.1109/TCCN.2018.2816068
https://zenodo.org/records/3234412
oai:zenodo.org:3234412
Croce, Daniele
Daniele
Croce
0000-0001-7663-4702
DEIM, Università di Palermo
Garlisi, Domenico
Domenico
Garlisi
0000-0001-6256-2752
DEIM, Università di Palermo
Giuliano, Fabrizio
Fabrizio
Giuliano
0000-0001-5784-6902
DEIM, Università di Palermo
Inzerillo, Nicola
Nicola
Inzerillo
0000-0002-7253-9350
DEIM, Università di Palermo
Tinnirello, Ilenia
Ilenia
Tinnirello
0000-0002-1305-0248
DEIM, Università di Palermo
Learning From Errors: Detecting Cross-Technology Interference in WiFi Networks
Zenodo
2018
Wireless fidelity
Interference
Long Term Evolution
ZigBee
Monitoring
Hidden Markov models
Throughput
2018-03-15
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
In this paper, we show that inter-technology interference can be recognized using commodity WiFi devices by monitoring the statistics of receiver errors. Indeed, while for WiFi standard frames the error probability varies during the frame reception in different frame fields (PHY, MAC headers, and payloads) protected with heterogeneous coding, errors may appear randomly at any point during the time the demodulator is trying to receive an exogenous interfering signal. We thus detect and identify cross-technology interference on off-the-shelf WiFi cards by monitoring the sequence of receiver errors (bad PLCP, bad FCS, invalid headers, etc.) and propose two methods to recognize the source of interference based on artificial neural networks and hidden Markov chains. The result is quite impressive, reaching an average accuracy of over 95% in recognizing ZigBee, microwave, and LTE (in unlicensed spectrum) interference.
European Commission
10.13039/501100000780
688156
Symbiosis of smart objects across IoT environments