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 …

Recently, the Internet of Things (IoT) has gained a lot of attention, since IoT devices are
placed in various fields. Many of these devices are based on machine learning (ML) models …

Mobile network operators (MNOs) allocate computing and caching resources for mobile
users by deploying a central control system. Existing studies mainly use programming and …

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

Nowadays, the Internet has spread to practically every country around the world and is
having unprecedented effects on people's lives. The Internet of Things (IoT) is getting more …

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 …

Machine Learning (ML) on the edge is key to enabling a new breed of IoT and autonomous
system applications. The departure from the traditional cloud-centric architecture means that …

Vehicles are the major source of air pollution in modern cities, emitting excessive levels of
CO2 and other noxious gases. Exploiting the OBD-II interface available on most vehicles …

Abstract In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained
significant interest from both, industry and academia. Notably, conventional ML techniques …