Anomaly Detection for Discovering Performance Degradation in Cellular IoT Services

Abstract

Connected and automated vehicles (CAVs) are envisioned to revolutionize the transportation industry, enabling autonomous processes and real-time exchange of information among vehicles and infrastructure. To safely navigate the roadways, CAVs rely on sensor readings and data from the surrounding vehicles. Hence, a fault or anomaly arising from the hardware, software, or the network can lead into devastating consequences regarding safety. This study investigates potential performance degradation caused by anomalies, by analyzing real-life vehicles' sensory and network-related data. The aim is to utilize unsupervised learning for anomaly detection, with a goal to describe the cause and effect of the detected anomalies from a performance perspective. The results show around 93% F1score when detecting anomalies imposed by the cellular network and the vehicle's sensors. Moreover, with approximately 90% F1-score we can detect anomalous predictions from a deployed network-related ML model predicting cellular throughput and describe the root-causes behind the detected anomalies.

Aging Analysis Framework of Windows-Based Systems through Differential-Analysis of System Snapshots

“…When the system is utilized for a long time, its performance degrades and the error occurrence rate tends to increase over time, which is generally called system aging [1]. System aging is observed not only in personal computer/server-based computer systems but also in emerging computing systems such as cloud computing [2], mobile [3], and IoT enhanced with 5G network [4] systems, and there has been a significant amount of research analyzing the reasons for system aging [5]. To determine the aging within the system kernel range, various related studies have been carried out within the range of the system kernel [6][7][8][9][10][11] to the user application [12], from kernel-based research on system aging based on the development of kernel tracing tools, the collection of parameters related to several kernel subsystems, and a statistical analysis of the collected data, to research hypothesizing that the reason for system aging is an element of the software design and errors in the software itself.…”