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 systems aid scientific studies by instrumenting the real world and collecting
measurements. Given the large volume of measurements collected by sensor systems, one …

With the growth of IoT applications, sensor data quality has become increasingly important
to ensure the success of these data-driven applications. Sensor data riddled with errors are …

Wireless Sensor Networks (WSNs) are important and necessary platforms for the future as
the concept “Internet of Things” has emerged lately. They are used for monitoring, tracking …

Machine learning (ML) and deep learning (DL), in particular, are common tools for anomaly
detection (AD). With the rapid increase in the number of Internet-connected devices, the …

The ability to detect and process anomalies for Big Data in real-time is a difficult task. The
volume and velocity of the data within many systems makes it difficult for typical algorithms to …

It is an era of Internet of Things, where various types of sensors, especially wireless, are
widely used to collect huge amount of data to feed various systems such as surveillance …

The increased use of Industrial Wireless Sensor Networks (IWSN) in a variety of different
applications, including those that involve critical infrastructure, has meant that adequately …

Heterogeneous wireless sensor networks are a source of large amount of different
information representing environmental aspects such as light, temperature, and humidity. A …

The data produced by millions of connected devices and smart sensors in the Industrial
Internet of Things (IIoT) is highly dynamic, large-scale, heterogeneous, and time-stamped …