AIoT and edge computing of sensor dataŠalej, Jakob (Avtor) Trebar, Mira (Mentor) internet of thingsmachine learningsensor dataedge computinganomaly detectionThe Internet of Things (IoT) consists of resource-constrained devices or sensors connected to the network. These devices send large amounts of data to the servers in the cloud, to be stored and processed. Relying on the cloud has its disadvantages, namely high network usage, privacy concerns and slower data processing. These downsides can be mitigated with a new paradigm - edge computing. The new category of smart AIoT (Artificial Intelligence + IoT) devices is capable of learning from and responding to new scenarios instantly by using ML methods locally, on-device. The goal of this master's thesis is to analyse the possibilities of local data processing on resource-constrained edge devices. An experimental analysis of classification and runtime performance of ML algorithms is provided. Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Artificial Neural Network (ANN) are evaluated on a dataset DS2OS traffic traces in an anomaly detection domain. Additionally, Stochastic Gradient Descent (SGD) is used for incremental learning. Algorithm performance is measured on two devices: Raspberry Pi 4 model B serves as a reference edge device, while a laptop PC XPS 13 provides a performance baseline. Mode Ds and mode D implementations differ in how data is split into training and test sets. Two methods for reducing dataset size are presented: randomly sampled smaller datasets and datasets with reduced majority class. Classification results on initial training sets and reduced training sets show that SVM, DT and RF perform the best. Performance analysis shows that DT achieves fastest training and inference times on RPi 4. By using datasets with reduced majority class, RPi 4 is able to match XPS 13 runtime performance results with only a small decrease in classification accuracy.20212021-11-23 11:40:00Magistrsko delo/naloga133344VisID: 29882COBISS_ID: 87405571sl