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 Today, Mobile Cloud Computing (MCC) alone can no longer respond to the
increasing volume of data and satisfy the necessary delays in real-time applications. In …

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 …

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

With the advent of Tiny Machine Learning (tinyML), it is increasingly feasible to deploy
optimized ML models on constrained battery-less Internet of Things (IoT) devices with …

The implementation of blockchain technology in integrated IoT networks for constructing
scalable Intelligent Transportation Systems (ITS) in India has the potential to revolutionize …

The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of
Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware …

This article comprehensively reviews the emerging concept of Internet of Intelligent Things
(IoIT), adopting an integrated perspective centred on the areas of embedded systems, edge …

Driver drowsiness is one of the main causes of traffic accidents today. In recent years, driver
drowsiness detection has suffered from issues integrating deep learning (DL) with Internet-of …

Green AI (Artificial Intelligence) and digitalization facilitate the “Dual-Carbon” goal of low-
carbon, high-quality economic development. Green AI is moving from “cloud” to “edge” …