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 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.

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.

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.

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.

Nowadays, vehicles are not only merely used as a transportation medium, but also as a highly mobile Internet of Things (IoT) node, collecting data from all types of sources. The delivery of aggregated vehicle sensor data into the cloud for further analysis is very fragile, as vehicles move fast in their environment and channel conditions vary heavily. Next to mastering possible packet loss, communication protocols need to quickly adapt to the frequent data rate changes. Random Linear Network Coding (RLNC) has been proven as an efficient and robust mechanism for reliable data transfer especially on lossy channels. In this paper, the authors extend the Scalable Network Coding (ScalaNC) framework with the novel congestion control Bottleneck, Bandwidth and Round-Trip Time (BBR) in order to quickly adapt to the frequent changes in data rate and allow effective transfer of high amounts of data from vehicles and into the cloud. ScalaNC is validated in a Hardware-in-the-Loop field test consisting of a Long Term Evolution (LTE) base station and channel emulator.

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 aggregation of multiple carriers and even heterogeneous links is a well established method to boost data rates in wireless networks. At the same time, Network Coding has been proven as an efficient and robust mechanism to distribute content in mesh networks or cloud storages. In this paper, we introduce with ScalaNC a new method of heterogeneous link aggregation, which requires no changes in the network infrastructure and incorporates Network Coding to allow for a scalable trade-off between speed, latency and security in terms of confidentiality (information distribution) of the aggregated end-to-end connection. The proposed method is particularly useful in emergency response scenarios, in which first responders need to access cloud storages to retrieve data in harsh communication environments. Using the scalable parameterization the users can increase real-time capability, speed or security depending on the actual needs. ScalaNC is validated with an experimental setup, in which the increase in real-time capability, goodput and security with high packet error rates was demonstrated: while the goodput of an aggregated link operated with Multipath TCP is reduced to around 50 % with occurring packet errors, the ScalaNC-enabled aggregated link maintains stable goodput.

Low-altitude unmanned aerial vehicles (UAVs) combined with WLAN mesh networks (WMNs) have facilitated the emergence of airborne network-assisted applications. In disaster relief, they are key solutions for (1) on-demand ubiquitous network access and (2) efficient exploration of sized areas. Nevertheless, these solutions still face major security challenges as WMNs are prone to routing attacks. Consequently, the network can be sabotaged, and the attacker might manipulate payload data or even hijack the UAVs. Contemporary security standards, such as the IEEE 802.11i and the security mechanisms of the IEEE 802.11s mesh standard, are vulnerable to routing attacks as we experimentally showed in previous works. Therefore, a secure routing protocol is indispensable for making feasible the deployment of UAV-WMN. As far as we know, none of the existing research approaches have gained acceptance in practice due to their high overhead or strong assumptions. Here, we present the position-aware, secure, and efficient mesh routing approach (PASER). Our proposal prevents more attacks than the IEEE 802.11sΓi security mechanisms and the well-known, secure routing protocol ARAN, without making restrictive assumptions. In realistic UAV-WMN scenarios, PASER achieves similar performance results as the well-established, nonsecure routing protocol HWMP combined with the IEEE 802.11s security mechanisms.

Swarms of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) are used for cooperative sensing, clarification and communication relaying in disaster situations and, thereby, allow to reduce jeopardizing the health of rescue personnel. Having meshed interconnection, a swarm maintains a communication network dedicated for applications with context-dependent performance requirements that require reliable traffic classification and conditioning. Nevertheless, these systems are vulnerable because UAVs/UGVs may act incorrectly resulting in an erroneous network behavior which may affect the performance of mission-critical flows. This is a major problem if a couple of untrusted UAVs and UGVs from different rescue organizations are connected. In this paper, the reliability of traffic conditioning within untrusted swarms is enhanced by the Distributed Flow Permission Inspection (DiFPIN) scheme. DiFPIN is used to verify the flow classification to circumvent the usage of erroneous entities on the transmission path. Thereby, the negative impairment of mission-critical flows can be reduced.

In this work we examine the influence of harsh communication conditions on cooperative consensus finding in multi-agent systems. We investigate the impact of network topology on the convergence behavior of cooperative consensus finding. Analysis of the effect of packet error rates on convergence speed and convergence error are carried out in simulations. We propose a model to determine the best number of communication links an agent should maintain in order to find the best tradeoff between spatial coverage and convergence speed. We use these results in our communication-aware mobility algorithms for unmanned aerial systems to actively influence the vehicles' network topology. Experimental implementations show that already small amounts of packet errors disable the agents to find the correct average consensus using well-known consensus algorithms. Consequently, we propose a novel consensus protocol which reliably converges to the average value of the initial agent states. Our results have been obtained using simulations and a hardware testbed.

The communication aspects of Swarm-based Unmanned Aerial Systems (UAS) for surveillance and monitoring tasks have received increased attention in recent research. In this paper, we address the specific challenges of unpredictable incidents as forest fires, accidents in chemical or nuclear plants which cause potentially dangerous plumes. Therefore, we focus on the detection of chemical plume borderlines while maintaining the connectivity within the UAV team. Leveraging previously developed spatial exploration algorithms like Cooperative Repelling Walk we introduce two novel strategies to optimize the trade-off between detection-efficiency and communication constraints: the Aerosol-detecting Cooperative Repelling Walk (ADCRW) and the Distributed Dispersion Detection (DDD) Algorithm. Both algorithms are evaluated with the help of a multi-scale simulation model which includes a newly incorporated Briggs plume model. Considering the mentioned scenario the results of the performance evaluation demonstrate that the detection-efficiency of our novel algorithms is significantly improved, while at the same time connectivity goals are still met: with the new algorithms the time to detect the borderline of a plume can be reduced by 35%, while the connectivity within the swarm is about 100%.

The constraints of current monitoring systems, such as stationary data loggers, wired sensor arrays, manned reconnaissance aircraft or remote sensing by means of satellites, have prompted extensive research in wireless sensor networks. Consequently, the use of Unmanned Aerial Vehicles (UAV) for aerial exploration enables the introduction of aerial sensor networks. In this paper we focus on the development of communication aware steering strategies for UAV swarms to achieve quick and comprehensive Spatial Exploration Ratio while maintaining connectivity within the swarm. We develop, enhance and compare two new random based steering strategies, namely Smart Cube and Cooperative RepellingWalk. Our results show that the proposed algorithms simultaneously attain an efficient equilibrium for the mesh connectivity, sensor perception and distribution in the aerial network. Considering autonomous communication awareness enhances the exploration efficiency in terms of swarm coherence and subsequently reliability.