Abstract
The Industrial Internet of Things (IIoT) enables the integration of physical devices such as sensors and actuators into the virtual world of automation application systems via different communication protocols. Interoperability among the “things” appears to be one of the biggest conceptual and technological challenges in developing the IIoT framework. Typically, collaboration at the field device level is very limited. Instead, the decision-making process is usually propagated to higher levels with substantial computational resources. This centralized architecture has been widely deployed based on global cloud infrastructure. However, sending data over the cloud for analysis may bring about privacy and security threats. Besides, network latency could be another factor that reduces adaptability. In this article, we propose a decentralized approach that applies the concepts of local automation cloud. By using semantic technologies to achieve autonomicity, the approach enables real-time monitoring of the control systems within one local cloud and automates orchestration and configuration locally through adaptation based on semantic policies. The approach is deployed and tested on a chemical production use case in which business-level policies have been used for dynamical planning for suppliers and automatic detection of malfunctioning sensors with subsequent adaptation to continuing supply planning and production as smooth as possible.
Highlights
Industry 4.0 promotes a new generation of manufacturing systems with integrated sensors and actuators that are used for process control at the factory floor [1]
EVALUATION we discuss the performance evaluation of the proposed approach based on the case study presented in the previous section
The following perspectives will be considered in our conducted experiments:
Summary
Industry 4.0 promotes a new generation of manufacturing systems with integrated sensors and actuators that are used for process control at the factory floor [1]. In this architecture, the decision-making process is delegated to the individual agents in the factory shop-floor instead of the central manufacturing system [5]. The decision-making process is delegated to the individual agents in the factory shop-floor instead of the central manufacturing system [5] The approaches employing this decentralized architecture are designed based on distributed control in which individual systems react to their local condition in real-time [6]. Self-adaptation enables the systems to exchange data with each other, analyze the data, and make appropriate decisions automatically This approach can be applied to condition monitoring tasks to determine the correctness of the operating states of the physical devices or manufacturing processes. 3) We lay out our vision that exploits the local automation cloud concept to migrate from traditional centralized architecture to decentralized architecture in order to achieve dynamic adaptation of manufacturing processes
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