In recent years, the landscape of computing paradigms has witnessed a gradual yet
remarkable shift from monolithic computing to distributed and decentralized paradigms such …

In recent years, deep learning (DL) models have demonstrated remarkable achievements
on non-trivial tasks such as speech recognition, image processing, and natural language …

Recently, intelligent scheduling approaches using surrogate models have been proposed to
efficiently allocate volatile tasks in heterogeneous fog environments. Advances like …

In recent years, deep learning models have become ubiquitous in industry and academia
alike. Deep neural networks can solve some of the most complex pattern-recognition …

Since emerging edge applications such as Internet of Things (IoT) analytics and augmented
reality have tight latency constraints, hardware AI accelerators have been recently proposed …

The ubiquity of smartphone cameras and IoT cameras, together with the recent boom of
deep learning and deep neural networks, proliferate various computer vision driven mobile …

The wide adoption of smart devices and Internet-of-Things (IoT) sensors has led to massive
growth in data generation at the edge of the Internet over the past decade. Intelligent real …

The rapid emergence of deep learning (DL) algorithms has paved the way for bringing
artificial intelligence (AI) services to end users. The intersection between edge computing …

Many real-world applications are widely adopting the edge computing paradigm due to its
low latency and better privacy protection. With notable success in AI and deep learning (DL) …

Modern IoT environments increasingly involve intensive data processing, using advanced
algorithms for artificial intelligence, locally on the nodes themselves. That is, for various …