Performing deep neural network (DNN) inference in real time requires excessive network
resources, which poses a big challenge to the resource-limited industrial Internet of things …

Joint task inference, which fully utilizes end edge cloud cooperation, can effectively enhance
the performance of deep neural network (DNN) inference services in the industrial internet of …

Collaboration among industrial Internet of Things (IoT) devices and edge networks is
essential to support computation-intensive deep neural network (DNN) inference services …

With the revolution of smart industry, more and more Industrial Internet of Things (IIoT)
devices as well as AI algorithms are deployed to achieve industrial intelligence. While …

Recent visualizations of smart cities, factories, healthcare system and etc. raise challenges
on the capability and connectivity of massive Internet of Things (IoT) devices. Hence, edge …

In recent years, deep learning algorithms are increasingly adopted by a wide range of data-
intensive and time-critical Internet of Things (IoT) applications. As a result, several new …

With the development of the Internet of Things (IoT) and the birth of various new IoT devices,
the capacity of massive IoT devices is facing challenges. Fortunately, edge computing can …

In this paper, we investigate how to deploy computational intelligence and deep learning
(DL) in edge-enabled industrial IoT networks. In this system, the IoT devices can …

Edge intelligence, as a prospective paradigm for accelerating DNN inference, is mostly
implemented by model partitioning which inevitably incurs the large transmission overhead …

With the rapid development of Internet-of-Things (IoT) and the explosive advance of deep
learning, there is an urgent need to enable deep learning inference on IoT devices in Mobile …