Parameter-Invariant Monitor Design for Cyber Physical Systems

The tight interaction between information technology and the physical world inherent in Cyber-Physical Systems (CPS) can challenge traditional approaches for monitoring safety and security. Data collected for robust CPS monitoring is often sparse and may lack rich training data describing critical events/attacks. Moreover, CPS often operate in diverse environments that can have significant inter/intra-system variability. Furthermore, CPS monitors that are not robust to data sparsity and inter/intra-system variability may result in inconsistent performance and may not be trusted for monitoring safety and security. Towards overcoming these challenges, this paper presents recent work on the design of parameter-invariant (PAIN) monitors for CPS. PAIN monitors are designed such that unknown events and system variability minimally affect the monitor performance. This work describes how PAIN designs can achieve a constant false alarm rate (CFAR) in the presence of data sparsity and intra/inter system variance in real-world CPS. To demonstrate the design of PAIN monitors for safety monitoring in CPS with different types of dynamics, we consider systems with networked dynamics, linear-time invariant dynamics, and hybrid dynamics that are discussed through case studies for building actuator fault detection, meal detection in type I diabetes, and detecting hypoxia caused by pulmonary shunts in infants. In all applications, the PAIN monitor is shown to have (significantly) less variance in monitoring performance and (often) outperforms other competing approaches in the literature. Finally, an initial application of PAIN monitoring for CPS security is presented along with challenges and research directions for future security monitoring deployments

Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges

Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence. As machine learning, federated learning is threatened by adversarial attacks against the integrity of the learning model and the privacy of data via a distributed approach to tackle local and global learning. This weak point is exacerbated by the inaccessibility of data in federated learning, which makes harder the protection against adversarial attacks and evidences the need to furtherance the research on defence methods to make federated learning a real solution for safeguarding data privacy. In this paper, we present an extensive review of the threats of federated learning, as well as as their corresponding countermeasures, attacks versus defences. This survey provides a taxonomy of adversarial attacks and a taxonomy of defence methods that depict a general picture of this vulnerability of federated learning and how to overcome it. Likewise, we expound guidelines for selecting the most adequate defence method according to the category of the adversarial attack. Besides, we carry out an extensive experimental study from which we draw further conclusions about the behaviour of attacks and defences and the guidelines for selecting the most adequate defence method according to the category of the adversarial attack. This study is finished leading to meditated learned lessons and challenges

From ethnographic research to big data analytics - A case of maritime energy-efficiency optimization

The shipping industry constantly strives to achieve efficient use of energy during sea voyages. Previous research that can take advantages of both ethnographic studies and big data analytics to understand factors contributing to fuel consumption and seek solutions to support decision making is rather scarce. This paper first employed ethnographic research regarding the use of a commercially available fuel-monitoring system. This was to contextualize the real challenges on ships and informed the need of taking a big data approach to achieve energy efficiency (EE). Then this study constructed two machine-learning models based on the recorded voyage data of five different ferries over a one-year period. The evaluation showed that the models generalize well on different training data sets and model outputs indicated a potential for better performance than the existing commercial EE system. How this predictive-analytical approach could potentially impact the design of decision support navigational systems and management practices was also discussed. It is hoped that this interdisciplinary research could provide some enlightenment for a richer methodological framework in future maritime energy researc

Cross-Border Collaboration in Disaster Management

Wenn sich eine Katastrophe ereignet, ist eine schnelle und koordinierte Reaktion der verschiedenen Krisenmanagementakteure unerlässlich, um die vorhandenen Ressourcen bestmöglich einzusetzen und somit ihre Auswirkungen zu begrenzen. Dieses Zusammenspiel wird erschwert, wenn die Katastrophe mehrere Länder betrifft. Neben den unterschiedlichen Regelungen und Systemen spielen dann auch kulturelle Einflüsse wie Sprachbarrieren oder mangelndes Vertrauen eine entscheidende Rolle. Obwohl die Resilienz von Grenzgebieten von fundamentaler Bedeutung ist, wird diese in der wissenschaftlichen Literatur immer noch unterschätzt. Im ersten Teil dieser Arbeit wird ein agentenbasiertes Modell zur Untersuchung der organisationsübergreifenden Zusammenarbeit bei Katastropheneinsätzen in einer Grenzregion vorgestellt. Indem Kommunikationsprotokolle aus der Literatur auf den Kontext der grenzüberschreitenden Kooperation erweitert werden, analysiert das Modell die globale Dynamik, die aus lokalen Entscheidungen resultiert. Ein szenariobasierter Ansatz zeigt, dass höheres Vertrauen zwar zu signifikant besseren Versorgungsraten führt, der Abbau von Sprachbarrieren aber noch effizienter ist. Insbesondere gilt dies, wenn die Akteure die Sprache des Nachbarlandes direkt sprechen, anstatt sich auf eine allgemeine Lingua franca zu verlassen. Die Untersuchung der Koordination zeigt, dass Informationsflüsse entlang der hierarchischen Organisationsstruktur am erfolgreichsten sind, während spontane Zusammenarbeit durch ein etabliertes informelles Netzwerk privater Kontakte den Informationsaustausch ergänzen und in dynamischen Umgebungen einen Vorteil darstellen kann. Darüber hinaus verdoppelt die Einbindung von Spontanfreiwilligen den Koordinationsaufwand. Die Koordination über beide Dimensionen, zum einen die Einbindung in den Katastrophenschutz und zum anderen über Grenzen hinweg, führt jedoch zu einer optimalen Versorgung der betroffenen Bevölkerung. In einem zweiten Teil stellt diese Arbeit ein innovatives empirisches Studiendesign vor, das auf transnationalem Sozialkapital und Weiners Motivationstheorie basiert, um prosoziale Beziehungen der Menschen über nationale Grenzen hinweg zu quantifizieren. Regionale Beziehungen innerhalb der Länder werden dabei als Vergleichsbasis genommen. Die mittels repräsentativer Telefoninterviews in Deutschland, Frankreich und der deutsch-französischen Grenzregion erhobenen Daten belegen die Hypothese, dass das Sozialkapital und die Hilfsbereitschaft über die deutsch-französische Grenze hinweg mindestens so hoch ist wie das regionale Sozialkapital und die Hilfsbereitschaft innerhalb der jeweiligen Länder. Folglich liefert die Arbeit wertvolle Erkenntnisse für Entscheidungsträger, um wesentliche Barrieren in der grenzüberschreitenden Kooperation abzubauen und damit die grenzüberschreitende Resilienz bei zukünftigen Katastrophen zu verbessern. Implikationen für die heutige Zeit in Bezug auf Globalisierung versus aufkommendem Nationalismus sowie Auswirkungen von (Natur-) Katastrophen werden diskutiert

Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts

The climate changes that are visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this book presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on energy internet, blockchain technology, and smart contracts, we hope that they are of interest to readers working in the related fields mentioned above