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 …

Rapid growth in numbers of connected devices including sensors, mobile, wearable, and
other Internet of Things (IoT) devices, is creating an explosion of data that are moving across …

Edge-assisted video analytics is gaining momentum. In this work, we tackle an important
problem to compress video content live streamed from the device to the edge without …

Edge computing is a decentralized computing paradigm with low latency and high response
time. This paradigm provides services for end-devices at the network edge and brings …

Edge video analytics based on deep learning has become an important building block for
many modern intelligent applications such as mobile augmented reality and autonomous …

Widely deployed smart cameras are generating a large amount of video data and capable of
processing frames on devices. Empowered by edge computing, the video data can also be …

Edge intelligence (EI) has received a lot of interest because it can reduce latency, increase
efficiency, and preserve privacy. More significantly, as the Internet of Things (IoT) has …

While recent advances in deep neural networks (DNNs) enabled remarkable performance
on various computer vision tasks, it is challenging for edge devices to perform real-time …

The efficient processing of video streams is a key component in many emerging Internet of
Things (IoT) and edge applications, such as Virtual and Augmented Reality (V/AR) and self …

For IoT networks, signals are captured by edge sensors, system-wide decisions are made at
a central host, and processing may be performed at either or both sides of the network …