Edge Intelligence for Industrial Internet of Things
摘要截稿:
全文截稿: 2018-12-01
影响因子: 8.808
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 计算机科学 - 1区
• 小类 : 计算机:硬件 - 1区
• 小类 : 计算机:信息系统 - 1区
• 小类 : 工程:电子与电气 - 2区
• 小类 : 电信学 - 2区
Overview
The growth of the internet of things (IoT) is making dramatic impact in both household and industry settings. The term industrial Internet of things (IIoT) is often encountered in the manufacturing industries, referring to the industrial subset of the IoT. Specifically, IIoT is a new ecosystem that combines intelligent and autonomous machines, advanced predictive analytics, and machine-human collaboration to improve productivity, efficiency and reliability. It brings about a world where smart, connected embedded systems and products operate as part of larger systems.
IIoT connects billions of mobile digital devices, manufacturing machines, industrial equipment, etc., and generates an unprecedented volume of industrial data. It will consume much network bandwidth and increase latency to move the large amount of data from industrial network edge to a central data center. Thus, IIoT with a centralized data center may not have the ability to support prompt data analysis, e.g., smart applications for making good and timely responses. To solve this issue, edge computing has been proposed to connect IIoT devices and their remote data center.
Apart from the above basic requirements of network latency and bandwidth, these IIoT-enabled smart applications are often in demand of capabilities of self-monitoring, self-diagnosing, self-healing, self-directing, etc. Thus, integrating intelligence into edge is without doubt a promising development trend. New machine learning engines are expected for emerging edge computing frameworks in IIoT, but limited research efforts have been made in Edge Intelligence for IIoT so far. The scope of this special issue is to present and highlight the advances and the latest intelligent technologies, implementations and applications in the field of edge-based IIoT, so as to move the theoretical and practical frontiers forward for a deeper understanding from both academic and industrial viewpoints.
Possible topics include but are not limited to:
Analytics architectures, frameworks, and models for intelligent edge-based IIoT
Theory, standards, protocols, and strategies for intelligent edge-based IIoT
Intelligent communication technologies for mobile, wireless, wired edge-based IIoT
Security and privacy in edge intelligent IIoT
Machine learning based optimization methods for edge intelligent IIoT
Data mining and knowledge discovery based on big data analysis from the aspect of edge-based IIoT
Intelligent decision-making systems for edge-based IIoT
Human and machine intelligence fusion in the edge-based IIoT