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“Cyber-Physical Systems or “smart” systems are co-engineered interacting networks of physical and computational components. These systems will provide the foundation of our critical infrastructure, form the basis of emerging and future smart services, and improve our quality of life in many areas.” (National Institute of Standards and Technology: C...
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Context 1
... involvement of the user in the planning process. The final aspect to be considered is Usability, which measures the effectiveness, efficiency and satisfaction the user experiences while performing a specific task with the help of the product in a given environment. [26] [27] The criteria referring to the general usability design of machines (see Fig. 2 Usability Engineering process) can be determined on the basis of the information provided by a much researched field. This is the field of the usability design of medical devices. The process of the usability design of machines consist of ten steps. [26][27] [27] Step 1 (User research): the Application specification contains the ...
Citations
... Due to their complexity and heterogeneity, CPSs can be vulnerable to a variety of cyber and physical threats of both random and malicious origin. Exposure to those threats might have serious consequences, ranging from financial loss to loss of human lives [2]. Therefore, in order to reduce the risk of malfunctions, both the cyber and physical parts of those systems must be carefully monitored [3], possibly employing techniques based on intelligent fault detection and predictive analytics, such as predictive maintenance (PdM), which are in the main focus of this paper. ...
... In the first case, we see that there is a focus of HMN-related projects on investigating the interactions rising between humans and robots and the ways in which more cognitive links arise. Both cognitive and interactive aspects of HMNs are closely related to increased collaboration outputs, that are key elements for achieving more resilient environments [46,47]. Second, there seems to be a mobility-related area where HMN-based projects expand their knowledge space, exploring the ways in which HMNs affect road safety, traffic and autonomous driving. ...
... It interesting to notice that there is an additional branch -not fully independent from the previous-focusing on aspects more connected to autonomous robots and including the terms "control", "sensors" and "navigation". Both perspectives capture a trend of EU-funded projects to further improve robots' capabilities in relation to their interactions with human and physical environment, which has been stressed as an essential aspect for boosting their role in building resilient spaces [47,50]. Fig. 6. ...
The relationship between humans and machines has been thoroughly investigated throughout existing literature focusing on various angles of everyday life. Research on cyber-physical systems and human-machine networks has tried to shed light on the connection between social and technological aspects, offering insights and helping on a better matching and exploitation of the revealed space amongst those elements. In several cases, the exploration of human-machine networks has offered new ways to engage with vulnerable and marginalized groups more effectively, as well as to foster the well-being of individuals and communities. This can be perceived as a hidden potential of cyber-physical systems and human-machine networks towards empowering resilience, which can be approached by various developmental dimensions, like community engagement, transport safety, energy production and consumption, as well as new techno-economic orientations. The study targets on mapping the links between elements being part of cyber-physical systems, human-machine networks and resilience, that have been created through research and innovation projects funded by the European Commission under the programme Horizon 2020, between 2014 and 2021. A total set of 7,859 projects are analyzed in relation to their title and abstract for revealing bridges that have been constructed between human-machine features and resilience. Our analysis further explores the main fields of application of projects on cyber-physical systems and human-machine networks and reveals the ways in which the relate to two resilience characteristics, connectivity and collaboration. It shows the increasing focus of European research projects on cyber-physical systems and human-machine networks and their rising potential for resilience.
... Cyber-physical systems are becoming increasingly complex, critical, ubiquitous and pervasive. Research shows that the complexity is a result of three main factors (Banerjee et al., 2012;Sztipanovits et al., 2012;Tavčar & Horváth, 2019;Tokody, Papp, Iantovics, & Flammini, 2019): (i) size of the software and of the whole system (system-of-systems) due to non-straightforward functional requirements to be fulfilled; (ii) hardware and software heterogeneity due to diverse embedded systems architectures, protocols, manufacturers and connection facilities, possibly including legacy devices; and (iii) distribution due to large networks of connected devices, including the Internet of Things (IoT), Industry 4.0, and domains with strict goals such as Intelligent Transportation Systems and e-health. These complexities raise challenges; we highlight two of the key challenges that are driven by the objectives of smarter systems. ...
With the advancing digitisation of society and industry we observe a progressing blending of computational, physical, and social processes. The trustworthiness and sustainability of these systems will be vital for our society. However, engineering modern computing systems is complex as they have to: i) operate in uncertain and continuously changing environments, ii) deal with huge amounts of data, and iii) require seamless interaction with human operators. To that end, we argue that both systems and the way we engineer them must become smarter. With smarter we mean that systems and engineering processes adapt and evolve themselves through a perpetual process that continuously improves their capabilities and utility to deal with the uncertainties and amounts of data they face. We highlight key engineering areas: cyber-physical systems, self-adaptation, data-driven technologies, and visual analytics, and outline key challenges in each of them. From this, we propose a research agenda for the years to come.
... Compared to 'dependable CPS', 'resilient CPS' can deliver services that can justifiably be trusted even in the presence of changes such as system upgrades and evolution. It is worth mentioning that CPS are often associated with the concepts of distributed embedded systems and smart systems, with which they share aspects of complexity, autonomy and criticality [17]. The interest in resilient CPS has grown in the last years as witnessed by the numerous projects and publications on related topics [7]. ...
Digital twins (DT) are emerging as an extremely promising paradigm for run-time modelling and performability prediction of cyber-physical systems (CPS) in various domains. Although several different definitions and industrial applications of DT exist, ranging from purely visual three-dimensional models to predictive maintenance tools, in this paper, we focus on data-driven evaluation and prediction of critical dependability attributes such as safety. To that end, we introduce a conceptual framework based on autonomic systems to host DT run-time models based on a structured and systematic approach. We argue that the convergence between DT and self-adaptation is the key to building smarter, resilient and trustworthy CPS that can self-monitor, self-diagnose and—ultimately—self-heal. The conceptual framework eases dependability assessment, which is essential for the certification of autonomous CPS operating with artificial intelligence and machine learning in critical applications.
This article is part of the theme issue ‘Towards symbiotic autonomous systems’.
... Cyber-physical systems are becoming increasingly complex, critical, ubiquitous and pervasive. Research shows that the complexity is a result of three main factors (Banerjee et al., 2012;Sztipanovits et al., 2012;Tavčar & Horváth, 2019;Tokody, Papp, Iantovics, & Flammini, 2019): (i) size of the software and of the whole system (system-of-systems) due to non-straightforward functional requirements to be fulfilled; (ii) hardware and software heterogeneity due to diverse embedded systems architectures, protocols, manufacturers and connection facilities, possibly including legacy devices; and (iii) distribution due to large networks of connected devices, including the Internet of Things (IoT), Industry 4.0, and domains with strict goals such as Intelligent Transportation Systems and e-health. These complexities raise challenges; we highlight two of the key challenges that are driven by the objectives of smarter systems. ...
With the advancing digitisation of society and industry we observe a progressing blending of computational, physical, and social processes. The trustworthiness and sustainability of these systems will be vital for our society. However, engineering modern computing systems is complex as they have to: i) operate in uncertain and continuously changing environments, ii) deal with huge amounts of data, and iii) require seamless interaction with human operators. To that end, we argue that both systems and the way we engineer them must become smarter. With smarter we mean that systems and engineering processes adapt and evolve themselves through a perpetual process that continuously improves their capabilities and utility to deal with the uncertainties and amounts of data they face. We highlight key engineering areas: cyber-physical systems, self-adaptation, data-driven technologies, and visual analytics, and outline key challenges in each of them. From this, we propose a research agenda for the years to come.
... Compared to "« dependable CPS," », "« resilient CPSs" » are able to delivery services that can justifiably be trusted even in the presence of changes such as system upgrades and evolution. It is worth mentioning that CPSs are often associated with the concepts of distributed embedded systems and smart systems, with which they share aspects of complexity, autonomy, and criticality (Tokody et al. 2019). The interest in resilient CPS has grown in the last years as witnessed by the numerous projects and publications addressing related topics (Flammini 2019). ...
Resilience in Cyber-Physical Systems (CPS)
Definition for the Encyclopedia of Cryptography, Security and Privacy
https://link.springer.com/referenceworkentry/10.1007/978-3-642-27739-9_1728-2
... Awareness of the latest technologies and threats can significantly improve protection against cyber-threats. However, the intrinsic complexity and the evolving nature of threats can make this task extremely tough [20]. Therefore, the future of CPS security strongly depends on the application of artificial intelligence methods to improve situation recognition, emergency response and crisis management, by automating the detection of threats and the activation of appropriate countermeasures by means Security Orchestration, Automation and Response (SOAR) systems. ...
Cybersecurity has become an emerging challenge for business information management and critical infrastructure protection in recent years. Artificial Intelligence (AI) has been widely used in different fields, but it is still relatively new in the area of Cyber-Physical Systems (CPS) security. In this paper, we provide an approach based on Machine Learning (ML) to intelligent threat recognition to enable run-time risk assessment for superior situation awareness in CPS security monitoring. With the aim of classifying malicious activity, several machine learning methods, such as k-nearest neighbours (kNN), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF), have been applied and compared using using two different publicly available real-world testbeds. The results show that RF allowed for the best classification performance. When used in reference industrial applications, the approach allows security control room operators to get notified of threats only when classification confidence will be above a threshold, hence reducing the stress of security managers and effectively supporting their decisions.
... Gao & Huang, 2019) and based on data triangulation activities (Cedeño et al., 2019) can develop a broader understanding of consumers. Do phase activities during the purchase process Virtual shopping assistance, human computer interaction (Tokody et al., 2019;Grandinetti, 2020;J. Li et al., 2020) Activities of autonomous shopping systems; two-way communication; interactive communication platform (B. ...
... Li et al., 2020) Activities of autonomous shopping systems; two-way communication; interactive communication platform (B. Gao & Huang, 2019;Zhan & Ning, 2021;Najlae et al., 2021;Nikulin & Bagautdinova, 2019;Schweitzer et al., 2019;Henkens et al., 2021;Lucia-Palacios & Pérez-López, 2021a;Lucia-Palacios & Pérez-López, 2021b) Activities of onboarding and information, smart platform (Michler et al., 2020;Cedeño et al., 2019;Zhan & Ning, 2021;Liu et al., 2020;Henkens et al., 2021;Bădică & Mitucă, 2021) Intrusiveness activities -the surveillance of the consumer and his/her daily life (Lucia-Palacios & Pérez-López, 2021a; Lucia-Palacios & Pérez-López, 2021b; Bădică & Mitucă, 2021) Value quantification activities -baseline assessment, performance evaluation (Classen & Friedli, 2019) Activities of mass customization (Tokody et al., 2019;Nikulin & Bagautdinova, 2019) Transparency activities for collecting data (Michler et al., 2020;Cedeño et al., 2019;Nikulin & Bagautdinova, 2019;Paluch & Tuzovic, 2019;Bădică & Mitucă, 2021) Establishment relationship network (defining types of partners, relationship type, sharing and coordination activities) (Ferreira Junior et al., 2022;Nikulin & Bagautdinova, 2019) Source: Authors ...
... Gao & Huang, 2019). The marketing process includes activities of managing consumer needs and mass adaptation to consumers with the help of intelligent interfaces (products, robots, software) capable to interact (Tokody et al., 2019). Very significant activities in this phase are onboarding and information for building understanding, knowledge transfer, and introduction to the use of the products (Michler et al., 2020). ...
In a digital context, the customer experience represents a complex field of competition for companies in the process of retaining loyal and attracting new customers. The digital transformation paradigm, in the technological and business aspect, should create value for the customer and increase the customer experience easier. However, challenges such as dynamic market changes and disruptions leading to increasingly complex customer requirements, make customer journey management a critical field for companies. This paper presents a preliminary review and provides insight into the problems of building loyalty and increasing customer experience under the influence of digital technologies. The recognized problems, according to secondary data, indicates that the potential of customer experience management with the help of digital technologies was not achieved. In this paper, recommendations for the elimination of mentioned problems were defined and how usage of digital technologies can contribute to building loyalty through analysis, monitoring, and support of customer journey.
... Intelligent systems, such as Smart Cities are based on the flow of information [2]. Another important aspect of a good smart city is to make good decisions. ...
In our current study, we are aiming to explore data management methods in Smart City systems. In data management, AI (Artificial Intelligence) can be used as well. Solutions for the usage of AI and integration into Smart City concept will be researched as well.
Main motivation of the study is to draw attention to one of the most important element of Smart Cities, to the flow of data. Our study provide a possible solution for managing data and keep data up-to-date in such systems with the usage of newest technology possibilities.
While explaining the solution, we will give all the necessary details about the data flow model between the AI based system and humans who are using the Smart City. For managing the data-flow, we would like to use Big Data methods extended with other required methods. We are using the term of Big Data as a technology maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets [1] connecting with AI solutions.
... Most modern connected systems, like the ones used in smart houses, smart cities and smart factories, include concepts inherited from the Internet of Things (IoT) and Cyber-Physical Systems (CPS) domains [3]. ...
Today’s digital world and evolving technology has improved the quality of our lives but it has also come with a number of new threats. In the society of smart-cities and Industry 4.0, where many cyber–physical devices connect and exchange data through the Internet of Things, the need for addressing information security and solve system failures becomes inevitable. System failures can occur because of hardware failures, software bugs or interoperability issues. In this paper we introduce the industry-originated concept of “smart-troubleshooting” that is the set of activities and tools needed to gather failure information generated by heterogeneous connected devices, analyze them, and match them with troubleshooting instructions and software fixes. As a consequence of implementing smart-troubleshooting, the system would be able to self-heal and thus become more resilient. This paper aims to survey frameworks, methodologies and tools related to this new concept, and especially the ones needed to model, analyze and recover from failures in a (semi)automatic way. Smart-troubleshooting has a relation with event analysis to perform diagnostics and prognostics on devices manufactured by different suppliers in a distributed system. It also addresses management of appropriate product information specified in possibly unstructured formats to guide the troubleshooting workflow in identifying fault—causes and solutions. Relevant research is briefly surveyed in the paper in order to highlight current state-of-the-art, open issues, challenges to be tackled and future opportunities in this emerging industry paradigm.
