With the booming of artificial intelligence (AI), Internet of Things (IoT), and high-speed
communication technology, integrating these technologies to innovate the smart grid (SG) …

With the rapid development of the Internet of Things (IoT) and communication technology,
Deep Neural Network (DNN) applications like computer vision, can now be widely used in …

Deep neural networks (DNNs) typically have a single exit point that makes predictions by
running the entire stack of neural layers. Since not all inputs require the same amount of …

Computer networks are dealing with growing complexity, given the ever-increasing volume
of data produced by all sorts of network nodes. Performance improvements are a non-stop …

The current dependency of Artificial Intelligence (AI) systems on Cloud computing implies
higher transmission latency and bandwidth consumption. Moreover, it challenges the real …

Edge devices can offload deep neural network (DNN) inference to the cloud to overcome
energy or processing constraints. Nevertheless, offloading adds communication delay …

Collaborative inference is a promising method for balancing the limited computational power
of Internet of Things (IoT) devices with the huge computational demands of convolutional …

Mobile devices can offload deep neural network (DNN)-based inference to the cloud,
overcoming local hardware and energy limitations. However, offloading adds …

The Internet of Things (IoT) is constantly growing, generating an uninterrupted data stream
pipeline to monitor physical world information. Hence, Artificial Intelligence (AI) continuously …

Edge-based and IoT devices have seen phenomenal growth in recent years, driven by the
surge in demand for emerging applications that leverage machine learning models, such as …