Detailed knowledge about the electrical power consumption in industrial production environments is a prerequisite to reduce and optimize their power consumption. Today's industrial production sites are equipped with a variety of sensors that, inter alia, monitor electrical power consumption in detail. However, these environments often lack an automated data collation and analysis. We present a system architecture that integrates different sensors and analyzes and visualizes the power consumption of devices, machines, and production plants. It is designed with a focus on scalability to support production environments of various sizes and to handle varying loads. We argue that a scalable architecture in this context must meet requirements for fault tolerance, extensibility, real-time data processing, and resource efficiency. As a solution, we propose a microservice-based architecture augmented by big data and stream processing techniques. Applying the fog computing paradigm, parts of it are deployed in an elastic, central cloud while other parts run directly, decentralized in the production environment. A prototype implementation of this architecture presents solutions how different kinds of sensors can be integrated and their measurements can be continuously aggregated. In order to make analyzed data comprehensible, it features a single-page web application that provides different forms of data visualization. We deploy this pilot implementation in the data center of a medium-sized enterprise, where we successfully monitor the power consumption of 16 servers. Furthermore, we show the scalability of our architecture with 20,000 simulated sensors.
We did not find any monitoring approaches for production environments designed in a microservices architecture. However, microservice-based approaches exist for other applications of the Internet of Things, such as @cite_22 and @cite_9 . As we propose in our architecture, these approaches intend to deploy microservices decentralized for flexibility and extendibility. Moreover, they use an asynchronous messaging bus for the exchange of sensor data as in our approach. Both approaches do not focus on scalability and, therefore, do not evaluate this.
@cite_9 @cite_22
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Industrial Internet of things (IIoTs) relies on different devices working together, gathering and sharing data using multiple communication protocols. This heterogeneity becomes a hindrance in the development of architectures that can support applications operating independently of the underlying protocols. Therefore in this paper, we proposed a modular and scalable architecture based on lightweight virtualization. The modularity provided by the proposed architecture combined with lightweight virtualization orchestration supplied by Docker simplifies management and enables distributed deployments. Availability and fault-tolerance characteristics are ensured by distributing the application logic across different devices where a single microservice or even device failure can have no effect on system performance. The proposed architecture is instantiated and tested on a simple time-dependent use case. The obtained results validates that the proposed architecture can be used to deploy services on demand at different architecture layers. This paper presents challenges and issues in smart buildings and the Internet of Things (IoT), which we identified in years of research in real buildings. To tackle these challenges, a decentralized service-oriented architecture based on a message-oriented middleware has been implemented for the domain of smart buildings. It uses a network-transparent IoT message bus and provides the means for composing applications from auxiliary services, which facilitate device abstraction, protocol adaption, modularity, and maintainability. We demonstrate the flexibility of our architecture by describing how three distinct applications---privacy-enhancing energy data visualization, automated building energy management, and a generic user interface---can be integrated and operated simultaneously in our real smart building laboratory. We compare the advantages of our architecture to conventional ones and provide a best-practice solution for the Intranet of Things and Energy in smart buildings. Abstract In recent years a considerable effort has been spent by research and industrial communities in the digitalization of production environments with the main objective of achieving a new automation paradigm, more flexible, responsive to changes, and safe. This paper presents the architecture, and discusses the benefits, of a distributed middleware prototype supporting a new generation of smart-factory-enabled applications with special attention paid to simulation tools. Devised within the scope of MAYA EU project, the proposed platform aims at being the first solution capable of empowering shop-floor Cyber-Physical-Systems (CPSs), providing an environment for their Digital Twin along the whole plant life-cycle. The platform implements a microservice IoT-Big Data architecture supporting the distributed publication of multidisciplinary simulation models, managing in an optimized way streams of data coming from the shop-floor for real-digital synchronization, ensuring security and confidentiality of sensible data. In the context of the Internet of Things, an increasing number of platforms like Xively or ThingSpeak are available to manage ubiquitous sensor data. Strict data formats allow interoperability and informative visualizations, supporting the development of custom user applications. Yet, these strict data formats as well as the common device-centric approach limit the flexibility of these platforms: there are no means to incorporate people and their subjective impressions about the collected data. In order to build the Internet of Things and People and ultimately the Internet of Everything, we aim at providing an extendable concept of data which allows to enrich existing data points with any kind of additional information. This enables us to gain semantic and user specific context by attaching subjective data to objective values. For this end we support data ranging from text-based formats like JSON to images and video footage. This paper provides an overview of our architecture including concept, implementation details and present applications. We distinguish our approach from several other systems and describe two sensing applications namely AirProbe and WideNoise that were implemented for our platform.