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Explainable semantic wireless anomaly characterization for digital twins
ID Bertalanič, Blaž (Author), ID Hanžel, Vid (Author), ID Fortuna, Carolina (Author)

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Abstract
The shift toward software-centric network infrastructures is driven by the increasing need for networks to be responsive, flexible, and scalable in the face of an expanding set of connected devices. The digital twin (DT) approach, mirroring physical entities in a digital format, has emerged as a key enabler of network reliability and availability. Incorporating artificial intelligence (AI) into DTs enhances the resilience of networks by providing in-depth analysis and increasingly automated mitigation strategies against operational disruptions. In this work, we propose a new AI-based information extraction module that is part of the DT Monitoring component able to processes RSS data, extract and characterize abrupt anomalies. The output of this component is used to maintain an anomaly history in the Link Abstraction within the DT and subsequently inform possible automatic mitigation actions. We design the AI-based information extraction module to identify and characterize three types of RSS based anomalies. Our extensive performance analysis on 10 versions of the ”You Only Look Once” architecture reveals that YOLOv8n produces a good tradeoff between performance and computational complexity. We show that our approach performs on par with the state of the art for anomaly detection, while enabling anomaly characterization by location, duration, and severity. By employing two SotA explainability algorithms, we also provide insights into the important regions of the input that trigger the selected model’s classification and characterization decisions.

Language:English
Keywords:wireless network, anomaly characterization, digital twin, explainable model, monitoring, maintenance, wireless anomaly detection
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FE - Faculty of Electrical Engineering
Publication status:Published
Publication version:Version of Record
Year:2024
Number of pages:13 str.
Numbering:Vol. 251, art. 110660
PID:20.500.12556/RUL-159823 This link opens in a new window
UDC:004
ISSN on article:1872-7069
DOI:10.1016/j.comnet.2024.110660 This link opens in a new window
COBISS.SI-ID:202453251 This link opens in a new window
Publication date in RUL:26.07.2024
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Downloads:71
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Record is a part of a journal

Title:Computer networks
Publisher:Elsevier
ISSN:1872-7069
COBISS.SI-ID:23281413 This link opens in a new window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Projects

Funder:ARRS - Slovenian Research Agency
Project number:P2-0016
Name:Komunikacijska omrežja in storitve

Funder:EC - European Commission
Funding programme:HE
Project number:101096456
Name:An Artificial Intelligent Aided Unified Network for Secure Beyond 5G Long Term Evolution
Acronym:NANCY

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