Differential privacy for industrial internet of things: Opportunities, applications, and challenges
The development of Internet of Things (IoT) brings new changes to various fields.
Particularly, industrial IoT (IIoT) is promoting a new round of industrial revolution. With more …
Particularly, industrial IoT (IIoT) is promoting a new round of industrial revolution. With more …
Industrial Internet of Things: A systematic literature review and insights
Y Liao, EFR Loures… - IEEE Internet of Things …, 2018 - ieeexplore.ieee.org
The connection of embedded computing devices via the Internet has dramatically changed
the way people live. This concept has also been extended to the industrial sector. It not only …
the way people live. This concept has also been extended to the industrial sector. It not only …
Review of smart meter data analytics: Applications, methodologies, and challenges
The widespread popularity of smart meters enables an immense amount of fine-grained
electricity consumption data to be collected. Meanwhile, the deregulation of the power …
electricity consumption data to be collected. Meanwhile, the deregulation of the power …
Big data analytics for 6G-enabled massive internet of things
Z Lv, R Lou, J Li, AK Singh… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
The purposes are to enable large-scale Internet of Things (IoT) devices to analyze data
more effectively and provide high-efficiency, low-energy, and wide-coverage technical …
more effectively and provide high-efficiency, low-energy, and wide-coverage technical …
Data management in industry 4.0: State of the art and open challenges
Information and communication technologies are permeating all aspects of industrial and
manufacturing systems, expediting the generation of large volumes of industrial data. This …
manufacturing systems, expediting the generation of large volumes of industrial data. This …
Deep convolutional computation model for feature learning on big data in internet of things
Currently, a large number of industrial data, usually referred to big data, are collected from
Internet of Things (IoT). Big data are typically heterogeneous, ie, each object in big datasets …
Internet of Things (IoT). Big data are typically heterogeneous, ie, each object in big datasets …
GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game
Due to its simplicity, versatility and the diversity of applications to which it can be applied, k-
means is one of the well-known algorithms for clustering data. The foundation of this …
means is one of the well-known algorithms for clustering data. The foundation of this …
A tensor-based big-data-driven routing recommendation approach for heterogeneous networks
Telecommunication networks are evolving toward a data-center-based architecture, which
includes physical network functions, virtual network functions, as well as various types of …
includes physical network functions, virtual network functions, as well as various types of …
Artificial intelligence for cloud-assisted smart factory
J Wan, J Yang, Z Wang, Q Hua - IEEE Access, 2018 - ieeexplore.ieee.org
In the context of industry 4.0, the main way to realize the intelligent manufacturing is to build
a smart factory integrated with the advanced technologies, such as the Internet of Things …
a smart factory integrated with the advanced technologies, such as the Internet of Things …
Energy-efficient scheduling for real-time systems based on deep Q-learning model
Energy saving is a critical and challenging issue for real-time systems in embedded devices
because of their limited energy supply. To reduce the energy consumption, a hybrid dynamic …
because of their limited energy supply. To reduce the energy consumption, a hybrid dynamic …