Positioning technology is considered to be an essential tool for future Internet of Things (IoT) applications, especially for the smart factory. Compared to traditional wireless network positioning systems, LiFi (Light Fidelity) localization technology has a greater potential to provide the sub-meter level accuracy required for smart manufacturing applications, due to its low multi-path and flat channel characteristics. In this paper, we evaluate a LiFi positioning system, based on the ITU-T G.9991 standard for optical wireless communication (OWC), for the first time in a factory environment. Our results show a 3-D positioning error of about 5 cm and a further improvement down to about 3 cm by introducing correction factor. The correction factor is based on an initial calibration of the system, to address the observed dependency of the ranging error vs. the angle between transmitter and receiver. The investigated approach demonstrates the possibility of further development of LiFi systems for the positioning of products and navigation vehicles in future smart factories.

This paper provides a techno-economical study comparing total cost of ownership (TCO) for the deployment of very dense wireless networks for new Internet of things (IoT) applications. Mobile optical wireless communication (OWC) also known as Light Fidelity (LiFi) offers unique advantages for the industrial IoT such as robustness through exclusive channel access in the unlicensed optical spectrum, lower latency, and enhanced security compared to common radio technologies like Wi-Fi or 5G. The deployment of LiFi and Wi-Fi is compared in an industrial scenario including the required fixed backbone and assuming full coverage at various wireless capacities. Six different network topologies are considered in different industrial scenarios. The comparison is performed in terms of Bill of Material, and in terms of costs. Regarding backhauling the paper shows that a fully wired topology requires higher installation costs but incurs in lower operational costs, resulting in lower total costs of ownership (TCO). Furthermore, power consumption has been identified as the cost driver for all topologies (more than 200% of the initial investments), triggering the need for power-saving techniques. Last but not least, the separation of LiFi Optical Front Ends (OFEs) and Wi-Fi access points (APs) has been identified as a critical design parameter, as shorter separation requires higher costs but reduces the cost per delivered bitrate. We discuss promising ways for the deployment of LiFi instead or in addition to Wi-Fi for very dense wireless networks demanding higher capacities than Wi-Fi can deliver in factory halls.

The denoising of super-resolution structured illumination microscopy (SR-SIM) images using deep convolutional neural networks (CNNs) has re-ceived much attention in recent years. The contrast and fidelity of biological cell structures in denoised SR-SIM images critically depend on the CNN architecture and the chosen loss function. In this work, we propose two new combinations of loss functions for denoising SR-SIM images with low signal-to-noise ratio. The first combination consists of a loss component computed in the frequency domain by applying Fast Fourier Transform (FFT) to the image data and of pixel-wise loss. The second combination combines the loss in the frequency domain with a feature-based loss. In a series of experiments, these new loss functions are compared with various traditional loss functions for image de-noising. The results show that CNNs trained with the novel losses are superior in terms of PSNR and SSIM values. Moreover, critical structures of biological cells are better preserved in the denoised network output. Further results on the BSD dataset are also promising.

The ongoing process of shop floor digitalization makes production processes more transparent and helps technical staff and managers at their day-to-day work in modern factories. The digitalization is enabled by a wide variety of applications which run on different device types and demand support for different network characteristics.

For a variety of applications, the task of global localization is to determine both, the position and the orientation of one or more mobile objects. An important area of application is mobile robotics, where knowledge of the heading angle is of great importance. One way to localize a mobile robot in real time is to use a network consisting of IEEE 802.15.4a radio nodes based on Ultra-wideband (UWB) technology. In addition to measuring the signal propagation time, UWB radio nodes equipped with multiple transceiver modules can also estimate the Angle of Arrival (AoA) of the signal by capturing the phase differences. The additional angle information can be used to increase location accuracy and reduce the absolute number of radio nodes required in a deployment environment. For this reason, this paper investigates the feasibility of AoA estimation based on UWB radio nodes. Specifically, radio nodes from Nanotron Technologies/Inpixon based on the swarm bee ER module are employed. One problem of the specific circuit board design used is that the antenna spacing is so large that ambiguities occur when detecting the phase differences, which leads to large AoA estimation errors. Hence, methods for solving this problem are presented and their functionality is evaluated based on real world measurements.

The ongoing shop floor digitalization confronts manufacturers with challenges in computer networks. Wi-Fi became a standard for enabling mobile applications in factories but suffers from operating in the shared industrial, scientific and medical (ISM) radio band, which makes Wi-Fi networks prone to interference from wireless networks that operate in parallel. Consequently, Wi-Fi is a less reliable wireless network technology for smart factories. LiFi and 5G are alternatives that do not rely on ISM bands. With a combination of 5G and LiFi we reach flexibility, reliability, wide coverage, and high throughput for fixed and mobile devices in factories. In this paper, we demonstrate the use of LiFi as a non-3GPP access technology for the 5G core network and describe our initial testing of the N3IWF implementation. Besides, this paper describes a multistage demonstrator set-up, which allows to conduct initial validation tests in a distributed testbed environment. This approach enables the direct migration of developed system components into a real factory environment considering the rough environment of manufacturing halls.

The article investigates the application of NeuroEvolution of Augmenting Topologies (NEAT) to generate and parameterize artificial neural networks (ANN) on determining allocation and sequencing decisions in a two-stage hybrid flow shop scheduling environment with family setup times. NEAT is a machine-learning and neural architecture search algorithm, which generates both, the structure and the hyper-parameters of an ANN. Our experiments show that NEAT can compete with state-of-the-art approaches in terms of solution quality and outperforms them regarding computational efficiency. The main contributions of this article are: (i) A comparison of five different strategies, evaluated with 14 different experiments, on how ANNs can be applied for solving allocation and sequencing problems in a hybrid flow shop environment, (ii) a comparison of the best identified NEAT strategy with traditional heuristic and metaheuristic approaches concerning solution quality and computational efficiency.

Softwarization facilitates the introduction of smart manufacturing applications in the industry. Manifold devices such as machine computers, Industrial IoT devices, tablets, smartphones and smart glasses are integrated into factory networks to enable shop floor digitalization and big data analysis. To handle the increasing number of devices and the resulting traffic, a flexible and scalable factory network is necessary which can be realized using softwarization technologies like Network Function Virtualization (NFV). However, the security risks increase with the increasing number of new devices, so that cyber security must also be considered in NFV-based networks. Therefore, extending our previous work, we showcase threat detection using a cloud-native NFV-driven intrusion detection system (IDS) that is integrated in our industrial-specific network services. As a result of the threat detection, the affected network service is put into quarantine via automatic network reconfiguration. We use the 5GTANGO service platform to deploy our developed network services on Kubernetes and to initiate the network reconfiguration. Our focus is on demonstrating the automatic network reconfiguration that is triggered by the IDS.

The Industry 4.0 sector is evolving in a tremendous pace by introducing a set of industrial automation mechanisms tightly coupled with the exploitation of Internet of Things (IoT), 5G and Artificial Intelligence (AI) technologies. By combining such emerging technologies, interconnected sensors, instruments, and other industrial devices are networked together with industrial applications, formulating the Industrial IoT (IIoT) and aiming to improve the efficiency and reliability of the deployed applications and provide Quality of Service (QoS) guarantees. However, in a 5G era, efficient, reliable and highly performant applications' provision has to be combined with exploitation of capabilities offered by 5G networks. Optimal usage of the available resources has to be realised, while guaranteeing strict QoS requirements such as high data rates, ultra-low latency and jitter. The first step towards this direction is based on the accurate profiling of vertical industries' applications in terms of resources usage, capacity limits and reliability characteristics. To achieve so, in this paper we provide an integrated methodology and approach for benchmarking and profiling 5G vertical industries' applications. This approach covers the realisation of benchmarking experiments and the extraction of insights based on the analysis of the collected data. Such insights are considered the cornerstones for the development of AI models that can lead to optimal infrastructure usage along with assurance of high QoS provision. The detailed approach is applied in a real IIoT use case, leading to profiling of a set of 5G network functions.

In recent years, the interest in interconnecting production machines has grown and new technologies such as augmented reality enter the shop floor. This evolution is driven by hyped topics such as Industry 4.0 and Internet of Things. These topics promise benefits through increasing transparency in factories, such as improvement in efficiency and reduced costs, e.g., due to data analysis. As a result, the demand on new concepts and technologies for factory networks is rising to fulfill the upcoming new requirements. Therefore, the development of 5G comes just in time. This paper is focused on one of the 5G core technologies network function virtualization (NFV). We propose two use cases that demonstrate how NFV enables flexible smart manufacturing. In NFV technology, virtual network functions (VNF) are composed to network services. We introduce the application of our vertical-specific network services that enable augmented reality on-demand and the flexible interconnection of production machines with services in company's cloud backend.

Ship propulsion concepts which use energy harvested from wind as an additional source of propulsive power are of great interest for the shipping industry, addressing both environmental and business considerations. Usage of such hybrid propulsion concepts is dependent on seamless integration with current systems and processes on board. An intelligent system, monitoring the wind-assisted propulsion system, assisting the vessel's crew in its use and enabling automated, energetically optimized operation makes more widespread use of such technologies feasible. This article describes the design of such an intelligent assistance system, using an ECO Flettner rotor (which uses the Magnus effect to generate an aerodynamic thrust force) installed on the MV Fehn Pollux as an example of a wind-assisted propulsion system. Special focus is given to the approach used in the system architecture's design for data acquisition, processing and storage as well as visualization using the system's human machine interface (HMI). Ultimately, the presented system helps further increase the fuel-saving potential of wind-assisted propulsion systems.

5G together with software defined networking (SDN) and network function virtualisation (NFV) will enable a wide variety of vertical use cases. One of them is the smart manufacturing case which utilises 5G networks to interconnect production machines, machine parks, and factory sites to enable new possibilities in terms of flexibility, automation, and novel applications (industry 4.0). However, the availability of realistic and practical proof-of-concepts for those smart manufacturing scenarios is still limited. This demo fills this gap by not only showing a real-world smart manufacturing application entirely implemented using NFV concepts, but also a lightweight prototyping framework that simplifies the realisation of vertical NFV proof-of-concepts. During the demo, we show how an NFV-based smart manufacturing scenario can be specified, on-boarded, and instantiated before we demonstrate how the presented NFV services simplify machine data collection, aggregation, and analysis.

In recent years developments of Industrial IoT components and data analysis paved the way for optimization of manufacturing processes. Additional sensors provide manifold data of machine parks and individual machines. Due to the huge amount of data, communication and storage systems get into focus of research. In this work, we are introducing a concept for the usage of Network Services in smart manufacturing, creating the missing link between the closed machine architecture and flexible 5G services.

The industrial world is currently undergoing changes due to increasing digitalization and computerization in companies (Industry 4.0). Existing control and management components and systems located within the upper layers of an enterprise's structure are connected to the shop floor. With the current paradigm shift in manufacturing, the need for more collaboration between personnel with different fields of expertise arises. This means that members of a multitude of professions need to work and communicate with each other. These developments in the industry also have an influence on current education curricula at schools and universities. Interdisciplinary knowledge across different curricula is required to work in an Industry 4.0 environment. This paper presents a general methodology for the evaluation of academic curricula of a university with regard to Industry 4.0 related topics. Based on this methodology, a case study was carried out, examining study programs of the Faculty of Technology at the University of Applied Sciences Emden/Leer. As a result of the case study, we present recommendations to increase the coverage of Industry 4.0 relevant topics in the current academic curriculum of the university.

Cyber-physical systems (CPS) are key enabling technologies for the fourth industrial revolution, referred to as Industrie 4.0 or Industry 4.0. The Reference Architecture Model Industrie 4.0 (RAMI4.0) has recently been standardized and OPC Unified Architecture (OPC UA) is listed as the sole recommendation for implementation of a communication layer. Many automation and control systems offer already implementations of OPC UA but no satisfying implementation of OPC UA was found for Arduino, a popular platform for engineering physical computing systems. This paper presents open source integration and application of a customizable OPC UA server on an Arduino Yun board using open62541, an open source and free implementation of OPC UA. The Arduino board discussed in this paper offers hot-end closed-loop temperature control for a 3D printer but the temperature set value and control parameters can be manipulated and requested via OPC UA using OPC UA clients. The application is verified using Prosys OPC UA Client and UaExpert. The results of our research can be used for developing open source cyber-physical systems without specialized knowledge in microcontroller programming, bringing Industry 4.0 applications into classrooms without effort.

Ensuring the software quality of mobile applications with respect to performance, robustness, energy consumption, security and privacy is an important problem for a growing researcher and developer community. In this paper, we present Dynalize, a Platform-as-a-Service cloud for the dynamic analysis of mobile applications. It allows researchers and developers to investigate mobile applications at runtime in a virtual device cloud and to publish the performed analyses as web services. In contrast to existing approaches, it makes use of container virtualization on top of Infrastructure-as-a-Service instances, enabling dynamic provisioning and fast deployment of dynamic analyses. A custom container layout and a novel storage solution on the virtual server layer ensures cost- and runtime-efficient large-scale analyses of thousands of apps. The applicability of Dynalize is demonstrated by a security analysis of about 6,000 Android applications. Experiments on container startup, virtual device to container throughput and different storage back ends show the feasibility of the proposed approach.

Pedestrian localization systems require the knowledge of a user's position for manifold applications in indoor and outdoor environments. For this purpose several methods can be used, such as a Global Navigation Satellite System (GNSS). Since GNSS are not available in indoor environments or deep street canyons other techniques have to be used. This could be a time based or signal strength based radio localization system. In this paper a Time Difference of Arrival (TDoA) system is used and combined with a low cost accelerometer and gyroscope. Since the localization device is free mountable at the user's body, the raw data of the gyroscope must be rotated in a global frame. Therefore the accelerometer is used to compute the rotation angles in relation to the earth's gravity. The data of the accelerometer is also used for a step detection and a step length estimation as well. To fuse the different measurements an Extended Kalman Filter (EKF) is employed. While the system is initialized the orientation of the user is not available. Therefore an initial phase is prepended where a reduced model is used. In this phase the orientation has to develop while the user is moving. The duration of the phase is dynamic, depending on the quality of the TDoA measurements. Once the initial phase is passed the complete model is used. To evaluate the introduced algorithm experimental results in different environments are presented.

We present experimental studies and simulations on gate-controlled doping in carbon-based field-effect transistors. While the low density of states in carbon-based semiconductors allows e.g. the investigation of the coupling between a metal and graphene, it is detrimental to the functionality of novel device concepts such as band-to-band tunneling FETs. Creating appropriate doping profiles in the source/drain contacts with additional gates, on the other hand, avoids all dopant-related issues. It will be shown that gate-controlled doping allows obtaining optimum transistor performance which enables exploiting the exceptional properties of graphene and carbon nanotubes for future nanoelectronics devices.

Pedestrian localization systems require the knowledge of a users position for manifold applications in indoor and outdoor environments. For this purpose severel methods can be used, such as a Global Navigation Satellite System (GNSS) or Inertial Navigation Systems (INS). Since GNSS are not available in indoor environments or street canyons, in this paper a 802.15.4a network is used to estimate the pedestrian's position. The used network platform provides the Time Difference of Arrival (TDoA) as well as the Received Signal Strength (RSS). To fuse both measurement types a novel method is implemented which is based on the Extended Kalman Filter (EKF). Due to the low accuracy of RSS it is ignored if the TDoA system performs well. But if the TDoA measurements are affected by multipath propagation or other effects the RSS values are used to identify those situations and to correct the estimated position of the user. To evaluate the algorithm experimental results in two different environments are presented.

Indoor location tracking of mobile robots or transport vehicles using wireless technology is attractive for many applications. IEEE 802.15.4a wireless networks offer an inexpensive facility for localizing mobile devices by time-based range measurements. The main problems of time-based range measurements in indoor environments are errors by multipath and non-line-of-sight (NLOS) signal propagation. This paper describes indoor tracking using range measurements and an Extended Kalman Filter with NLOS mitigation. The commercially available nanoLOC wireless network is utilized for range measurements. The paper presents experimental results of tracking a forklift truck in an industrial environment.

A wireless sensor network consist of spatially distributed autonomous sensor nodes for data acquisition. Accurate localization of sensor nodes is a strong requirement in a wide area of applications. This paper describes the localization of sensor nodes using range measurements and trilateration with the Extended Kalman Filter. The commercially available nanoLOC sensor network is utilized for range measurements. Experimental results show, that a localization accuracy better than 0.5 m can be achieved in most cases.