Machine Vision Based Wireless Link Layer Anomaly Characterization
Valerij Jovanov, Blaž Bertalanič, Carolina Fortuna · 2023
As the number of wireless end and edge devices increases, so does the volume of data to be monitored in view of predicting or detecting malfunctions. Furthermore, as the networks become more complex, the more context can be provided around a certain anomaly, fault or malfunction, the easier will be to establish mitigation actions in a fast and efficient manner. While detecting and classifying shapes of anomalous link behaviour from time series has already been investigated, the precise localization in time and characterization in duration and amplitude from actual data traces that would provide more context related to the anomaly is outstanding.