Themes and Scopes

Smart devices, sensors, and machines generate a wealth of valuable data with suitable connectivity from Industrial Internet of Things (IIoT) across manufacturing environments or supply chain. Artificial intelligence (AI) technologies (e.g. deep learning, reinforcement learning, machine vision, etc.) can create new value chain through gathering, processing, and learning these data. The integration of AI and IIoT have an exciting promise. Machine learning-powered rapid testing during the industrial design process can enable greater program and cost certainty than previously realizable. Data analytics tools can now not only predict when and where machinery and equipment is going to need maintenance with a high degree of accuracy, but act on that need. Machine vision and deep learning help the self-drive vehicle to enhance accuracy of recognizing traffic signs. Meanwhile, connected equipment across the supply-chain provides real time data which can be utilized by machine learning and predictive analytics technologies.

There are still some challenges, which hinder the large-scale application. Specially, some of these data sources are structured (such as sensor signals), some are semi-structured (such as records of manual operations), and some are completely unstructured (such as image files). However, most of the unstructured data is either unused or used only for very specific, tactical purposes. lots of industrial data is generally not utilized strategically, and is poor interoperability across incompatible technologies, systems, and data types, so it is unable to effectively extract value from them. Furthermore, the transmission of mass industrial data with different priorities also is a hard job for industrial heterogeneous networks, AI-enabled analytic technologies will help to optimize networks and realize intelligent data transmission. In addition, AI-powered applications usually run on the remote cloud, and the service delay is uncontrollable, but in industrial process, the service delay is constrained strictly. Fortunately, the emerging of edge computing at the edge of network can further propel the integration AI and IIoT.

Thus, this special issue aims to solicit original papers with novel contributions on the AI for advance IIoT, Novel applications by the AI for IIoT are particularly welcome.

Topics of this special issue include, but are not limited to:

l  New architecture and framework of AI in IIoT

l  AI-enabled SDN network technologies in IIoT

l  AI algorithms for signal detection and synchronization in IIoT

l  AI algorithms for localization, positioning and tracking techniques in IIoT

l  AI-enabled fault diagnosis and prognostic management in IIoT

l  AI for smart maintenance, upgrades, and management using IIoT

l  Intelligent robots based on integration of AI and IIoT

l  Edge computing based on integration of AI and IIoT

l  Other concepts and applications related to AI in IIoT

Instructions for Manuscripts

Selected Manuscripts must be prepared according to the standards for submission to the Journal of Internet Technology; see instructions for Authors in:

https://jit.ndhu.edu.tw/about/submissions#authorGuidelines

The sample format can be downloaded from

https://jit.ndhu.edu.tw/about/submissions#authorGuidelines

Manuscripts must also be uploaded through the electronic submission system: https://mc04.manuscriptcentral.com/internet-tech

Please specify that the manuscript is for the “Advanced Artificial Intelligence for Industrial Internet of Things” special issue.

Important Dates:

Manuscripts submission deadline: Aug. 15, 2019

Notification of Acceptance/Rejection/Revision: Sep. 10, 2019

2nd round check: Oct. 10, 2019

Final Manuscripts Due: Nov. 30, 2019 (Tentative)

Tentative Publication Date: Fourth Quarter in 2020

Guest Editors:

Jiafu Wan

School of Mechanical & Automotive Engineering

South China University of Technology, China

jiafuwan_76@163.com; jiafu_wan@ieee.org

Jianqi Liu

School of Automation,

Guangdong University of Technology, China

liujianqi@ieee.org

Ling Xia Liao

School of Electronic Information & Automation,

Guilin University of Aerospace Technology, China

Liaolx@ieee.org