Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

Enormous amounts of data are being produced everyday by sub-meters and smart sensors
installed in residential buildings. If leveraged properly, that data could assist end-users …

Anomaly detection is concerned with identifying data patterns that deviate remarkably from
the expected behavior. This is an important research problem, due to its broad set of …

Wireless sensor network (WSN) is one of the most promising technologies for some real-
time applications because of its size, cost-effective and easily deployable nature. Due to …

Detecting anomalies in time series data is becoming mainstream in a wide variety of
industrial applications in which sensors monitor expensive machinery. The complexity of this …

The rapid spread of the Internet of Things (IoT) devices has prompted many people and
companies to adopt the IoT paradigm, as this paradigm allows the automation of several …

Intrusion detection systems (IDSs) are intrinsically linked to a comprehensive solution of
cyberattacks prevention instruments. To achieve a higher detection rate, the ability to design …

Sensor data quality plays a vital role in Internet of Things (IoT) applications as they are
rendered useless if the data quality is bad. This systematic review aims to provide an …

In most cases, wireless sensor networks (WSNs) are deployed in unattended scenarios and
are featured by energy sensitivity and low cost, thus making the performance of WSNs prone …

Unsupervised anomaly detection is an important research direction in the process of
unsupervised knowledge acquisition. It has been successfully applied in many fields, such …