Deep learning is currently widely used in a variety of applications, including computer vision
and natural language processing. End devices, such as smartphones and Internet-of-Things …

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 …

For multi-task mobile edge computing (MEC) systems in satellite Internet of Things (IoT),
there are dependencies between different tasks, which need to be collected and jointly …

Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …

Multi-access edge computing (MEC) aims to extend cloud service to the network edge to
reduce network traffic and service latency. A fundamental problem in MEC is how to …

The Internet is evolving rapidly toward the future Internet of Things (IoT) which will potentially
connect billions or even trillions of edge devices which could generate huge amount of data …

The computation for today's intelligent personal assistants such as Apple Siri, Google Now,
and Microsoft Cortana, is performed in the cloud. This cloud-only approach requires …

In this paper, we propose an optimization framework of offloading from a single mobile
device (MD) to multiple edge devices. We aim to minimize both total tasks' execution latency …

Deep learning shows great promise in providing more intelligence to augmented reality (AR)
devices, but few AR apps use deep learning due to lack of infrastructure support. Deep …

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