Journal article Open Access

Learning From Errors: Detecting Cross-Technology Interference in WiFi Networks

Croce, Daniele; Garlisi, Domenico; Giuliano, Fabrizio; Inzerillo, Nicola; Tinnirello, Ilenia


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="URL">https://zenodo.org/record/3234412</identifier>
  <creators>
    <creator>
      <creatorName>Croce, Daniele</creatorName>
      <givenName>Daniele</givenName>
      <familyName>Croce</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7663-4702</nameIdentifier>
      <affiliation>DEIM, Università di Palermo</affiliation>
    </creator>
    <creator>
      <creatorName>Garlisi, Domenico</creatorName>
      <givenName>Domenico</givenName>
      <familyName>Garlisi</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6256-2752</nameIdentifier>
      <affiliation>DEIM, Università di Palermo</affiliation>
    </creator>
    <creator>
      <creatorName>Giuliano, Fabrizio</creatorName>
      <givenName>Fabrizio</givenName>
      <familyName>Giuliano</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5784-6902</nameIdentifier>
      <affiliation>DEIM, Università di Palermo</affiliation>
    </creator>
    <creator>
      <creatorName>Inzerillo, Nicola</creatorName>
      <givenName>Nicola</givenName>
      <familyName>Inzerillo</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-7253-9350</nameIdentifier>
      <affiliation>DEIM, Università di Palermo</affiliation>
    </creator>
    <creator>
      <creatorName>Tinnirello, Ilenia</creatorName>
      <givenName>Ilenia</givenName>
      <familyName>Tinnirello</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-1305-0248</nameIdentifier>
      <affiliation>DEIM, Università di Palermo</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Learning From Errors: Detecting Cross-Technology Interference in WiFi Networks</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>Wireless fidelity</subject>
    <subject>Interference</subject>
    <subject>Long Term Evolution</subject>
    <subject>ZigBee</subject>
    <subject>Monitoring</subject>
    <subject>Hidden Markov models</subject>
    <subject>Throughput</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-03-15</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3234412</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/TCCN.2018.2816068</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/688156/">688156</awardNumber>
      <awardTitle>Symbiosis of smart objects across IoT environments</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
4
5
views
downloads
Views 4
Downloads 5
Data volume 4.8 MB
Unique views 4
Unique downloads 5

Share

Cite as