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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.
Blockchain interoperability: the state of heterogenous blockchain-to-blockchain communication
(2023)
Blockchain technology has been increasingly adopted over the past few years since the introduction of Bitcoin, with several blockchain architectures and solutions being proposed. Most proposed solutions have been developed in isolation, without a standard protocol or cryptographic structure to work with. This has led to the problem of interoperability, where solutions running on different blockchain platforms are unable to communicate, limiting the scope of use. With blockchains being adopted in a variety of fields such as the Internet of Things, it is expected that the problem of interoperability if not addressed quickly, will stifle technology advancement. This paper presents the current state of interoperability solutions proposed for heterogenous blockchain systems. A look is taken at interoperability solutions, not only for cryptocurrencies, but also for general data-based use cases. Current open issues in heterogenous blockchain interoperability are presented. Additionally, some possible research directions are presented to enhance and to extend the existing blockchain interoperability solutions. It was discovered that though there are a number of proposed solutions in literature, few have seen real-world implementation. The lack of blockchain-specific standards has slowed the progress of interoperability. It was also realized that most of the proposed solutions are developed targeting cryptocurrency-based applications.
This paper presents an overview of EREMI, a two-year project funded under ERASMUS+ KA203, and its results. The project team’s main objective was to develop and validate an advanced interdisciplinary higher education curriculum, which includes lifelong learning components. The curriculum focuses on enhancing resource efficiency in the manufacturing industry and optimising poorly or non-digitised industrial physical infrastructure systems. The paper also discusses the results of the project, highlighting the successful achievement of its goals. EREMI effectively supports the transition to Industry 5.0 by preparing a common European pool of future experts. Through comprehensive research and collaboration, the project team has designed a curriculum that equips students with the necessary skills and knowledge to thrive in the evolving manufacturing landscape. Furthermore, the paper explores the significance of EREMI’s contributions to the field, emphasising the importance of resource efficiency and system optimisation in industrial settings. By addressing the challenges posed by under-digitised infrastructure, the project aims to drive sustainable and innovative practices in manufacturing. All five project partner organisations have been actively engaged in offering relevant educational content and framework for decentralised sustainable economic development in regional and national contexts through capacity building at a local level. A crucial element of the added value is the new channel for obtaining feedback from students. The survey results, which are outlined in the paper, offer valuable insights gathered from students, contributing to the continuous improvement of the project.
Conceptualization and implementation of automated optimization methods for private 5G networks
(2023)
Today’s companies are adjusting to the new connectivity realities. New applications require more bandwidth, lower latency, and higher reliability as industries become more distributed and autonomous. Private 5th Generation (5G) networks known as 5G Non-Public Networks (5G-NPN), is a novel 3rd Generation Partnership Project (3GPP)- based 5G network that can deliver seamless and dedicated wireless access for a particular industrial use case by providing the mentioned application’s requirements. To meet these requirements, several radio-related aspects and network parameters should be considered. In many cases, the behavior of the link connection may vary based on wireless conditions, available network resources, and User Equipment (UE) requirements. Furthermore, Optimizing these networks can be a complex task due to the large number of network parameters and KPIs that need to be considered. For these reasons, traditional solutions and static network configuration are not affordable or simply impossible. Despite the existence of papers in the literature that address several optimization methods for cellular networks in industrial scenarios, more insight into these existing but complex or unknown methods is needed.
In this thesis, a series of optimization methods were implemented to deliver an optimal configuration solution for a 5G private network. To facilitate this implementation, a testing system was implemented. This system enables remote control over the UE and 5G network, establishment of a test environment, extraction of relevant KPI reports from both UE and network sides, assessment of test results and KPIs, and effective utilization of the optimization and sampling techniques.
The research highlights the advantageous aspects of automated testing by using OFAT, Simulated Annealing, and Random Forest Regressor methods. With OFAT, as a common sampling method, a sensitivity analysis and an impact of each single parameter variation on the performance of the network were revealed. With Simulated Annealing, an optimal solution with MSE of roughly 10 was revealed. And, in the Random Forest Regressor, it was seen that this method presented a significant advantage over the simulated annealing method by providing substantial benefits in time efficiency due to its machine- learning capability. Additionally, it was seen that by providing a larger dataset or using some other machine-learning techniques, the solution might be more accurate.
Extensible Authentication Protocol (EAP) bietet eine flexible Möglichkeit zur Authentifizierung von Endgeräten und kann in Kombination mit TLS für eine zertifikatsbasierte Authentifizierung verwendet werden. Motiviert wird diese Arbeit von einer potenziellen Erweiterung für PROFINET, die diese Protokolle einsetzen soll.
Dabei soll eine sicherer EAP-TLS-Protokollstacks für eingebettete Systeme in der Programmiersprache Rust entwickelt werden. Durch das Ownership-System von Rust können Speicherfehler eliminiert werden, ohne dabei auf die positiven Eigenschaften von nativen Sprachen zu verzichten. Es wird ein besonderes Augenmerk auf wie die Verwendung klassischer Rust-Bibliotheken im Umfeld von eingebetteten Systemen, den Einfluss des Speichermodells auf das Design, sowie die Integration von C-Bibliotheken für automatisierte Interoperabilitätstests gelegt.
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.
Blockchain-IIoT integration into industrial processes promises greater security, transparency, and traceability. However, this advancement faces significant storage and scalability issues with existing blockchain technologies. Each peer in the blockchain network maintains a full copy of the ledger which is updated through consensus. This full replication approach places a burden on the storage space of the peers and would quickly outstrip the storage capacity of resource-constrained IIoT devices. Various solutions utilizing compression, summarization or different storage schemes have been proposed in literature. The use of cloud resources for blockchain storage has been extensively studied in recent years. Nonetheless, block selection remains a substantial challenge associated with cloud resources and blockchain integration. This paper proposes a deep reinforcement learning (DRL) approach as an alternative to solving the block selection problem, which involves identifying the blocks to be transferred to the cloud. We propose a DRL approach to solve our problem by converting the multi-objective optimization of block selection into a Markov decision process (MDP). We design a simulated blockchain environment for training and testing our proposed DRL approach. We utilize two DRL algorithms, Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) to solve the block selection problem and analyze their performance gains. PPO and A2C achieve 47.8% and 42.9% storage reduction on the blockchain peer compared to the full replication approach of conventional blockchain systems. The slowest DRL algorithm, A2C, achieves a run-time 7.2 times shorter than the benchmark evolutionary algorithms used in earlier works, which validates the gains introduced by the DRL algorithms. The simulation results further show that our DRL algorithms provide an adaptive and dynamic solution to the time-sensitive blockchain-IIoT environment.
In this paper, we study the runtime performance of symmetric cryptographic algorithms on an embedded ARM Cortex-M4 platform. Symmetric cryptographic algorithms can serve to protect the integrity and optionally, if supported by the algorithm, the confidentiality of data. A broad range of well-established algorithms exists, where the different algorithms typically have different properties and come with different computational complexity. On deeply embedded systems, the overhead imposed by cryptographic operations may be significant. We execute the algorithms AES-GCM, ChaCha20-Poly1305, HMAC-SHA256, KMAC, and SipHash on an STM32 embedded microcontroller and benchmark the execution times of the algorithms as a function of the input lengths.
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.
The integration of Internet of Things devices onto the Blockchain implies an increase in the transactions that occur on the Blockchain, thus increasing the storage requirements.
A solution approach is to leverage cloud resources for storing blocks within the chain. The paper, therefore, proposes two solutions to this problem. The first being an improved hybrid architecture design which uses containerization to create a side chain on a fog node for the devices connected to it and an Advanced Time‑variant Multi‑objective Particle Swarm Optimization Algorithm (AT‑MOPSO) for determining the optimal number of blocks that should be transferred to the cloud for storage. This algorithm uses time‑variant weights for the velocity of the particle swarm optimization and the non‑dominated sorting and mutation schemes from NSGA‑III. The proposed algorithm was compared with results from the original MOPSO algorithm, the Strength Pareto Evolutionary Algorithm (SPEA‑II), and the Pareto Envelope‑based Selection Algorithm with region‑based selection (PESA‑II), and NSGA‑III. The proposed AT‑MOPSO showed better results than the aforementioned MOPSO algorithms in cloud storage cost and query probability optimization. Importantly, AT‑MOPSO achieved 52% energy efficiency compared to NSGA‑III.
To show how this algorithm can be applied to a real‑world Blockchain system, the BISS industrial Blockchain architecture was adapted and modified to show how the AT‑MOPSO can be used with existing Blockchain systems and the benefits it provides.