miun.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Real-time Interference Identification via Supervised Learning: Embedding Coexistence Awareness in IoT Devices
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. (Communication Systems and Networks)
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. (Communication Systems and Networks)ORCID iD: 0000-0003-3717-7793
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. (Communication Systems and Networks)ORCID iD: 0000-0003-0873-7827
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 835-850Article in journal (Refereed) Published
Abstract [en]

Energy sampling-based interference detection and identification (IDI) methods collide with the limitations of commercial off-the-shelf (COTS) IoT hardware. Moreover, long sensing times, complexity and inability to track concurrent interference strongly inhibit their applicability in most IoT deployments. Motivated by the increasing need for on-device IDI for wireless coexistence, we develop a lightweight and efficient method targeting interference identification already at the level of single interference bursts. Our method exploits real-time extraction of envelope and model-aided spectral features, specifically designed considering the physical properties of signals captured with COTS hardware. We adopt manifold supervised-learning (SL) classifiers ensuring suitable performance and complexity trade-off for IoT platforms with different computational capabilities. The proposed IDI method is capable of real-time identification of IEEE 802.11b/g/n, 802.15.4, 802.15.1 and Bluetooth Low Energy wireless standards, enabling isolation and extraction of standard-specific traffic statistics even in the case of heavy concurrent interference. We perform an experimental study in real environments with heterogeneous interference scenarios, showing 90%–97% burst identification accuracy. Meanwhile, the lightweight SL methods, running online on wireless sensor networks-COTS hardware, ensure sub-ms identification time and limited performance gap from machine-learning approaches.

Place, publisher, year, edition, pages
2019. Vol. 7, p. 835-850
Keywords [en]
Bluetooth; interference detection and identification, IoT, machine learning, wireless coexistence, wireless sensor networks, WLAN
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:miun:diva-35184DOI: 10.1109/ACCESS.2018.2885893ISI: 000455177700001OAI: oai:DiVA.org:miun-35184DiVA, id: diva2:1270303
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)Available from: 2018-12-12 Created: 2018-12-12 Last updated: 2019-09-09Bibliographically approved

Open Access in DiVA

fulltext(10650 kB)196 downloads
File information
File name FULLTEXT01.pdfFile size 10650 kBChecksum SHA-512
ca252d229813d8d19a9f5db8fc4096bc478a343ad2ec1f498f61820d24414f8c3c0b9e833a950dc4bdc80705c0979a61707fd35356600272c807aaa549f538ff
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records BETA

Grimaldi, SimoneMahmood, AamirGidlund, Mikael

Search in DiVA

By author/editor
Grimaldi, SimoneMahmood, AamirGidlund, Mikael
By organisation
Department of Information Systems and Technology
In the same journal
IEEE Access
Communication Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 196 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 385 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf