Abstract Edge Artificial Intelligence (AI) incorporates a network of interconnected systems
and devices that receive, cache, process, and analyse data in close communication with the …

In order to describe the roadmap of current edge computing research activities, we first
address a brief overview of the most advanced edge computing surveys published in the last …

The rapid adoption of large language models (LLMs) has led to significant advances in
natural language processing and text generation. However, the energy consumed through …

Edge AI seeks for the deployment of deep neural network (DNN) based services across
distributed edge devices, embedding intelligence close to data sources. Due to capacity …

The advent of the computing continuum, ie, the blending of all existing computational tiers,
calls for novel techniques and methods that consider its complex dynamics. This work …

The recent migration towards Internet of Things determined the rise of a Computing
Continuum paradigm where Edge and Cloud resources coordinate to support the execution …

It is commonly assumed that the end-to-end networking performance of edge offloading is
purely dictated by that of the network connectivity between end devices and edge computing …

Hardware accelerators such as GPUs are required for real-time, low latency inference with
Deep Neural Networks (DNN). Providing inference services in the cloud can be resource …

Smart resource allocation is essential for optimising cloud computing efficiency and
utilisation, but it is also very challenging as traditional approaches often overprovision CPU …

Mobile Augmented Reality (MAR) has gained increased attention thanks to its potential to
transform applications in different domains. One of the challenges to realizing MAR systems …