Due to Industry 4.0, machines can be connected to their manufacturing processes with the
ability to react faster and smarter to changing conditions in a factory. Previously, Internet of …

In recent years, ML (Machine Learning) models that have been trained in data centers can
often be deployed for use on edge devices. When the model deployed on these devices …

Edge Computing is becoming more and more essential for the Industrial Internet of Things
(IIoT) for data acquisition from shop floors. The shifting from central (cloud) to distributed …

Artificial Intelligence of Things (AIoT) is the combination of artificial intelligence (AI)
technologies with the Internet of Things (IoT) infrastructure to achieve more efficient IoT …

Industrial applications, such as real-time manufacturing, fault classification and inference,
autonomous cars, etc., are data-driven applications that require machine learning with a …

Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of
interconnected devices, allowing the use of various smart applications. The enormous …

AI, machine learning, and deep learning tools have now become easily accessible on the
cloud. However, the adoption of these cloud-based services for heavy industries has been …

In recent years we have witnessed a boom in Internet of Things (IoT) device deployments,
which has resulted in big data and demand for low-latency communication. This shift in the …

Recently, we have witnessed the evolution of Edge Computing (EC) and Deep Learning
(DL) serving Industrial Internet of Things (IIoT) applications, in which executing DL models is …

Machine learning techniques have been widely adopted to assist in data analysis in a
variety of Internet of Things (IoT) systems. To enable flexible use of trained learning models …