With the popularity of mobile devices, intelligent applications, eg, face recognition, intelligent
voice assistant, and gesture recognition, have been widely used in our daily lives. However …

As intelligence is moving from data centers to the edges, intelligent edge devices such as
smartphones, drones, robots, and smart IoT devices are equipped with the capability to …

New applications such as intelligent manufacturing, autonomous vehicles and smart cities
drive large-scale deep learning models deployed in the Internet of Things (IoT) edge …

Efforts to leverage the benefits of Deep Learning (DL) models for performing inference in
resource constrained embedded devices is very popular nowadays. Researchers worldwide …

Deep neural network (DNN) inference task offloading is an essential problem of edge
intelligence, which faces the challenges of limited computing resources shortage of edge …

Deep reinforcement learning (DRL) is a promising approach for developing control policies
by learning how to perform tasks. Edge devices are required to control their actions by …

The increasing pervasiveness of intelligent mobile applications requires to exploit the full
range of resources offered by the mobile-edge-cloud network for the execution of inference …

Delay-sensitive task offloading in a device-to-device assisted mobile edge computing (D2D-
MEC) system with energy harvesting devices is a critical challenge due to the dynamic load …

Recent years have witnessed the increasing popularity of mobile applications, eg, virtual
reality, unmanned driving, which are generally computation-intensive and latency-sensitive …

The computation and storage requirements of Deep Neural Networks (DNNs) make them
challenging to deploy on edge devices, which often have limited resources. Conversely …