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An SVM-Based Method for Classification of External Interference in Industrial Wireless Sensor and Actuator Networks
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. (Communication Systems and Networks (CSN))
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. (Communication Systems and Networks (CSN))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 (CSN))ORCID iD: 0000-0003-0873-7827
2017 (English)In: Journal of Sensor and Actuator Network, ISSN 2224-2708, Vol. 6, no 2, 9Article in journal (Refereed) Published
Abstract [en]

In recent years, the adoption of industrial wireless sensor and actuator networks (IWSANs) has greatly increased. However, the time-critical performance of IWSANs is considerably affected by external sources of interference. In particular, when an IEEE 802.11 network is coexisting in the same environment, a significant drop in communication reliability is observed. This, in turn, represents one of the main challenges for a wide-scale adoption of IWSAN. Interference classification through spectrum sensing is a possible step towards interference mitigation, but the long sampling window required by many of the approaches in the literature undermines their run-time applicability in time-slotted channel hopping (TSCH)-based IWSAN. Aiming at minimizing both the sensing time and the memory footprint of the collected samples, a centralized interference classifier based on support vector machines (SVMs) is introduced in this article. The proposed mechanism, tested with sample traces collected in industrial scenarios, enables the classification of interference from IEEE 802.11 networks and microwave ovens, while ensuring high classification accuracy with a sensing duration below 300 ms. In addition, the obtained results show that the fast classification together with a contained sampling frequency ensure the suitability of the method for TSCH-based IWSAN

Place, publisher, year, edition, pages
2017. Vol. 6, no 2, 9
Keyword [en]
Industrial wireless sensor and actuator networks; support vector machine; interference classification; spectrum sensing; Wireless LAN; microwave owen
National Category
Computer Engineering Communication Systems Telecommunications
Identifiers
URN: urn:nbn:se:miun:diva-30891DOI: 10.3390/jsan6020009ISI: 000404529000005OAI: oai:DiVA.org:miun-30891DiVA: diva2:1110894
Projects
ASISTIMELINESS
Funder
Knowledge Foundation
Available from: 2017-06-16 Created: 2017-06-16 Last updated: 2017-08-08Bibliographically approved

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Grimaldi, SimoneMahmood, AamirGidlund, Mikael
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CiteExportLink to record
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Citation style
  • apa
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