In response to users' demand for privacy, trust and control over their data, executing
machine learning tasks at the edge of the system has the potential to make the Internet of …

As the number of interconnected devices increases and more artificial intelligence (AI)
applications upon the Internet of Things (IoT) start to flourish, so does the environmental cost …

The massive number of Internet-of-Things (IoT) devices generates a hard-to-manage
volume of data. Cloud-centric processing approaches for the IoT data suffer from high and …

The massive number of sensors deployed in the Internet of Things (IoT) produce gigantic
amounts of data for facilitating a wide range of applications. Deep Learning (DL) would …

Recently, Deep Learning (DL) techniques have shown their effectiveness for Human Activity
Recognition (HAR) tasks. However, due to the storage and computational requirements …

Abstract The Internet of Things (IoT), cloud computing, and machine learning opened an
opportunity for new smart systems. These technologies have triggered huge traffic and delay …

Human activity recognition is commonly used in several Internet of Things applications to
recognize different contexts and respond to them. Deep learning has gained momentum for …

Human Activity Recognition (HAR) is used in several human-centric real-world situations. In
many learning-based systems, sensors capture situational data and transfer it to adjacent …

With the recent advances in the Internet of Things (IoT) technologies, various human-
centered applications have proliferated and improved the quality of users' life. In the …

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 …