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In the modern era of digital transformation, the evolution of the fifth-generation (5G) wireless network has played a pivotal role in revolutionizing communication technology and accelerating the growth of smart technology applications.…
As the number of Internet of Things (IoT) devices and systems have surged, IoT data analytics techniques have been developed to detect malicious cyber-attacks and secure IoT systems; however, concept drift issues often occur in IoT data…
In recent years, with the increasing popularity of "Smart Technology", the number of Internet of Things (IoT) devices and systems have surged significantly. Various IoT services and functionalities are based on the analytics of IoT…
To ensure reliability and service availability, next-generation networks are expected to rely on automated anomaly detection systems powered by advanced machine learning methods with the capability of handling multi-dimensional data. Such…
Industrial IoT ecosystems bring together sensors, machines and smart devices operating collaboratively across industrial environments. These systems generate large volumes of heterogeneous, high-velocity data streams that require…
Within data-driven artificial intelligence (AI) systems for industrial applications, ensuring the reliability of the incoming data streams is an integral part of trustworthy decision-making. An approach to assess data validity is data…
With the wide spread of sensors and smart devices in recent years, the data generation speed of the Internet of Things (IoT) systems has increased dramatically. In IoT systems, massive volumes of data must be processed, transformed, and…
In recent years, with the rapid development of sensing technology and the Internet of Things (IoT), sensors play increasingly important roles in traffic control, medical monitoring, industrial production and etc. They generated high volume…
Modern streaming data categorization faces significant challenges from concept drift and class imbalanced data. This negatively impacts the output of the classifier, leading to improper classification. Furthermore, other factors such as the…
Increasingly, Internet of Things (IoT) domains, such as sensor networks, smart cities, and social networks, generate vast amounts of data. Such data are not only unbounded and rapidly evolving. Rather, the content thereof dynamically…
In the Industrial Internet of Things (IIoT), condition monitoring sensor signals from complex systems often exhibit nonlinear and stochastic spatial-temporal dynamics under varying conditions. These complex dynamics make fault detection…
Internet of Things (IoT) Analytics often involves applying machine learning (ML) models on data streams. In such scenarios, traditional ML paradigms face obstacles related to continuous learning while dealing with concept drifts, temporal…
Automated machine learning techniques benefited from tremendous research progress in recently. These developments and the continuous-growing demand for machine learning experts led to the development of numerous AutoML tools. However, these…
A massive number of devices are expected to fulfill the missions of sensing, processing and control in cyber-physical Internet-of-Things (IoT) systems with new applications and connectivity requirements. In this context, scarce spectrum…
Number of IoT devices is constantly increasing which results in greater complexity of computations and high data velocity. One of the approach to process sensor data is dataflow programming. It enables the development of reactive software…
With the popularity of Internet of Things (IoT), edge computing and cloud computing, more and more stream analytics applications are being developed including real-time trend prediction and object detection on top of IoT sensing data. One…
Recent transfer learning (TL) approaches in industrial intelligent fault diagnosis (FD) mostly follow the "pre-train and fine-tuning" paradigm to address data drift, which emerges from variable working conditions. However, we find that this…
Automated Machine Learning (AutoML) systems have been shown to efficiently build good models for new datasets. However, it is often not clear how well they can adapt when the data evolves over time. The main goal of this study is to…
Industrial organisations, particularly Small and Medium-sized Enterprises (SME), face a number of challenges with regard to the adoption of Industrial Internet of Things (IIoT) technologies and methods. The scope of analytics processing…
Ever-increasing amounts of data and requirements to process them in real time lead to more and more analytics platforms and software systems being designed according to the concept of stream processing. A common area of application is the…