We are living in a world where massive end devices perform computing everywhere and
everyday. However, these devices are constrained by the battery and computational …

In recent years unmanned aerial vehicles (UAVs) have emerged as a popular and cost-
effective technology to capture high spatial and temporal resolution remote sensing (RS) …

Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that
enables in-situ model training and testing on edge data. Despite having the same end goals …

With the breakthroughs in deep learning, the recent years have witnessed a booming of
artificial intelligence (AI) applications and services, spanning from personal assistant to …

To comply with the increasing number of government regulations about data placement and
processing, and to protect themselves against major cloud outages, many users want the …

Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to
enable computing services devices deployed at network edge, aiming to improve the user's …

Video analytics will drive a wide range of applications with great potential to impact society.
A geographically distributed architecture of public clouds and edges that extend down to the …

Currently, there are numerous drawbacks associated with infrastructure health monitoring
and management, such as inefficiency, subpar real-time functionality, demanding data …

Machine learning (ML) is widely used to derive useful information from large-scale data
(such as user activities, pictures, and videos) generated at increasingly rapid rates, all over …

Organizations deploy a hierarchy of clusters-cameras, private clusters, public clouds-for
analyzing live video feeds from their cameras. Video analytics queries have many …