Explainable semantic wireless anomaly characterization for digital twins

Bertalanič, Blaž (author), Hanžel, Vid (author), Fortuna, Carolina (author)

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
• UDC: 004
• ISSN on article: 1872-7069
• DOI: 10.1016/j.comnet.2024.110660
• COBISS.SI-ID: 202453251
• Publication date in RUL: 26.07.2024

Record is a part of a journal

• Title: Computer networks
• Publisher: Elsevier
• ISSN: 1872-7069
• COBISS.SI-ID: 23281413

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