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.
Shrouf and Miragliotta @cite_2 report on different approaches for energy management enabled by Internet of Things (IoT) technologies. Based on literature, expert interviews, and reports of manufactures, they summarize different IoT architectures for power monitoring and present a general abstraction of them. The resulting architecture primarily focuses on network interconnections and integration of other systems. As in our approach, it respects real-time data processing and the challenge of integrating data of different sensors and data formats. However, data is only processed in a cloud or local server infrastructure and does not follow fog computing paradigms. The architecture represents a general approach and is therefore too abstract to offer a reference implementation.
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Summary Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM c