Differential privacy for industrial internet of things: Opportunities, applications, and challenges

B Jiang, J Li, G Yue, H Song - IEEE Internet of Things Journal, 2021 - ieeexplore.ieee.org
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 …

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 …

Review of smart meter data analytics: Applications, methodologies, and challenges

Y Wang, Q Chen, T Hong… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
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 …

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 …

Data management in industry 4.0: State of the art and open challenges

TP Raptis, A Passarella, M Conti - IEEE Access, 2019 - ieeexplore.ieee.org
Information and communication technologies are permeating all aspects of industrial and
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

P Li, Z Chen, LT Yang, Q Zhang… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
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 …

GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game

MJ Rezaee, M Eshkevari, M Saberi… - Knowledge-Based Systems, 2021 - Elsevier
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 …

A tensor-based big-data-driven routing recommendation approach for heterogeneous networks

X Wang, LT Yang, L Kuang, X Liu, Q Zhang… - IEEE …, 2019 - ieeexplore.ieee.org
Telecommunication networks are evolving toward a data-center-based architecture, which
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 …

Energy-efficient scheduling for real-time systems based on deep Q-learning model

Q Zhang, M Lin, LT Yang, Z Chen… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
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 …