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  • Explainable semantic wireless anomaly characterization for digital twins [Elektronski vir]
    Bertalanič, Blaž ; Hanžel, Vid ; Fortuna, Carolina
    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.
    Source: Computer Networks [Elektronski vir]. - ISSN 1872-7069 (Vol. 251, [article no.] 110660, Sep. 2024, str. 1-13)
    Type of material - e-article ; adult, serious
    Publish date - 2024
    Language - english
    COBISS.SI-ID - 202453251

    Link(s):

    https://www.sciencedirect.com/science/article/pii/S1389128624004924?via%3Dihub
    DOI

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