… IoT sensors data and identify anomalous incidents in the environment. We propose an adaptive
context-aware anomaly detection … In this approach, we devise a novel sensor association …

… The present work addresses the problem of anomaly detection by combining a one-class
SVM classifier that has previously been used with success for anomaly detection with a novel …

… In the context of IoT applications, sensors are the real source of big data, which suggests
that anomaly detection at the Edge could be a powerful vehicle to address the inevitable data …

… anomaly detection algorithms such as Gaussian, binary association rule and fuzzy association
rule by using sensor … , binary association rule as well as fuzzy association rule to detect …

… A model for data uncertainty management for the entire process, starting from sensor events
to causal anomaly detection. In particular, rule weights are dynamically computed according …

… of anomaly detection techniques in WSNs are discussed in particular. Then, the state-of-the-art
techniques of anomaly detection in … /Flat) and detection technique categories (statistical …

… Then we present the anomaly detection for predictive maintenance approach in Section 4.
Experiments conducted with real and simulated data are described in Section 5 and related …

… unified modeling of pointwise representation and pairwise association, and we find that the
… association with the whole series. Our key observation is that due to the rarity of anomalies, it …

… detection, where anomalous data signal system behaviors that could result in a natural
disaster. This study develops two automated anomaly detection … on a single sensor data stream, …

… In this paper, we focus on anomaly detection for NISSs. Our objective is to develop an
anomaly detection algorithm that has the following three properties: • Real-time Detection. Since …