The advent of the 6G era aims for ubiquitous connectivity, with the integration of non-
terrestrial networks (NTN) offering extensive coverage and enhanced capacity. As …

The advancement of technology towards the realization of the evolving mobile computing
paradigm brings a rapid paradigm shift in its usage, especially in the Internet, computation …

With the rapid development of Internet-of-Things (IoT) and the explosive advance of deep
learning, there is an urgent need to enable deep learning inference on IoT devices in Mobile …

Reducing the inference time of Deep Neural Networks (DNNs) is critical when running time
sensitive applications on mobile devices. Existing research has shown that partitioning a …

The evolution of the Internet of Things (IoT) has been driving the explosive growth of deep
neural network (DNN)-based applications and processing demands. Hence, edge …

Deep Neural Networks (DNNs) have been widely deployed in mobile applications. DNN
inference latency is a critical metric to measure the service quality of those applications …

This work analyses the most relevant research conducted under the mobile cloud computing
paradigm to bring vision tasks supported by state-of-the-art deep convolutional neural …

Abstract Multi-access Edge Computing (MEC) is an enabling technology to leverage new
network applications, such as virtual/augmented reality, by providing faster task processing …

As applications grow in scale, the centralized computing approach leads to excessive
bandwidth requirements and high computational latencies. The traditional computing model …

With the development of Internet of Things (IoT) and the advance of deep learning, there is
an urgent need to enable deep learning inference on IoT devices. To address the …