The industrial wireless automation sector exhibits a huge market growth in the last years. Today, many applications already use wireless technologies. However, the existing wireless solutions do not yet offer sufficient performance with respect to real-time and reliability requirements, particularly for closed-loop control applications. As a result, low latency wireless communication technologies will bridge the gap and can become a key factor for the wide-spread penetration of wireless in industrial communication systems. It is therefore the main goal of this paper to provide a comprehensive overview on requirements, current solutions, and challenges as well as opportunities for future wireless industrial systems. Thereby, presented requirement figures, analysis results, and performance evaluations are based on numerous practical examples from industry.

Smart manufacturing aims to overcome the limitations of today's rigid assembly lines by making the material flow and manufacturing process more flexible, versatile, and scalable. The main economic drivers are higher resource and cost efficiency as the manufacturers can more quickly adapt to changing market needs and also increase the lifespan of their production sites. The ability to close feedback loops fast and reliably over long distances among mobile robots, remote sensors, and human operators is a key enabler for smart manufacturing. Thus, this article provides a perspective on control and coordination over wireless networks. Based on an analysis of real-world use cases, we identify the main technical challenges that need to be solved to close the large gap between the current state of the art in industry and the vision of smart manufacturing. We discuss to what extent existing control-over-wireless solutions in the literature address those challenges, including our own approach toward a tight integration of control and wireless communication. In addition to a theoretical analysis of closed-loop stability, practical experiments on a cyber-physical testbed demonstrate that our approach supports relevant smart manufacturing scenarios. This article concludes with a discussion of open challenges and future research directions.

Automated spectrum analysis serves as a troubleshooting tool that helps to diagnose faults in wireless networks such as difficult signal propagation conditions and coexisting wireless networks. It provides a higher monitoring coverage while requiring less expertise compared with manual spectrum analysis. In this paper, we introduce a data set that can be used to train and evaluate deep learning models, capable of detecting frames from different wireless standards as well as interference between single frames. Since manually labeling a high variety of frames in different environments is too challenging, an artificial data generation pipeline was developed. The data set consists of 20,000 augmented signal segments, each containing a random number of different Wi-Fi and Bluetooth frames, their spectral image representations and labels that describe the position and type of frame within the spectrogram. The data set contains results of intermediate processing steps that enable the research or teaching community to create new data sets for specific requirements or to provide new interesting examination examples.

An increase in popularity of wireless networks, mainly in industrial automation and manufacturing has escalated the need of reliable wireless networks. One subtle way of achieving reliability is early diagnosis of transmission failures and taking preventive measures against it. However, it is difficult to achieve as these failures mainly arise by challenging signal propagation conditions and interference from co-existing networks, which is hard to diagnose. This leads to Quality-of-service degradation and faulty applications which may result in system breakdown and financial distress. In this paper, we propose a novel Dynamic Time Warping (DTW) based method known as Variable Adaptive DTW (VADTW) for synchronizing spectral and protocol domains to obtain precise and complete information about collisions and co-existing links causing interference and further utilize it for troubleshooting. VADTW divides the spectral and protocol sequences in adaptive time bins and calculates variable window limits for each frame to perform synchronization. To prove the efficiency of the proposed approach, we tested it in a Wi-Fi network as a proof-of-concept. The experiments are performed with real-time data traffic captured in different scenarios. VADTW successfully synchronizes sequences even with a frame loss ratio of 50% in both cases. We also perform comparative analysis of VADTW with other DTW based approaches on the basis of performance, computational cost and execution time.

The growing popularity of cellular networks among users, primarily due to affordable prices and high speeds, has escalated the need for strategic capacity planning to ensure a seamless end-user experience and profitable returns on network investments. Traditional capacity planning methods rely on static analysis of network parameters with the aim of minimizing the CAPEX and the OPEX. However, to address the evolving dynamics of cellular networks, this paper advocates for a data-driven approach that considers user behavioral analysis in the planning process to make it proactive and adaptive. We introduce a Hierarchical Feature-based Time Series Clustering (HFTSC) approach that organizes clustering in a multi-level tree structure. Each level addresses a specific aspect of time series data using focused features, enabling explainable clustering. The proposed approach assigns labels to clusters based on the time series properties targeted at each level, generating annotated clusters while applying unsupervised clustering methods. To evaluate the effectiveness of HFTSC, we conduct a comprehensive case study using real-world data from thousands of network elements. Our evaluation examines the identified clusters from analytical and geographical perspectives, focusing on supporting network planners in data-informed decision-making and analysis. Finally, we perform an extensive comparison with several baseline methods to reflect the practical advantages of our approach in capacity planning and optimization.

With the increasing proliferation of wireless devices and Internet-of- Things (IoT) applications in various fields, such as patient monitoring, vehicle-to everything (V2X) communication and industrial automation, there is a growing significance in developing robust methods and tools for evaluating and predicting link quality, monitoring information flow, as well as conducting failure analysis. This is particularly important in safety-critical industrial IoT (IIoT) environments such as smart factories, where challenging signal propagation conditions and interference from coexisting wireless technologies can severely impact network performance and application reliability. This contribution provides a comprehensive analysis of coexistence issues in industrial IIoT networks and highlights the complexities and challenges associated with performing failure analysis on a large scale. The necessity of using data-driven methods in the development of efficient and user-friendly failure analysis systems is discussed and the challenges regarding required datasets are highlighted.