Ulf Wetzker’s research while affiliated with Fraunhofer Institute for Integrated Circuits IIS, Division Engineering of Adaptive Systems EAS and other places

Time series clustering is an unsupervised method of organizing homogeneous time series in groups based on certain similarity criteria. As a result, it can be an essential step in Exploratory Data Analysis (EDA), especially for complex time series data. This applies specifically to industrial datasets for applications like predictive maintenance, energy consumption, etc., due to the heterogeneity and peculiarity of collected data sets. Understanding the underlying trends and patterns in such datasets could help strategize advanced analysis methods such as forecasting, regression testing, etc. In this paper, we present a case study on a real-world energy consumption dataset of 4G cells, where we perform a pre-segmented clustering based EDA to uncover hidden insights about the data. The empirical study demonstrates that performing pre-segmented clustering based EDA enhances data interpretation by revealing prevalent and infrequent patterns, empowering users to refine analyses such as prediction more precisely, leading to performance improvement.

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.

Multivariate time series (MTS) are captured in a great variety of real-world applications. However, analysing and modeling the data for classification and forecasting purposes can become very challenging if values are missing in the data set. The need for imputation methods, to fill the gaps in MTS, is well known. Thus, a great variaty of algorithms for solving this task has been proposed in the literature. However, the research community is constantly working on the development of advanced algorithms, that fulfill the special requirements of multidimensional temporal data, since most of the existing imputation methods treat MTS as ordinary structured data and fail to model the temporal relationships within and between sequences of observations. The main emphasis of MTS imputation research is currently put on deep learning (DL) models, especially those making use of generative adversarial networks (GANs). In our survey, we present our categorization of imputation algorithms, especially of GAN-models. We included eighteen different GAN-models designed for the MTS imputation task, which we introduce in detail. We provide a comparison of the models including their performance regarding MTS imputation, based on our findings in the literature. The following points can be considered the most important findings from our survey: The research on GAN-based imputation models for MTS has gained momentum in the last years across different domains, therewhile showing the effectiveness of these methods. The latest trend in the research area is the incorporation of attention mechanisms into the algorithms. Nevertheless, there are open research challenges, e.g. the transferability of models across data sets from different domains.

p> The increase in popularity of wireless networks in industrial, embedded, medical and public sectors has made them an appealing attack surface for attackers who exploit the vulnerabilities in network protocols to launch attacks such as Evil Twin, Man-in-the-middle, sniffing, etc ., which may result in economic and non-economic losses. To protect wireless networks against such attacks, IEEE 802.11 keep updating the protocol standards with new and more secure versions. There has always been a direct correlation between attacks and the improvement of protocol standards. As the sophistication of attacks increases, protocol standards tend to move towards higher security, resulting in a significant rise in both latency and computational overhead, and severe degradation in the performance of low-latency applications such as Industrial Internet of Things ( IIoT ), automotive, robotics, etc . In this paper, we make the first attempt to highlight the importance of both latency and security in wireless networks from implementation and performance perspective. We make a review of existing IEEE 802.11 protocols in terms of security offered and overhead incurred to substantiate the fact that there is a need of a protocol which in addition to providing optimum security against attacks also maintains the latency and overhead. We also propose a secure and low-latency protocol known as Secure Authentication Protocol (SAP) which operates in two phases - registration and authentication, where the first phase is a one time process implemented using asymmetric cryptography and the second phase is implemented using symmetric cryptography. The protocol is structured in a way that it maintains the original structure of IEEE 802.11 protocols and performs both phases using fewer messages than existing protocols. By simulating the protocol using well-established OMNeT ++ simulator, we proved that the proposed protocol incurs a low computation overhead, making it ideal for low-latency applications. We extensively verified the security properties of the proposed protocol using formal verification through widely-accepted Scyther tool. Finally, we perform a comparative analysis of SAP with existing IEEE 802.11 wireless network protocols to highlight the improvement.</p

p> The increase in popularity of wireless networks in industrial, embedded, medical and public sectors has made them an appealing attack surface for attackers who exploit the vulnerabilities in network protocols to launch attacks such as Evil Twin, Man-in-the-middle, sniffing, etc ., which may result in economic and non-economic losses. To protect wireless networks against such attacks, IEEE 802.11 keep updating the protocol standards with new and more secure versions. There has always been a direct correlation between attacks and the improvement of protocol standards. As the sophistication of attacks increases, protocol standards tend to move towards higher security, resulting in a significant rise in both latency and computational overhead, and severe degradation in the performance of low-latency applications such as Industrial Internet of Things ( IIoT ), automotive, robotics, etc . In this paper, we make the first attempt to highlight the importance of both latency and security in wireless networks from implementation and performance perspective. We make a review of existing IEEE 802.11 protocols in terms of security offered and overhead incurred to substantiate the fact that there is a need of a protocol which in addition to providing optimum security against attacks also maintains the latency and overhead. We also propose a secure and low-latency protocol known as Secure Authentication Protocol (SAP) which operates in two phases - registration and authentication, where the first phase is a one time process implemented using asymmetric cryptography and the second phase is implemented using symmetric cryptography. The protocol is structured in a way that it maintains the original structure of IEEE 802.11 protocols and performs both phases using fewer messages than existing protocols. By simulating the protocol using well-established OMNeT ++ simulator, we proved that the proposed protocol incurs a low computation overhead, making it ideal for low-latency applications. We extensively verified the security properties of the proposed protocol using formal verification through widely-accepted Scyther tool. Finally, we perform a comparative analysis of SAP with existing IEEE 802.11 wireless network protocols to highlight the improvement.</p

The increase in popularity of wireless networks in industrial, embedded, medical and public sectors has made them an appealing attack surface for attackers who exploit the vulnerabilities in network protocols to launch attacks such as Evil Twin, Man-in-the-middle, sniffing, etc., which may result in economic and non-economic losses. To protect wireless networks against such attacks, IEEE 802.11 keep updating the protocol standards with new and more secure versions. There has always been a direct correlation between attacks and the improvement of protocol standards. As the sophistication of attacks increases, protocol standards tend to move towards higher security, resulting in a significant rise in both latency and computational overhead, and severe degradation in the performance of low-latency applications such as Industrial Internet of Things (IIoT), automotive, robotics, etc. In this paper, we make an attempt to highlight the importance of both latency and security in wireless networks from implementation and performance perspective. We make a review of existing IEEE 802.11 protocols in terms of security offered and overhead incurred to substantiate the fact that there is a need of a protocol which in addition to providing optimum security against attacks also maintains the latency and overhead. We also propose a secure and low-latency protocol known as Secure Authentication Protocol (SAP) which operates in two phases - registration and authentication, where the first phase is a one time process implemented using asymmetric cryptography and the second phase is implemented using symmetric cryptography. The protocol is structured in a way that it maintains the original structure of IEEE 802.11 protocols and performs both phases using fewer messages than existing protocols. By simulating the protocol using well-established OMNeT++ simulator, we proved that the proposed protocol incurs a low computation overhead, making it ideal for low-latency applications. We extensively verified the security properties of the proposed protocol using formal verification through widely-accepted Scyther tool. Finally, we perform a comparative analysis of SAP with existing IEEE 802.11 wireless network protocols to highlight the improvement.

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.

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.

p>Multivariate time series (MTS) are captured in a great variety of real-world applications. However, analysing and modelling the data for classification and forecasting purposes can become very challenging if values are missing in the data set. The need for imputation methods, to fill the gaps in MTS, is well known. Thus, a great variaty of algorithms for solving this task has been proposed in the literature. However, research community is constantly working on the development of advanced algorithms, that fulfill the special requirements of multidimensional temporal data, since most of the existing imputation methods treat MTS as ordinary structured data and fail to model the temporal relationships within and between sequences of observations. The main emphasis of MTS imputation research is currently put on deep learning (DL) models, especially models making use of generative adversarial networks (GANs). In our survey, we present a general categorization of imputation algorithms and introduce groups of hybrid GAN-models used for the MTS imputation task, which we investigate and discuss in detail. A quantitative comparison of the hybrid GANs’ performance regarding MTS imputation is presented based on our findings in the literature. </p