Abstract Machine learning has become an indispensable part of the existing technological
domain. Edge computing and Internet of Things (IoT) together presents a new opportunity to …

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 …

Abstract Recently, Smart Home Systems (SHSs) have gained enormous popularity with the
rapid development of the Internet of Things (IoT) technologies. Besides offering many …

Tiny Machine Learning (TinyML), a rapidly evolving edge computing concept that links
embedded systems (hardware and software) and machine learning, with the purpose of …

Since the inception of Industry 4.0 in 2012, emerging technologies have enabled the
acquisition of vast amounts of data from diverse sources such as machine tools, robust and …

Abstract Advanced Machine Learning (ML) algorithms can be applied using Edge
Computing (EC) to detect anomalies, which is the basis of Artificial Intelligence of Things …

Technology solutions must effectively balance economic growth, social equity, and
environmental integrity to achieve a sustainable society. Notably, although the Internet of …

TinyML has the potential to be a huge enabler of smart sensor nodes for fault diagnosis of
machines by embedding powerful machine learning algorithms in low-cost edge devices …

Recently, there has been a substantial interest in on-device Machine Learning (ML) models
to provide intelligence for the Internet of Things (IoT) applications such as image …

Abstract The Internet of Things (IoT) has grown rapidly to become the core of many areas of
application, leading to the integration of sensors, with IoT devices. However, the number of …