Internet of Things (IoT) is an emerging domain that promises ubiquitous connection to the Internet, turning common objects into connected devices. The IoT paradigm is changing the way people interact with things around them. It paves the way for creating pervasively connected infrastructures to support innovative services and promises better flexibility and efficiency. Such advantages are attractive not only for consumer applications, but also for the industrial domain. Over the last few years, we have been witnessing the IoT paradigm making its way into the industry marketplace with purposely designed solutions. In this paper, we clarify the concepts of IoT, Industrial IoT, and Industry 4.0. We highlight the opportunities brought in by this paradigm shift as well as the challenges for its realization. In particular, we focus on the challenges associated with the need of energy efficiency, real-time performance, coexistence, interoperability, and security and privacy. We also provide a systematic overview of the state-of-the-art research efforts and potential research directions to solve Industrial IoT challenges.

Industrial Internet of Things: Challenges, Opportunities, and Directions

With the introduction of the Internet of Things (IoT) and cyberphysical system (CPS) concepts in industrial application scenarios, industrial automation is undergoing a tremendous change. This is made possible in part by recent advances in technology that allow interconnection on a wider and more fine-grained scale. The purpose of this article is to review technological trends and the impact they may have on industrial communication. We will review the impact of IoT and CPSs on industrial automation from an industry 4.0 perspective, give a survey of the current state of work on Ethernet time-sensitive networking (TSN), and shed light on the role of fifth-generation (5G) telecom networks in automation. Moreover, we will point out the need for harmonization beyond networking.

The Future of Industrial Communication: Automation Networks in the Era of the Internet of Things and Industry 4.0

One of the most significant advances in the development of computer science, information and communication technologies is represented by the cyber-physical systems (CPS). They are systems of collaborating computational entities which are in intensive connection with the surrounding physical world and its on-going processes, providing and using, at the same time, data-accessing and data-processing services available on the Internet. Cyber-physical production systems (CPPS), relying on the latest, and the foreseeable further developments of computer science, information and communication technologies on one hand, and of manufacturing science and technology, on the other, may lead to the 4th industrial revolution, frequently noted as Industrie 4.0. The paper underlines that there are significant roots in general – and in particular to the CIRP community – which point towards CPPS. Expectations towards research in and implementation of CPS and CPPS are outlined and some case studies are introduced. Related new R&D challenges are highlighted.

Cyber-physical systems in manufacturing

This paper provides an overview of the recent developments in data-based techniques focused on modern industrial applications. As one of the hottest research topics for complicated processes, the data-based techniques have been rapidly developed over the past two decades and widely used in numerous industrial sectors nowadays. The core of data-based techniques is to take full advantage of the huge amounts of available process data, aiming to acquire the useful information within. Compared with the well-developed model-based approaches, data-based techniques provide efficient alternative solutions for different industrial issues under various operating conditions. The main objective of this paper is to review and summarize the recent achievements in data-based techniques, especially for complicated industrial applications, thus providing a referee for further study on the related topics both from academic and practical points of view. This paper begins with a brief evolutionary overview of data-based techniques in the last two decades. Then, the methodologies only based on process measurements and the model-data integrated techniques will be further introduced. The recent developments for modern industrial applications are, respectively, presented mainly from perspectives of monitoring and control. The new trends of data-based technique as well as potential application fields are finally discussed.

Data-Based Techniques Focused on Modern Industry: An Overview

In this paper we discuss a selection of promising and interesting research areas in the design of protocols and systems for wireless industrial communications. We have selected topics that have either emerged as hot topics in the industrial communications community in the last few years (like wireless sensor networks), or which could be worthwhile research topics in the next few years (for example cooperative diversity techniques for error control, cognitive radio/opportunistic spectrum access for mitigation of external interferences).

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Recent and Emerging Topics in Wireless Industrial Communications: A Selection

Industrielle Netzwerke werden immer komplexer durch den Einsatz neuer Technologien wie Time Sensitive Networking (TSN). Die Anforderungen werden zudem verscharft durch sich andernde gesetzliche Vorgaben wie z.B. bei Smart Grids. Planer solcher Netzwerke haben haufig spezifische Echtzeitanforderungen, Datenraten und Redundanzanforderungen zu beachten, deren Realisierung u.a. abhangig sind von der Leistungsfahigkeit und der Topologie des Netzwerkes. Designalternativen sollten daher quantitativ bewertbar sein, um individuelle Planungsaspekte erweiterbar sein und eine generelle Anwendbarkeit aufweisen. Da diese Kriterien aktuell von keinem verfugbaren Planungswerkzeug erfullt werden, wurde in diesem Beitrag mit Hilfe von Matlab Simulink® ein generisches und skalierbares Modell von Netzwerkkomponenten erstellt. Diese Komponenten konnen parametriert und in einer anwendungsnahen Topologie kombiniert werden. Leistungsqualifizierende Parameter wie die Echtzeitfahigkeit, Auslastung der Komponenten, Ausfallsicherheit und die Topologie wurden zur Bewertung von Designszenarien eines beispielhaften Windparks herangezogen. Eine Erweiterung des Modells ermoglicht es daruberhinausgehend die Auswirkungen von zukunftigen Technologien, wie z.B. TSN zu analysieren.

Modellbasierter Entwurfsassistent zur Auslegung Spezifischer Architektur- und Konfigurationseigenschaften von Kommunikationsnetzen mit Echtzeitanforderungen

INTERBUS Means Speed, Connectivity, Safety

Increasing traffic congestion leads to significant costs, whereby poorly configured signaled intersections are a common bottleneck and root cause. Traditional traffic signal control (TSC) systems employ rule-based or heuristic methods to decide signal timings, while adaptive TSC solutions utilize a traffic-actuated control logic to increase their adaptability to real-time traffic changes. However, such systems are expensive to deploy and are often not flexible enough to adequately adapt to the volatility of today’s traffic dynamics. More recently, this problem became a frontier topic in the domain of deep reinforcement learning (DRL) and enabled the development of multi-agent DRL approaches that can operate in environments with several agents present, such as traffic systems with multiple signaled intersections. However, many of these proposed approaches were validated using artificial traffic grids. This paper presents a case study, where real-world traffic data from the town of Lemgo in Germany is used to create a realistic road model within VISSIM. A multi-agent DRL setup, comprising multiple independent deep Q-networks, is applied to the simulated traffic network. Traditional rule-based signal controls, modeled in LISA+ and currently employed in the real world at the studied intersections, are integrated into the traffic model and serve as a performance baseline. The performance evaluation indicates a significant reduction of traffic congestion when using the RL-based signal control policy over the conventional TSC approach with LISA+. Consequently, this paper reinforces the applicability of RL concepts in the domain of TSC engineering by employing a highly realistic traffic model.

Multi-Agent Deep Reinforcement Learning For Real-World Traffic Signal Controls - A Case Study

Migrationskonzept zur Einführung von Ethernet TSN in die Feldebene

The advent of Industry 4.0 brings the Internet of Things (IoT) and Cyber-Physical Systems (CPSs) inside the factory premise and made the boundaries between the information and operational technologies (IT & OT) obsolete. This, in turn, introduces the problem of interoperability due to the integration of multiple industrial protocols and technologies from various automation networks. The aim of this paper is to investigate this interoperability problem of heterogeneous network management and propose a semantic network knowledge base as a solution to it. It also describes a set of generic interface functions for data acquisition.

Jurgen Jasperneite

Papers

Modellbasierter Entwurfsassistent zur Auslegung Spezifischer Architektur- und Konfigurationseigenschaften von Kommunikationsnetzen mit Echtzeitanforderungen

INTERBUS Means Speed, Connectivity, Safety

Multi-Agent Deep Reinforcement Learning For Real-World Traffic Signal Controls - A Case Study

Migrationskonzept zur Einführung von Ethernet TSN in die Feldebene

Work-in-Progress: Semantic Knowledge Base as a Solution for Heterogeneous Industrial Network Management