The rapid emergence of low-power embedded devices and modern machine learning (ML)
algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks …

The rapid growth in miniaturization of low-power embedded devices and advancement in
the optimization of machine learning (ML) algorithms have opened up a new prospect of the …

Tiny machine learning (TinyML) is a fast-growing research area committed to democratizing
deep learning for all-pervasive microcontrollers (MCUs). Challenged by the constraints on …

Focusing on comprehensive networking, the Industrial Internet-of-Things (IIoT) facilitates
efficiency and robustness in factory operations. Various intelligent sensors play a central …

On-device artificial intelligence has attracted attention globally, and attempts to combine the
internet of things and TinyML (machine learning) applications are increasing. Although most …

In the context of the application of artificial intelligence in an intellectual property trading
platform, the number of demanders and suppliers that exchange scarce resources is …

The Internet of Moving Things (IoMT) takes a step further with respect to traditional static IoT
deployments. In this line, the integration of new eco-friendly mobility devices such as …

Deep learning (DL) techniques have been extensively studied to improve their precision and
scalability in a vast range of applications. Recently, a new milestone has been reached …

TinyML technology, situated at the intersection of Machine Learning, Embedded Systems,
and the Internet of Things (IoT), presents a promising solution for a wide range of IoT …

Embedded systems are becoming interconnected and collaborative systems able to perform
autonomous tasks. The remarkable expansion of the embedded and IoT market, together …