Industry 4.0: A bibliometric analysis and detailed overview

Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective

Scientific mapping to identify competencies required by industry 4.0

Intrusion detection systems in the Internet of things: A comprehensive investigation

Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This paper surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, and criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Second, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, and data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances in industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this paper thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field, and the industrial, manufacturing, and automation field. This paper can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

Data Management in Industry 4.0: State of the Art and Open Challenges

Large-scale machine coordination is a primary approach for flexible manufacturing, enabling large-scale autonomous machines to dynamically coordinate their actions in pursuit of a custom task. One of the key challenges for such large-scale systems is finding high-dimensional coordination decision-making policies. Multitask policy gradient algorithms can be used in search of high-dimensional policies, particularly in collaborative decision support systems and distributed control systems. However, it is difficult for these algorithms to learn online high-dimensional coordination control policies (CCP) from large-scale custom manufacturing tasks. This paper proposes a large-scale online multitask learning and decision-making approach, which can consecutively learn high-dimensional CCP in order to quickly coordinate machine actions online for large-scale custom manufacturing task. A large-scale online multitask leaning algorithm is developed, which is able to learn large-scale high-dimensional CCP in a flexible manufacturing scenario. An online stochastic planning algorithm is proposed, which online optimizes the Markov network structure in order to avoid expensive global search for the optimal policy. Experiments have been undertaken using a professional flexible manufacturing testbed deployed within a smart factory of Weichai Power in China. Results show the proposed approach to be more efficient when compared with previous works.

Large-Scale Online Multitask Learning and Decision Making for Flexible Manufacturing

The coordinated learning is importance of technique for cooperative multi-objects system in large-scale Internet of Things . The coordinated learning has attracted a lot of attention for its applications in Internet of Things. However, the self-adaptive makes the coordinated learning difficult to be used in IoT. This paper proposes multi-objects scalable coordinated learning algorithm based on the maximum potential loss of coordination. The algorithm defines an interaction measure that allows objects to dynamically estimate the potential utility loss of coordination with any cluster of objects. The interaction mechanism makes each object compute their beneficial coordination set in different situations and makes the best use of their limited communication resource in Internet of Things. As a result of experiments, our algorithm adapts policy learning of object and their coordination network for different context. Finally, the experiments with the smart agriculture data set demonstrate that the proposed scheme is effective and robust.

Multi-objects scalable coordinated learning in internet of things

Generating sequential decision process from huge amounts of measured process data is a future research direction for collaborative factory automation, making full use of those online or offline process data to directly design flexible make decisions policy, and evaluate performance. The key challenges for the sequential decision process is to online generate sequential decision-making policy directly, and transferring knowledge across tasks domain. Most multi-task policy generating algorithms often suffer from insufficient generating cross-task sharing structure at discrete-time nonlinear systems with applications. This paper proposes the multi-task generative adversarial nets with shared memory for cross-domain coordination control, which can generate sequential decision policy directly from raw sensory input of all of tasks, and online evaluate performance of system actions in discrete-time nonlinear systems. Experiments have been undertaken using a professional flexible manufacturing testbed deployed within a smart factory of Weichai Power in China. Results on three groups of discrete-time nonlinear control tasks show that our proposed model can availably improve the performance of task with the help of other related tasks.

Multi-Task Generative Adversarial Nets with Shared Memory for Cross-Domain Coordination Control.

The online anomaly detection has been propounded as the key idea of monitoring fault of large-scale sensor nodes in Internet of Things. Now, the exciting progresses of research have been made in online anomaly detection area. However, the highly dynamic distributing character of Internet of Things makes the anomaly detection scheme difficult to be used in online manner. This paper presents a new online anomaly learning and detection mechanism for large-scale service of Internet of Thing. Firstly, our model uses the reversible-jump MCMC learning to online learn anomaly-free of dynamics network and service data. Next, we perform a structural analysis of IoT-based service topology by network utility maximization theory. The results of experiment demonstrate the method accuracy in forecasting dynamics network and service structures from synthetic data.

A new online anomaly learning and detection for large-scale service of Internet of Thing

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Papers

Large-Scale Online Multitask Learning and Decision Making for Flexible Manufacturing

Multi-objects scalable coordinated learning in internet of things

Multi-Task Generative Adversarial Nets with Shared Memory for Cross-Domain Coordination Control.

A new online anomaly learning and detection for large-scale service of Internet of Thing