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Das Monitoring von Industrieanlagen stellt in der Wirtschaft sicher, dass hoch-automatisierte Prozesse reibungslos ablaufen können. Meistens steht hier das Monitoring der Anlagen selbst im Mittelpunkt, die Kommunikationsleitungen für den Datenaustausch auf Ethernet-Basis (z.B. Profinet) sind gegenwärtig noch nicht Teil einer kontinuierlichen Überwachung. Zwar werden auch hier die physischen Verbindungen überprüft, jedoch geschieht häufig dies nur zum Zeitpunkt der Inbetriebnahme, wenn die Anlage noch nicht in das Gesamtsystem integriert ist oder während eines Wartungszyklus, wenn die Maschine für die Dauer der Wartung aus dem Betriebsablauf genommen wird. Dies führt dazu, dass insbesondere heute, wo vor allem Ethernet zunehmend als Basis für die industrielle Kommunikation herangezogen wird, Maschinenausfälle aufgrund fehlender Kabelüberwachung immer wahrscheinlicher werden. Um dem entgegenwirken zu können, wurde im Projekt Ko2SiBus ein neues Messverfahren konzipiert, implementiert und validiert, das kostengünstig in neue oder bestehende Systeme integriert werden kann. Um die Tauglichkeit zu zeigen, wurden die Projektergebnisse in Prototypen und Demonstratoren implementiert, die sowohl als Stand-Alone aber auch als Integrationslösungen dienen können.
Die neueste Generation von programmierbaren Logikbausteinen verfügt neben den konfigurierbaren Logikzellen über einen oder mehrere leistungsfähige Mikroprozessoren. In dieser Arbeit wird gezeigt, wie ein bestehendes Zwei-Chip-System auf einen Xilinx Zynq 7000 mit zwei ARM A9-Cores migriert wird. Bei dem System handelt es sich um das „GPS-gestützte Kreisel-system ADMA“ des Unternehmens GeneSys. Die neue Lösung verbessert den Datenaustausch zwischen dem ersten Mikroprozessor zur digitalen Signalverarbeitung und dem zweiten Prozessor zur Ablaufsteuerung durch ein Shared Memory. Für die schnelle und echtzeitfähige Datenübertragung werden zahlreiche hochbitratige Schnittstellengenutzt.
The authentication method of electronic devices, based on individual forms of correlograms of their internal electric noises, is well-known. Specific physical differences in the components – for example, caused by variations in production quality – cause specific electrical signals, i.e. electric noise, in the electronic device. It is possible to obtain this information and to identify the specific differences of the individual devices using an embedded analog-to-digital converter (ADC). These investigations confirm the possibility to identify and authenticate electronic devices using bit templates, calculated from the sequence of values of the normalized autocorrelation function of noise. Experiments have been performed using personal computers. The probability of correct identification and authentication increases with increasing noise recording duration. As a result of these experiments, an accuracy of 98.1% was achieved for a 1 second-long registration of EM for a set of investigated computers.
The paper describes the hardware and software architecture of the developed multi MEMS sensor prototype module, consisting of ARM Cortex M4 STM32F446 microcontroller unit, five 9-axis inertial measurement units MPU9255 (3D accelerometer, 3D gyroscope, 3D magnetometer and temperature sensor) and a BMP280 barometer. The module is also equipped with WiFi wireless interface (Espressif ESP8266 chip). The module is constructed in the form of a truncated pyramid. Inertial sensors are mounted on a special basement at different angles to each other to eliminate hardware sensors drifts and to provide the capability for self-calibration. The module fuses information obtained from all types of inertial sensors (acceleration, rotation rate, magnetic field and air pressure) in order to calculate orientation and trajectory. It might be used as an Inertial Measurement Unit, Vertical Reference Unit or Attitude and Heading Reference System.
Legacy industrial communication protocols are proved robust and functional. During the last decades, the industry has invented completely new or advanced versions of the legacy communication solutions. However, even with the high adoption rate of these new solutions, still the majority industry applications run on legacy, mostly fieldbus related technologies. Profibus is one of those technologies that still keep on growing in the market, albeit a slow in market growth in recent years. A retrofit technology that would enable these technologies to connect to the Internet of Things, utilize the ever growing potential of data analysis, predictive maintenance or cloud-based application, while at the same time not changing a running system is fundamental.
The IEEE 1588 precision time protocol (PTP) is a time synchronization protocol with sub-microsecond precision primarily designed for wired networks. In this letter, we propose wireless precision time protocol (WPTP) as an extension to PTP for multi-hop wireless networks. WPTP significantly reduces the convergence time and the number of packets required for synchronization without compromising on the synchronization accuracy.
Institute of Reliable Embedded Systems and Communication Electronics, Offenburg University of Applied Sciences, Germany has developed an automated testing environment, Automated Physical TestBeds (APTB), for analyzing the performance of wireless systems and its supporting protocols. Wireless physical networking nodes can connect to this APTB and the antenna output of this attaches with the RF waveguides. To model the RF environment this RF waveguides then establish wired connection among RF elements like splitters, attenuators and switches. In such kind of set up it’s well possible to vary the path characteristics by altering the attenuators and switches. The major advantage of using APTB is the possibility of isolated, well controlled, repeatable test environment in various conditions to run statistical analysis and even to execute regression tests. This paper provides an overview of the design and implementation of APTB, demonstrates its ability to automate test cases, and its efficiency.
Climate change and resultant scarcity of water are becoming major challenges for countries around the world. With the advent of Wireless Sensor Networks (WSN) in the last decade and a relatively new concept of Internet of Things (IoT), embedded systems developers are now working on designing control and automation systems that are lower in cost and more sustainable than the existing telemetry systems for monitoring. The Indus river basin in Pakistan has one of the world's largest irrigation systems and it is extremely challenging to design a low-cost embedded system for monitoring and control of waterways that can last for decades. In this paper, we present a hardware design and performance evaluation of a smart water metering solution that is IEEE 802.15.4-compliant. The results show that our hardware design is as powerful as the reference design, but allows for additional flexibility both in hardware and in firmware. The indigenously designed solution has a power added efficiency (PAE) of 24.7% that is expected to last for 351 and 814 days for nodes with and without a power amplifier (PA). Similarly, the results show that a broadband communication (434 MHz) over more than 3km can be supported, which is an important stepping stone for designing a complete coverage solution of large-scale waterways.
Due to climate change and scarcity of water reservoirs, monitoring and control of irrigation systems is now becoming a major focal area for researchers in Cyber-Physical Systems (CPS). Wireless Sensor Networks (WSNs) are rapidly finding their way in the field of irrigation and play the key role as data gathering technology in the domain of IoT and CPS. They are efficient for reliable monitoring, giving farmers an edge to take precautionary measures. However, designing an energy-efficient WSN system requires a cross-layer effort and energy-aware routing protocols play a vital role in the overall energy optimization of a WSN. In this paper, we propose a new hierarchical routing protocol suitable for large area environmental monitoring such as large-scale irrigation network existing in the Punjab province of Pakistan. The proposed protocol resolves the issues faced by traditional multi-hop routing protocols such as LEACH, M-LEACH and I-LEACH, and enhances the lifespan of each WSN node that results in an increased lifespan of the whole network. We used the open-source NS3 simulator for simulation purposes and results indicate that our proposed modifications result in an average 27.8% increase in lifespan of the overall WSN when compared to the existing protocols.
The Internet of Things (IoT), ubiquitous computing and ubiquitous connectivity, Cyber Physical Systems (CPS), ambient intelligence, Machine-to-Machine communication (M2M) or Car-to-Car (C2C)-communication, smart metering, smart grid, telematics, telecare, telehealth – there are many buzzwords around current developments related to the Internet.
This contribution gives an overview on such IoT-applications, as they are already used today to improve the availability of information, increase efficiency, push system limits and extend the value chain. At a closer look, the economic and technical development can be separated into different phases. It is interesting that we are currently at the threshold to a new phase, with decentralized and cooperative communication and control nodes as cornerstones. Thus, embedded systems and their connectivity are in the middle of the scene.
This recent development is described along with some example projects from the author’s team which are used in industrial automation, energy supply and distribution (home automation and smart metering), traffic engineering (cooperative driver assistance systems), and in telehealth and telecare.
In recent times, 5G has found applications in several public as well as private networks. There is a growing need to make it compatible with diverse services without compromising security. Current security options for authenticating devices into a home network are 5G Authentication and Key Agreement (5G-AKA) and Extensible Authentication Protocol (EAP)-AKA'. However, for specific use cases such as private networks, more customizable and convenient authentication mechanisms are required. The current mobile networks use authentication based only on SIM cards, but as 5G is being applied in fields like IIoT and automation, even in Non-Public-Networks (NPNs), there is a need for a simpler method of authentication. Certificate-based authentication is one such mechanism that is passwordless and works solely on the information present in the digital certificate that the user holds. The paper suggests an authentication mechanism that performs certificate-based mutual authentication between the UE and the Home network. The proposed concept identifies both the user and network with digital certificates and intends to carry out primary authentication with the help of it. In this work we conduct a study on presently available authentication protocols for 5G networks, both theoretically and experimentally in hardware as well as virtual environments. On the basis of the analysis a series of proposed steps for certificate primary authentication are presented.
As cyber-attacks and functional safety requirements increase in Operational Technology (OT), implementing security measures becomes crucial. The IEC/IEEE 60802 draft standard addresses the security convergence in Time-Sensitive Networks (TSN) for industrial automation.We present the standard’s security architecture and its goals to establish end-to-end security with resource access authorization in OT systems. We compare the standard to our abstract technology-independent model for the management of cryptographic credentials during the lifecycles of OT systems. Additionally, we implemented the processes, mechanisms, and protocols needed for IEC/IEEE 60802 and extended the architecture with public key infrastructure (PKI) functionalities to support complete security management processes.
In this work, we consider a duty-cycled wireless sensor network with the assumption that the on/off schedules are uncoordinated. In such networks, as all nodes may not be awake during the transmission of time synchronization messages, nodes will require to re-transmit the synchronization messages. Ideally a node should re-transmit for the maximum sleep duration to ensure that all nodes are synchronized. However, such a proposition will immensely increase the energy consumption of the nodes. Such a situation demands that there is an upper bound of the number of retransmissions. We refer to the time a node spends in re-transmission of the control message as broadcast duration. We ask the question, what should be the broadcast duration to ensure that a certain percentage of the available nodes are synchronized. The problem to estimate the broadcast duration is formulated so as to capture the probability threshold of the nodes being synchronized. Results show the proposed analytical model can predict the broadcast duration with a given lower error margin under real world conditions, thus demonstrating the efficiency of our solution.
To demonstrate how deep learning can be applied to industrial applications with limited training data, deep learning methodologies are used in three different applications. In this paper, we perform unsupervised deep learning utilizing variational autoencoders and demonstrate that federated learning is a communication efficient concept for machine learning that protects data privacy. As an example, variational autoencoders are utilized to cluster and visualize data from a microelectromechanical systems foundry. Federated learning is used in a predictive maintenance scenario using the C-MAPSS dataset.
The application of leaky feeder (radiating) cables is a common solution for the implementation of reliable radio communication in huge industrial buildings, tunnels and mining environment. This paper explores the possibilities of leaky feeders for 1D and 2D localization in wireless systems based on time of flight chirp spread spectrum technologies. The main focus of this paper is to present and analyse the results of time of flight and received signal strength measurements with leaky feeders in indoor and outdoor conditions. The authors carried out experiments to compare ranging accuracy and radio coverage area for a point-like monopole antenna and for a leaky feeder acting as a distributed antenna. In all experiments RealTrac equipment based on nanoLOC radio standard was used. The estimation of the most probable path of a chirp signal going through a leaky feeder was calculated using the ray tracing approach. The typical non-line-of-sight errors profiles are presented. The results show the possibility to use radiating cables in real time location technologies based on time-of-flight method.
Schlussbericht VanAssist
(2021)
Deep learning approaches are becoming increasingly important for the estimation of the Remaining Useful Life (RUL) of mechanical elements such as bearings. This paper proposes and evaluates a novel transfer learning-based approach for RUL estimations of different bearing types with small datasets and low sampling rates. The approach is based on an intermediate domain that abstracts features of the bearings based on their fault frequencies. The features are processed by convolutional layers. Finally, the RUL estimation is performed using a Long Short-Term Memory (LSTM) network. The transfer learning relies on a fixed-feature extraction. This novel deep learning approach successfully uses data of a low-frequency range, which is a precondition to use low-cost sensors. It is validated against the IEEE PHM 2012 Data Challenge, where it outperforms the winning approach. The results show its suitability for low-frequency sensor data and for efficient and effective transfer learning between different bearing types.
In recent years, predictive maintenance tasks, especially for bearings, have become increasingly important. Solutions for these use cases concentrate on the classification of faults and the estimation of the Remaining Useful Life (RUL). As of today, these solutions suffer from a lack of training samples. In addition, these solutions often require high-frequency accelerometers, incurring significant costs. To overcome these challenges, this research proposes a combined classification and RUL estimation solution based on a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. This solution relies on a hybrid feature extraction approach, making it especially appropriate for low-cost accelerometers with low sampling frequencies. In addition, it uses transfer learning to be suitable for applications with only a few training samples.
The often-occurring short-term orders of manufactured products require a high machine availability. This requirement increases the importance of predictive maintenance solutions for bearings used in machines. There are, among others, hybrid solutions that rely on a physical model. For their usage, knowing the different degradation stages of bearings is essential. This research analyzes the underlying failure mechanisms of these stages theoretically and in a practical example of the well-known FEMTO dataset used for the IEEE PHM 2012 Data Challenge to provide this knowledge. In addition, it shows for which use cases the usage of low-frequency accelerometers is sufficient. The analysis provides that the degradation stages toward the end of the bearing life can also be detected with low-frequency accelerometers. Further, the importance of high-frequency accelerometers to detect bearing faults in early degradation stages is pointed out. These aspects have not been paid attention to by industry and research until now, despite providing a considerable cost-saving potential.
In the last decade, deep learning models for condition monitoring of mechanical systems increasingly gained importance. Most of the previous works use data of the same domain (e.g., bearing type) or of a large amount of (labeled) samples. This approach is not valid for many real-world scenarios from industrial use-cases where only a small amount of data, often unlabeled, is available.
In this paper, we propose, evaluate, and compare a novel technique based on an intermediate domain, which creates a new representation of the features in the data and abstracts the defects of rotating elements such as bearings. The results based on an intermediate domain related to characteristic frequencies show an improved accuracy of up to 32 % on small labeled datasets compared to the current state-of-the-art in the time-frequency domain.
Furthermore, a Convolutional Neural Network (CNN) architecture is proposed for transfer learning. We also propose and evaluate a new approach for transfer learning, which we call Layered Maximum Mean Discrepancy (LMMD). This approach is based on the Maximum Mean Discrepancy (MMD) but extends it by considering the special characteristics of the proposed intermediate domain. The presented approach outperforms the traditional combination of Hilbert–Huang Transform (HHT) and S-Transform with MMD on all datasets for unsupervised as well as for semi-supervised learning. In most of our test cases, it also outperforms other state-of-the-art techniques.
This approach is capable of using different types of bearings in the source and target domain under a wide variation of the rotation speed.
It is important to minimize the unscheduled downtime of machines caused by outages of machine components in highly automated production lines. Considering machine tools such as, grinding machines, the bearing inside of spindles is one of the most critical components. In the last decade, research has increasingly focused on fault detection of bearings. In addition, the rise of machine learning concepts has also intensified interest in this area. However, up to date, there is no single one-fits-all solution for predictive maintenance of bearings. Most research so far has only looked at individual bearing types at a time.
This paper gives an overview of the most important approaches for bearing-fault analysis in grinding machines. There are two main parts of the analysis presented in this paper. The first part presents the classification of bearing faults, which includes the detection of unhealthy conditions, the position of the error (e.g. at the inner or at the outer ring of the bearing) and the severity, which detects the size of the fault. The second part presents the prediction of remaining useful life, which is important for estimating the productive use of a component before a potential failure, optimizing the replacement costs and minimizing downtime.
The last decades have seen the evolution of industrial production into more sophisticated processes. The development of specialized, high-end machines has increased the importance of predictive maintenance of mechanical systems to produce high-quality goods and avoid machine breakdowns. Predictive maintenance has two main objectives: to classify the current status of a machine component and to predict the maintenance interval by estimating its remaining useful life (RUL). Nowadays, both objectives are covered by machine learning and deep learning approaches and require large training datasets that are often not available. One possible solution may be transfer learning, where the knowledge of a larger dataset is transferred to a smaller one. This thesis is primarily concerned with transfer learning for predictive maintenance for fault classification and RUL estimation. The first part presents the state-of-the-art machine learning techniques with a focus on techniques applicable to predictive maintenance tasks (Chapter 2). This is followed by a presentation of the machine tool background and current research that applies the previously explained machine learning techniques to predictive maintenance tasks (Chapter 3). One novelty of this thesis is that it introduces a new intermediate domain that represents data by focusing on the relevant information to allow the data to be used on different domains without losing relevant information (Chapter 4). The proposed solution is optimized for rotating elements. Therefore, the presented intermediate domain creates different layers by focusing on the fault frequencies of the rotating elements. Another novelty of this thesis is its semi and unsupervised transfer learning-based fault classification approach for different component types under different process conditions (Chapter 5). It is based on the intermediate domain utilized by a convolutional neural network (CNN). In addition, a novel unsupervised transfer learning loss function is presented based on the maximum mean discrepancy (MMD), one of the state-of-the-art algorithms. It extends the MMD by considering the intermediate domain layers; therefore, it is called layered maximum mean discrepancy (LMMD). Another novelty is an RUL estimation transfer learning approach for different component types based on the data of accelerometers with low sampling rates (Chapter 6). It applies the feature extraction concepts of the classification approach: the presented intermediate domain and the convolutional layers. The features are then used as input for a long short-term memory (LSTM) network. The transfer learning is based on fixed feature extraction, where the trained convolutional layers are taken over. Only the LSTM network has to be trained again. The intermediate domain supports this transfer learning type, as it should be similar for different component types. In addition, it enables the practical usage of accelerometers with low sampling rates during transfer learning, which is an absolute novelty. All presented novelties are validated in detailed case studies using the example of bearings (Chapter 7). In doing so, their superiority over state-of-the-art approaches is demonstrated.
Embedded Analog Physical Unclonable Function System to Extract Reliable and Unique Security Keys
(2020)
Internet of Things (IoT) enabled devices have become more and more pervasive in our everyday lives. Examples include wearables transmitting and processing personal data and smart labels interacting with customers. Due to the sensitive data involved, these devices need to be protected against attackers. In this context, hardware-based security primitives such as Physical Unclonable Functions (PUFs) provide a powerful solution to secure interconnected devices. The main benefit of PUFs, in combination with traditional cryptographic methods, is that security keys are derived from the random intrinsic variations of the underlying core circuit. In this work, we present a holistic analog-based PUF evaluation platform, enabling direct access to a scalable design that can be customized to fit the application requirements in terms of the number of required keys and bit width. The proposed platform covers the full software and hardware implementations and allows for tracing the PUF response generation from the digital level back to the internal analog voltages that are directly involved in the response generation procedure. Our analysis is based on 30 fabricated PUF cores that we evaluated in terms of PUF security metrics and bit errors for various temperatures and biases. With an average reliability of 99.20% and a uniqueness of 48.84%, the proposed system shows values close to ideal.
Physically Unclonable Functions (PUFs) are hardware-based security primitives, which allow for inherent device fingerprinting. Therefore, intrinsic variation of imperfect manufactured systems is exploited to generate device-specific, unique identifiers. With printed electronics (PE) joining the internet of things (IoT), hardware-based security for novel PE-based systems is of increasing importance. Furthermore, PE offers the possibility for split-manufacturing, which mitigates the risk of PUF response readout by third parties, before commissioning. In this paper, we investigate a printed PUF core as intrinsic variation source for the generation of unique identifiers from a crossbar architecture. The printed crossbar PUF is verified by simulation of a 8×8-cells crossbar, which can be utilized to generate 32-bit wide identifiers. Further focus is on limiting factors regarding printed devices, such as increased parasitics, due to novel materials and required control logic specifications. The simulation results highlight, that the printed crossbar PUF is capable to generate close-to-ideal unique identifiers at the investigated feature size. As proof of concept a 2×2-cells printed crossbar PUF core is fabricated and electrically characterized.
Hybrid low-voltage physical unclonable function based on inkjet-printed metal-oxide transistors
(2020)
Modern society is striving for digital connectivity that demands information security. As an emerging technology, printed electronics is a key enabler for novel device types with free form factors, customizability, and the potential for large-area fabrication while being seamlessly integrated into our everyday environment. At present, information security is mainly based on software algorithms that use pseudo random numbers. In this regard, hardware-intrinsic security primitives, such as physical unclonable functions, are very promising to provide inherent security features comparable to biometrical data. Device-specific, random intrinsic variations are exploited to generate unique secure identifiers. Here, we introduce a hybrid physical unclonable function, combining silicon and printed electronics technologies, based on metal oxide thin film devices. Our system exploits the inherent randomness of printed materials due to surface roughness, film morphology and the resulting electrical characteristics. The security primitive provides high intrinsic variation, is non-volatile, scalable and exhibits nearly ideal uniqueness.
Printed electronics can add value to existing products by providing new smart functionalities, such as sensing elements over large-areas on flexible or non-conformal surfaces. Here we present a hardware concept and prototype for a thinned ASIC integrated with an inkjet-printed temperature sensor alongside in-built additional security and unique identification features. The hybrid system exploits the advantages of inkjet-printable platinum-based sensors, physically unclonable function circuits and a fluorescent particle-based coating as a tamper protection layer.
Eine kontinuierliche Überwachung von Ethernet-Leitungne beugt Maschinenausfällen in der Industrie vor. Aktuell fehlen jedoch geiegnete Methoden, um diese Überwachung flächendeckend durchzuführen. Im Projekt Ko²SiBus wurde deshalb ein kostengünstiges Verfahren zur kontinuierlichen Überwachung von Ethernet-Leitungen entwickelt.
6LoWPAN (IPv6 over Low Power Wireless Personal Area Networks) is gaining more and more attraction for the seamless connectivity of embedded devices for the Internet of Things. It can be observed that most of the available solutions are following an open source approach, which significantly leads to a fast development of technologies and of markets. Although the currently available implementations are in a pretty good shape, all of them come with some significant drawbacks. It was therefore decided to start the development of an own implementation, which takes the advantages from the existing solutions, but tries to avoid the drawbacks. This paper discussed the reasoning behind this decision, describes the implementation and its characteristics, as well as the testing results. The given implementation is available as open-source project under [15].
For the past few years Low Power Wide Area Networks (LPWAN) have emerged as key technologies for the connectivity of many applications in the Internet of Things (IoT) combining low-data rates with strict cost and energy restrictions. Especially LoRa/LoRaWAN enjoys a high visibility on today’s markets, because of its good performance and its open community. Originally LoRa was designed for operation within the Sub-GHz ISM bands for Industrial, Scientific and Medical applications. However, at the end of 2018, a LoRa-based solution in the 2.4GHz ISM-band was presented promising higher bandwidths and higher data rates. Furthermore, it overcomes the limited duty-cycle prescribed by the regulations in the ISM-bands and therefore also opens doors to many novel application fields. Also, due to higher bandwidths and shorter transmission times, the use of alternative MAC layer protocols becomes very interesting, i.e. for TDMA based-approaches. Within this paper, we propose a system architecture with 2.4GHz LoRa components combining two aspects. On the one hand, we present a design and an implementation of a 2.4GHz based LoRaWAN solution that can be seamlessly integrated into existing LoRaWAN back-hauls. On the other hand, we describe deterministic setup using a Time Slotted Channel Hopping (TSCH) approach as defined in the IEEE802.15.4-2015 standard for industrial applications. Finally, measurements show the performance of the system.
The identification of vulnerabilities is an important element in the software development life cycle to ensure the security of software. While vulnerability identification based on the source code is a well studied field, the identification of vulnerabilities on basis of a binary executable without the corresponding source code is more challenging. Recent research [1] has shown how such detection can generally be enabled by deep learning methods, but appears to be very limited regarding the overall amount of detected vulnerabilities. We analyse to what extent we could cover the identification of a larger variety of vulnerabilities. Therefore, a supervised deep learning approach using recurrent neural networks for the application of vulnerability detection based on binary executables is used. The underlying basis is a dataset with 50,651 samples of vulnerable code in the form of a standardised LLVM Intermediate Representation. Te vectorised features of a Word2Vec model are used to train different variations of three basic architectures of recurrent neural networks (GRU, LSTM, SRNN). A binary classification was established for detecting the presence of an arbitrary vulnerability, and a multi-class model was trained for the identification of the exact vulnerability, which achieved an out-of-sample accuracy of 88% and 77%, respectively. Differences in the detection of different vulnerabilities were also observed, with non-vulnerable samples being detected with a particularly high precision of over 98%. Thus, our proposed technical approach and methodology enables an accurate detection of 23 (compared to 4 [1]) vulnerabilities.
As the Industry 4.0 is evolving, the previously separated Operational Technology (OT) and Information Technology (IT) is converging. Connecting devices in the industrial setting to the Internet exposes these systems to a broader spectrum of cyber-attacks. The reason is that since OT does not have much security measures as much as IT, it is more vulnerable from the attacker's perspective. Another factor contributing to the vulnerability of OT is that, when it comes to cybersecurity, industries have focused on protecting information technology and less prioritizing the control systems. The consequences of a security breach in an OT system can be more adverse as it can lead to physical damage, industrial accidents and physical harm to human beings. Hence, for the OT networks, certificate-based authentication is implemented. This involves stages of managing credentials in their communication endpoints. In the previous works of ivESK, a solution was developed for managing credentials. This involves a CANopen-based physical demonstrator where the certificate management processes were developed. The extended feature set involving certificate management will be based on the existing solution. The thesis aims to significantly improve such a solution by addressing two key areas that is enhancing functionality and optimizing real-time performance. Regarding the first goal, firstly, an analysis of the existing feature set shall be carried out, where the correct functionality shall be guaranteed. The limitations from the previously implemented system will be addressed and to make sure it can be applied to real world scenarios, it will be implemented and tested in the physical demonstrator. This will lay a concrete foundation that these certificate management processes can be used in the industries in large-scale networks. Implementation of features like revocation mechanism for certificates, automated renewal of the credentials and authorization attribute checks for the certificate management will be implemented. Regarding the second goal, the impact of credential management processes on the ongoing CANopen real-time traffic shall be a studied. Since in real life scenarios, mission-critical applications like Industrial control systems, medical devices, and transportation networks rely on real-time communication for reliable operation, delays or disruptions caused by credential management processes can have severe consequences. Optimizing these processes is crucial for maintaining system integrity and safety. The effect to minimize the disturbance of the credential management processes on the normal operation of the CANopen network shall be characterized. This shall comprise testing real-time parameters in the network such as CPU load, network load and average delay. Results obtained from each of these tests will be studied.
In recent years, the topic of embedded machine learning has become very popular in AI research. With the help of various compression techniques such as pruning, quantization and others compression techniques, it became possible to run neural networks on embedded devices. These techniques have opened up a whole new application area for machine learning. They range from smart products such as voice assistants to smart sensors that are needed in robotics. Despite the achievements in embedded machine learning, efficient algorithms for training neural networks in constrained domains are still lacking. Training on embedded devices will open up further fields of applications. Efficient training algorithms would enable federated learning on embedded devices, in which the data remains where it was collected, or retraining of neural networks in different domains. In this paper, we summarize techniques that make training on embedded devices possible. We first describe the need and requirements for such algorithms. Then we examine existing techniques that address training in resource-constrained environments as well as techniques that are also suitable for training on embedded devices, such as incremental learning. At the end, we also discuss which problems and open questions still need to be solved in these areas.
Training deep neural networks using backpropagation is very memory and computationally intensive. This makes it difficult to run on-device learning or fine-tune neural networks on tiny, embedded devices such as low-power micro-controller units (MCUs). Sparse backpropagation algorithms try to reduce the computational load of on-device learning by training only a subset of the weights and biases. Existing approaches use a static number of weights to train. A poor choice of this so-called backpropagation ratio limits either the computational gain or can lead to severe accuracy losses. In this paper we present TinyProp, the first sparse backpropagation method that dynamically adapts the back-propagation ratio during on-device training for each training step. TinyProp induces a small calculation overhead to sort the elements of the gradient, which does not significantly impact the computational gains. TinyProp works particularly well on fine-tuning trained networks on MCUs, which is a typical use case for embedded applications. For typical datasets from three datasets MNIST, DCASE2020 and CIFAR10, we are 5 times faster compared to non-sparse training with an accuracy loss of on average 1%. On average, TinyProp is 2.9 times faster than existing, static sparse backpropagation algorithms and the accuracy loss is reduced on average by 6 % compared to a typical static setting of the back-propagation ratio.
This paper presents a novel low-jitter interface between a low-cost integrated IEEE802.11 chip and a FPGA. It is designed to be part of system hardware for ultra-precise synchronization between wireless stations. On physical level, it uses Wi-Fi chip coexistence signal lines and UART frame encoding. On its basis, we propose an efficient communication protocol providing precise timestamping of incoming frames and internal diagnostic mechanisms for detecting communication faults. Meanwhile it is simple enough to be implemented both in low-cost FPGA and commodity IEEE802.11 chip firmware. The results of computer simulation shows that developed FPGA implementation of the proposed protocol can precisely timestamp incoming frames as well as detect most of communication errors even in conditions of high interference. The probability of undetected errors was investigated. The results of this analysis are significant for the development of novel wireless synchronization hardware.
Time-Sensitive Networking (TSN) is the most promising time-deterministic wired communication approach for industrial applications. To extend TSN to "IEEE 802.11" wireless networks two challenging problems must be solved: synchronization and scheduling. This paper is focused on the first one. Even though a few solutions already meet the required synchronization accuracies, they are built on expensive hardware that is not suited for mass market products. While next Wi-Fi generation might support the required functionalities, this paper proposes a novel method that makes possible high-precision wireless synchronization using commercial low-cost components. With the proposed solution, a standard deviation of synchronization error of less than 500 ns can be achieved for many use cases and system loads on both CPU and network. This performance is comparable to modern wired real-time field busses, which makes the developed method a significant contribution for the extension of the TSN protocol to the wireless domain.
This article deals with the problem of wireless synchronization between onboard computing devices of small-sized unmanned aerial vehicles (SUAV) equipped with integrated wireless chips (IWC). Accurate synchronization between several devices requires the precise timestamping of batches transmitting and receiving on each of them. The best precision is demonstrated by those solutions where timestamping is performed on the PHY level, right after modulation/demodulation of the batch. Nowadays, most of the currently produced IWC are Systems-on-a-Chip (SoC) that include both PHY and MAC, implemented with one or several processor cores application. SoC allows create more cost and energy efficient wireless devices. At the same time, it limits the developers direct access to the internal signals and significantly complicates precise timestamping for sent and received batches, required for mutual synchronization of industrial devices. Some modern IEEE 802.11 IWCs have inbuilt functions that use internal chip clock to register timestamps. However, high jitter of the interfaces between the external device and IWC degrades the comparison of the timestamps from the internal clock to those registered by external devices. To solve this problem, the article proposes a novel approach to the synchronization, based on the analysis of IWC receiver input potential. The benefit of this approach is that there is no need to demodulate and decode the received batches, thus allowing it implementation with low-cost IWCs. In this araticle, Cypress CYW43438 was taken as an example for designing hardware and software solutions for synchronization between two SUAV onboard computing devices, equipped with IWC. The results of the performed experimental studies reveal that mutual synchronization error of the proposed method does not exceed 10 μs.
Wireless synchronization of industrial controllers is a challenging task in environments where wired solutions are not practical. The best solutions proposed so far to solve this problem require pretty expensive and highly specialized FPGA-based devices. With this work we counter the trend by introducing a straightforward approach to synchronize a fairly cheap IEEE 802.11 integrated wireless chip (IWC) with external devices. More specifically we demonstrate how we can reprogram the software running in the 802.11 IWC of the Raspberry Pi 3B and transform the receiver input potential of the wireless transceiver into a triggering signal for an external inexpensive FPGA. Experimental results show a mean-square synchronization error of less than 496 ns, while the absolute synchronization error does not exceed 6 μs. The jitter of the output signal that we obtain after synchronizing the clock of the external device did not exceed 5.2 μs throughout the whole measurement campaign. Even though we do not score new records in term of accuracy, we do in terms of complexity, cost, and availability of the required components: all these factors make the proposed technique a very promising of the deployment of large-scale low-cost automation solutions.
Precisely synchronized communication is a major precondition for many industrial applications. At the same time, hardware cost and power consumption need to be kept as low as possible in the Internet of Things (IoT) paradigm. While many wired solutions on the market achieve these requirements, wireless alternatives are an interesting field for research and development. This article presents a novel IEEE802.11n/ac wireless solution, exhibiting several advantages over state-of-the-art competitors. It is based on a market-available wireless System on a Chip with modified low-level communication firmware combined with a low-cost field-programmable gate array. By achieving submicrosecond synchronization accuracy, our solution outperforms the precision of low-cost products by almost four orders of magnitude. Based on inexpensive hardware, the presented wireless module is up to 20 times cheaper than software-defined-radio solutions with comparable timing accuracy. Moreover, it consumes three to five times less power. To back up our claims, we report data that we collected with a high sampling rate (2000 samples per second) during an extended measurement campaign of more than 120 h, which makes our experimental results far more representative than others reported in the literature. Additional support is provided by the size of the testbed we used during the experiments, composed of a hybrid network with nine nodes divided into two independent wireless segments connected by a wired backbone. In conclusion, we believe that our novel Industrial IoT module architecture will have a significant impact on the future technological development of high-precision time-synchronized communication for the cost-sensitive industrial IoT market.
One of the most important questions about smart metering systems for the end users is their data privacy and security. Indeed, smart metering systems provide a lot of advantages for distribution system operators (DSO), but functionalities offered to users of existing smart meters are still limited and society is becoming increasingly critical. Smart metering systems are accused of interfering with personal rights and privacy, providing unclear tariff regulations which not sufficiently encourage households to manage their electricity consumption in advance. In the specific field of smart grids, data security appears to be a necessary condition for consumer confidence without which they will not be able to give their consent to the collection and use of personal data concerning them.
The paper describes the methodology and experimental results for revealing similarities in thermal dependencies of biases of accelerometers and gyroscopes from 250 inertial MEMS chips (MPU-9250). Temperature profiles were measured on an experimental setup with a Peltier element for temperature control. Classification of temperature curves was carried out with machine learning approach.
A perfect sensor should not have thermal dependency at all. Thus, only sensors inside the clusters with smaller dependency (smaller total temperature slopes) might be pre-selected for production of high accuracy inertial navigation modules. It was found that no unified thermal profile (“family” curve) exists for all sensors in a production batch. However, obviously, sensors might be grouped according to their parameters. Therefore, the temperature compensation profiles might be regressed for each group. 12 slope coefficients on 5 degrees temperature intervals from 0°C to +60°C were used as the features for the k-means++ clustering algorithm.
The minimum number of clusters for all sensors to be well separated from each other by bias thermal profiles in our case is 6. It was found by applying the elbow method. For each cluster a regression curve can be obtained.
The Bluetooth community is in the process to develop mesh technology. This is highly promising as Bluetooth is widely available in Smart Phones and Tablet PCs, allowing an easy access to the Internet of Things. In this paper work, we investigate the performance of Bluetooth enabled mesh networking that we performed to identify the strengths and weaknesses. A demonstrator for this protocol has been implemented by using the Fruity Mesh protocol implementation. Extensive test cases have been executed to measure the performance, the reliability, the power consumption and the delay. For this, an Automated Physical Testbed (APTB), which emulates the physical channels has been used. The results of these measurements are considered useful for the real implementation of Bluetooth; not only for home and building automation, but also for industrial automation.
Digital networked communications are the key to all Internet-of-Things applications, especially to smart metering systems and the smart grid. In order to ensure a safe operation of systems and the privacy of users, the transport layer security (TLS) protocol, a mature and well standardized solution for secure communications, may be used. We implemented the TLS protocol in its latest version in a way suitable for embedded and resource-constrained systems. This paper outlines the challenges and opportunities of deploying TLS in smart metering and smart grid applications and presents performance results of our TLS implementation. Our analysis shows that given an appropriate implementation and configuration, deploying TLS in constrained smart metering systems is possible with acceptable overhead.
Enabling ultra-low latency is one of the major drivers for the development of future cellular networks to support delay sensitive applications including factory automation, autonomous vehicles and tactile internet. Narrowband Internet of Things (NB-IoT) is a 3 rd Generation Partnership Project (3GPP) Release 13 standardized cellular network currently optimized for massive Machine Type Communication (mMTC). To reduce the latency in cellular networks, 3GPP has proposed some latency reduction techniques that include Semi Persistent Scheduling (SPS) and short Transmission Time Interval (sTTI). In this paper, we investigate the potential of adopting both techniques in NB-IoT networks and provide a comprehensive performance evaluation. We firstly analyze these techniques and then implement them in an open-source network simulator (NS3). Simulations are performed with a focus on Cat-NB1 User Equipment (UE) category to evaluate the uplink user-plane latency. Our results show that SPS and sTTI have the potential to greatly reduce the latency in NB-IoT systems. We believe that both techniques can be integrated into NB-IoT systems to position NB-IoT as a preferred technology for low data rate Ultra-Reliable Low-Latency Communication (URLLC) applications before 5G has been fully rolled out.
Time Sensitive Networking (TSN) provides mechanisms to enable deterministic and real-time networking in industrial networks. Configuration of these mechanisms is key to fully deploy and integrate TSN in the networks. The IEEE 802.1 Qcc standard has proposed different configuration models to implement a TSN configuration. Up until now, TSN and its configuration have been explored mostly for Ethernet-based industrial networks. However, they are still considered “work-in-progress” for wireless networks. This work focuses on the fully centralized model and describes a generic concept to enable the configuration of TSN mechanisms in wireless industrial networks. To this end, a configuration entity is implemented to conFigure the wireless end stations to satisfy their requirements. The proposed solution is then validated with the Digital Enhanced Cordless Telecommunication ultra-low energy (DECT ULE) wireless communication protocol.
We propose secure multi-party computation techniques for the distributed computation of the average using a privacy-preserving extension of gossip algorithms. While recently there has been mainly research on the side of gossip algorithms (GA) for data aggregation itself, to the best of our knowledge, the aforementioned research line does not take into consideration the privacy of the entities involved. More concretely, it is our objective to not reveal a node's private input value to any other node in the network, while still computing the average in a fully-decentralized fashion. Not revealing in our setting means that an attacker gains only minor advantage when guessing a node's private input value. We precisely quantify an attacker's advantage when guessing - as a mean for the level of data privacy leakage of a node's contribution. Our results show that by perturbing the input values of each participating node with pseudo-random noise with appropriate statistical properties (i) only a minor and configurable leakage of private information is revealed, by at the same time (ii) providing a good average approximation at each node. Our approach can be applied to a decentralized prosumer market, in which participants act as energy consumers or producers or both, referred to as prosumers.
Wireless sensor networks have found their way into a wide range of applications among which environmental monitoring systems have attracted increasing interests of researchers. The main challenges for the applications are scalability of the network size and energy efficiency of the spatially distributed motes. These devices are mostly battery-powered and spend most of their energy budget on the radio transceiver module. A so-called Wake-On-Radio (WOR) technology can be used to achieve a reasonable balance among power consumption, range, complexity and response time. In this paper, a novel design for integration of WOR into IEEE802.1.5.4 is presented, which flexibly allows trade-offs in energy consumption between sender and receiver station, between real-time capability and energy consumption. For identical behavior, the proposed scheme is significantly more efficient than other schemes, which were proposed in recent publications, while preserving backward compatibility with standard IEEE802.15.4 transceivers.
Environmental Monitoring is an attractive application field for Wireless Sensor Network (WSN). Water Level Monitoring helps to increase the efficiency of water distribution and management. In Pakistan, the world’s largest irrigation system covers 90.000 km of channels which needs to be monitored and managed on different levels. Especially the sensor systems for the small distribution channels need to be low energy and low cost. The distribution presents a technical solution for a communication system which is developed in a research project being co-funded by German Academic Exchange Service (DAAD). The communication module is based on IEEE-802.15.4 transceivers which are enhanced through Wake-On-Radio (WOR) to combine low-energy and real-time behavior. On higher layers, IPv6 (6LoWPAN) and corresponding routing protocols like Routing Protocol for Low power and Lossy Networks (RPL) can extend range of the network. The data are stored in a database and can be viewed online via a web interface. Of course, also automatic data analysis can be performed.
Wireless sensor networks have recently found their way into a wide range of applications among which environmental monitoring system has attracted increasing interests of researchers. Such monitoring applications, in general, don way into a wide range of applications among which environmental monitoring system has attracted increasing interests of researc latency requirements regarding to the energy efficiency. Also a challenge of this application is the network topology as the application should be able to be deployed in very large scale. Nevertheless low power consumption of the devices making up the network must be on focus in order to maximize the lifetime of the whole system. These devices are usually battery-powered and spend most of their energy budget on radio transceiver module. A so-called Wake-On-Radio (WoR) technology can be used to achieve a reasonable balance among power consumption, range, complexity and response time. In this paper, some designs for integration of WOR into IEEE 802.1.5.4 are to be discussed, providing an overview of trade-offs in energy consumption while deploying the WoR schemes in a monitoring system.