IET Cyber-Physical Systems: Theory and Applications最新文献

Integrating physical processes with computational components creates cyber-physical systems (CPS) that seamlessly interact between the physical and digital worlds. The cyber-physical system has become an interesting research area and an attractive application domain, especially in industry based on the big advantages of this new paradigm. All companies are trying to use this model to control the real industry sector by integrating the cyber system. Many solutions were proposed however, they were not entirely satisfactory. This research proposes a novel CPS model based on Multi-Agent Systems (MAS). This model takes advantage of MAS's collaborative and distributed nature to improve CPS's performance and functionality. Therefore, this model offers a flexible and scalable approach to the development and management of intricate, and interwoven CPSs. The research focuses on developing a CPS model, that encompasses nine layers: the physical agent, security agent, computation agent, decision-making agent, control agent, communication agent, resilience agent, maintenance agent, and application agent layer. The MAS framework is employed to overcome the challenges associated with CPS design, such as coordination, dependability, maintainability, robustness, security, control etc. The results of this exploration are significant in their contribution to the advancement of CPS modelling by utilising Multi-Agent Systems.

In the recent years, cyberattacks to smart grids are becoming more frequent. Among the many malicious activities that can be launched against smart grids, the False Data Injection (FDI) attacks have raised significant concerns from both academia and industry. FDI attacks can affect the (internal) state estimation process—critical for smart grid monitoring and control—thus being able to bypass conventional Bad Data Detection (BDD) methods. Hence, prompt detection and precise localisation of FDI attacks are becoming of paramount importance to ensure smart grids security and safety. Several papers recently started to study and analyse this topic from different perspectives and address existing challenges. Data-driven techniques and mathematical modelling are the major ingredients of the proposed approaches. The primary objective is to provide a systematic review and insights into FDI attacks joint detection and localisation approaches considering that other surveys mainly concentrated on the detection aspects without detailed coverage of localisation aspects. For this purpose, more than 40 major research contributions were selected and inspected, while conducting a detailed analysis of the methodology and objectives in relation to the FDI attacks detection and localisation. Key findings of the identified papers were provided according to different criteria, such as employed FDI attacks localisation techniques, utilised evaluation scenarios, investigated FDI attack types, application scenarios, adopted methodologies and the use of additional data. Finally, open issues and future research directions were discussed.

The conditions for a feasible Cyber-Physical System (CPS) in a welding environment are explored for the manufacturing technology components while also focusing on machine learning tools. Increasing manufacturing efficiency means making digitalisation feasible for all technologies, including welding, given today's challenges. Early versions of manufacturing management, such as Computer Integrated Manufacturing, are already leading the way, and one of the latest milestones in these developments is CPS. It can be shown that the digital migration of specific sub-domains (e.g. visual inspection of the weld seam during quality assurance) is significantly more challenging and unimaginable without artificial intelligence applications. However, it is also true that the full integration needed to achieve autonomous manufacturing has yet to be fully achieved, although there is a strong demand in the industry for these CPS to work. In some areas, the digital switchover has already been prepared. However, the interconnection of these subsystems requires modern information systems or, in the case of existing ones, their upgrading to the appropriate level. This research area is set to be addressed comprehensively by initiating several projects. In the initial phase, the aim is to develop an architecture that integrates the various Information Technology applications. In this work, the digital manufacturing environment under CPS is studied, the relevant components are explored, the conditions for the transition from traditional to CPS-based manufacturing are examined and examples of planned further specific studies on the components are listed.

With the current COVID-19 pandemic, sophisticated epidemiological surveillance systems are more important than ever because conventional approaches have not been able to handle the scope and complexity of this global emergency. In response to this challenge, the authors present the state-of-the-art SEIR-Driven Semantic Integration Framework (SDSIF), which leverages the Internet of Things (IoT) to handle a variety of data sources. The primary innovation of SDSIF is the development of an extensive COVID-19 ontology, which makes unmatched data interoperability and semantic inference possible. The framework facilitates not only real-time data integration but also advanced analytics, anomaly detection, and predictive modelling through the use of Recurrent Neural Networks (RNNs). By being scalable and flexible enough to fit into different healthcare environments and geographical areas, SDSIF is revolutionising epidemiological surveillance for COVID-19 outbreak management. Metrics such as Mean Absolute Error (MAE) and Mean sqḋ Error (MSE) are used in a rigorous evaluation. The evaluation also includes an exceptional R-squared score, which attests to the effectiveness and ingenuity of SDSIF. Notably, a modest RMSE value of 8.70 highlights its accuracy, while a low MSE of 3.03 highlights its high predictive precision. The framework's remarkable R-squared score of 0.99 emphasises its resilience in explaining variations in disease data even more.

Alzheimer’s disease (AD) is a neurodegenerative disorder that mostly affects old aged people. Its symptoms are initially mild, but they get worse over time. Although this health disease has no cure, its early diagnosis can help to reduce its impacts. A methodology SMOTE-RF is proposed for AD prediction. Alzheimer's is predicted using machine learning algorithms. Performances of three algorithms decision tree, extreme gradient boosting (XGB), and random forest (RF) are evaluated in prediction. Open Access Series of Imaging Studies longitudinal dataset available on Kaggle is used for experiments. The dataset is balanced using synthetic minority oversampling technique. Experiments are done on both imbalanced and balanced datasets. Decision tree obtained 73.38% accuracy, XGB obtained 83.88% accuracy and RF obtained a maximum of 87.84% accuracy on the imbalanced dataset. Decision tree obtained 83.15% accuracy, XGB obtained 91.05% accuracy and RF obtained maximum 95.03% accuracy on the balanced dataset. A maximum accuracy of 95.03% is achieved with SMOTE-RF.

The Internet of Things (IoT) is revolutionizing the healthcare industry by enhancing personalized patient care. However, the transmission of sensitive health data in IoT systems presents significant security and privacy challenges, further exacerbated by the difficulty of exploiting traditional protection means due to poor battery equipment and limited storage and computational capabilities of IoT devices. The authors analyze techniques applied in the medical context to encrypt sensible data and deal with the unique challenges of resource-constrained devices. A technique that is facing increasing interest is the Physical Unclonable Function (PUF), where biometrics are implemented on integrated circuits' electric features. PUFs, however, demand special hardware, so in this work, instead of considering the physical device as a source of randomness, an ElectroCardioGram (ECG) can be taken into consideration to make a ‘virtual’ PUF. Such an mechanism leverages individual ECG signals to generate a cryptographic key for encrypting and decrypting data. Due to the poor stability of the ECG signal and the typical noise existing in the measurement process for such a signal, filtering and feature extraction techniques must be adopted. The proposed model considers the adoption of pre-processing techniques in conjunction with a fuzzy extractor to add stability to the signal. Experiments were performed on a dataset containing ECG records gathered over 6 months, yielding good results in the short term and valuable outcomes in the long term, paving the way for adaptive PUF techniques in this context.

Along with the development of industrial and distributed systems, security concerns have also emerged in industrial communication protocols. PTP, Precision Time Protocol, is one of the most precise time synchronisation protocols for industrial devices. It ensures real-time activity of the industrial control systems with precision equal to microseconds. In order to address the actual or potential security issues of PTP, this article firstly describes attack models applicable to PTP and then focuses on applying Coloured Petri Net to formally analyse the attack detection methods and also model PTP. The alignment of simulation results with the model and the considered assumptions show the suitability and accuracy of the proposed model.

Cyber-physical systems have behaviour that crosses domain boundaries during events such as planned operational changes and malicious disturbances. Traditionally, the cyber and physical systems are monitored separately and use very different toolsets and analysis paradigms. The security and privacy of these cyber-physical systems requires improved understanding of the combined cyber-physical system behaviour and methods for holistic analysis. Therefore, the authors propose leveraging clustering techniques on cyber-physical data from smart grid systems to analyse differences and similarities in behaviour during cyber-, physical-, and cyber-physical disturbances. Since clustering methods are commonly used in data science to examine statistical similarities in order to sort large datasets, these algorithms can assist in identifying useful relationships in cyber-physical systems. Through this analysis, deeper insights can be shared with decision-makers on what cyber and physical components are strongly or weakly linked, what cyber-physical pathways are most traversed, and the criticality of certain cyber-physical nodes or edges. This paper presents several types of clustering methods for cyber-physical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyber-physical situational awareness. The collection of these clustering techniques provide a foundational basis for cyber-physical graph interdependency analysis.

Energy storage is playing an increasingly important role in the modern world as sustainability is becoming a critical issue. Within this domain, rechargeable battery is gaining significant popularity as it has been adopted to serve as the power supplier in a broad range of application scenarios, such as cyber-physical system (CPS), due to multiple advantages. On the other hand, battery inspection and management solutions have been constructed based on the CPS architecture in order to guarantee the quality, reliability and safety of rechargeable batteries. In specific, lifetime prediction is extensively studied in recent research as it can help assess the quality and health status to facilitate the manufacturing and maintenance. Due to the aforementioned importance, the authors aim to conduct a comprehensive survey on the data-driven techniques for battery lifetime prediction, including their current status, challenges and promises. In contrast to existing literature, the battery lifetime prediction methods are studied under CPS context in this survey. Hence, the authors focus on the algorithms for lifetime prediction as well as the engineering frameworks that enable the data acquisition and deployment of prediction models in CPS systems. Through this survey, the authors intend to investigate both academic and practical values in the domain of battery lifetime prediction to benefit both researchers and practitioners.