Collaborative edge computing (CEC) is a recently popular paradigm enabling sharing of
data and computation resources among different edge devices. Task offloading is an …

Emerging edge applications require both a fast response latency and complex processing.
This is infeasible with-out expensive hardware that can process complex operations-such as …

Clustering aims to group unlabeled objects based on similarity inherent among them into
clusters. It is important for many tasks such as anomaly detection, database sharding, record …

Despite the demand for realtime deep learning applications such as video analytics at the
edge, resource-constrained edge devices can largely not process video streams at their …

Collaborative inference enables resource-constrained edge devices to make inferences by
uploading inputs (eg, images) to a server (ie, cloud) where the heavy deep learning models …

In industrial scenarios, it is very common to replace manual detection with new intelligent
applications such as image recognition based on deep learning. However, these …

Novel smart environments, such as smart home, smart city, and intelligent transportation, are
driving increasing interest in deploying deep neural networks (DNN) in edge devices …

Deep Learning models have achieved tremendous success lately towards analysing high-
dimensional data like images, texts, audio, etc. Despite their phenomenal predictive …

With billions of Internet of Things (IoT) devices, such as sensors, security cameras, and dash
cams, generating huge amounts of data and transferring it to the cloud, it creates a network …

According to the perspective of machine learning, the system discovered via the process of
applying unsupervised learning to look for clusters is a data idea. As a result, clustering may …