Systemic formalisation of Cyber-Physical-Social System (CPSS): A systematic literature review

peer reviewedThe notion of Cyber-Physical-Social System (CPSS) is an emerging concept developed as a result of the need to understand the impact of Cyber-Physical Systems (CPS) on humans and vice versa. This paradigm shift from CPS to CPSS was mainly attributed to the increasing use of sensor enabled smart devices and the tight link with the users. The concept of CPSS has been around for over a decade and it has gained an increasing attention over the past few years. The evolution to incorporate human aspects in the CPS research has unlocked a number of research challenges. Particularly human dynamics brings additional complexity that is yet to be explored. The exploration to conceptualise the notion of CPSS has been partially addressed in few scientific literatures. Although its conceptualisation has always been use-case dependent. Thus, there is a lack of generic view as most works focus on specific domains. Furthermore the systemic core and design principles linking it with the theory of systems are loose. This work aims at addressing these issues by first exploring and analysing scientific literatures to understand the complete spectrum of CPSS through a Systematic Literature Review (SLR). Thereby identifying the state-of-the-art perspectives on CPSS regarding definitions, underlining principles and application areas. Subsequently, based on the findings of the SLR, we propose a domain-independent definition and a meta-model for CPSS, grounded in the Theory of Systems. Finally a discussion on feasible future research directions is presented based on the systemic notion and the proposed meta-models

Data science for industry 4.0 and sustainability: a survey and analysis based on open data

The last few years have been marked by the transition of companies and organizations to more efficient, productive and leaner practices in their processes and systems. In the spectrum of Industry and Engineering, the successful transition to Industry 4.0 is a clear goal for many Small and Medium Enterprises (SMEs) and bigger-sized companies. However, there are economic, social and environmental challenges for this transition that require innovative approaches to overcome them. The starting point for the development of this dissertation is exploring the importance of Data as a crucial resource and Data-science as a tool for companies, organizations and even public institutions to achieve innovative solutions through collaboration. As it will be further explained, Data is essential in decision making, but in many cases, organizations can’t access relevant information and tools because they are either proprietary or because there is a lack of collaboration between them and third parties. There is a common misconception that competition between companies within the same industry prohibits them from collaborating with each other. However, many times data-sharing and collaborative approaches can actually benefit both of them, increase the market they operate in, and accelerate innovation. Even though the adoption of Industry 4.0 has been already underway, this transition cannot be considered successful unless it improves sustainability across the economic, social and environmental areas of society. Those three sustainable pillars should always be considered a priority in the research of industrial and engineering evolution. Today, more than ever before, information about those topics is widely available but there is still a lack of interest by scientists and scholars in studying some of them. The following research aims to study Industry 4.0 and Sustainability themes through Data Science by incorporating open data and leveraging open-source tools in order to achieve Sustainable Industry 4.0. For that, studying the trends and current state of Industry 4.0, Sustainability and open data in the world, as well as identifying the industries, regions, and enterprises that benefit the most from Industry 4.0 adoption, and understanding if openness of data has a positive impact on Social Sustainability are the main objectives of the study. For that are used methods such as SLR (Sistematic Literature Review) in the bibliographic review and quantitative analysis through open-source software such as Python and R in the development of the research. The main results show a positive trend in Industry 4.0 adoption through sustainable practices, mainly on developed countries, and a growing trend of openness of data, which can be positive for transparency in both Industry and Sustainability.Os últimos anos têm sido marcados pela transição por parte de empresas e organizações para práticas mais eficientes, produtivas e de menores desperdícios nos seus processos e sistemas. No espectro da Indústria e Engenharia, a transição bem sucedida para a Indústria 4.0 é um objetivo claro por várias Pequenas e Médias Empresas (PMEs) e também por empresas maiores. No entanto, existem desafios de cariz económico, social e ambiental para esta transição, que requerem abordagens inovadoras para que os mesmos sejam ultrapassados. O ponto de partida para o desenvolvimento desta dissertação passou por explorar a importância de Dados como um recurso crucial e da Ciência de Dados como uma ferramenta para empresas, organizações e até mesmo instituições públicas atingirem soluções inovadoras através de colaboração. Como será explicado ao longo da dissertação, os dados são essenciais em tomadas de decisão, mas em muitos casos, as organizações não conseguem aceder a informação ou ferramentas relevantes porque ou são proprietárias, ou porque existe a falta de colaboração entre elas e terceiros. Existe também o conceito errado de que a competição entre empresas numa dada indústria as proíbe de colaborarem entre si. No entanto, muitas vezes a partilha de informação e abordagens colaborativas podem, na verdade, beneficiar ambas, expandindo o mercado onde operam e acelerando inovação. Apesar da adoção da Indústria 4.0 estar em progresso, esta transição não pode ser considerada bem sucedida se não melhorar a sustentabilidade nas áreas económicas, sociais e ambientais da sociedade. Esses três pilares da sustentabilidade devem ser considerados uma prioridade no estudo da evolução industrial e da engenharia. Hoje, mais do que nunca, a informação acerca desses tópicos é facilmente acedida, mas continua a existir interesse por parte de cientistas e académicos no estudo de alguns deles. A presente pesquisa tenciona estudar a Indústria 4.0 e temas de Sustentabilidade através de Ciência de Dados, incorporando dados abertos e explorando ferramentas open-source, para contribuir para uma Indústria 4.0 Sustentável. Para tal, estudar a tendência e estado atual da Indústria 4.0, Sustentabilidade e abertura de dados no mundo, assim como identificar as indústrias, regiões e empresas que mais beneficiam desta adoção, e finalmente compreender se uma maior abertura de dados pode ter um impacto positivo na Sustentabilidade Social são os principais objetivos do estudo. Assim, são usados métodos como RSL (Revisão Sistemática da Literatura) na revisão bibliográfica e análise quantitativa através de software open-source como o Python e R nos capítulos de desenvolvimento. Os principais resultados mostram uma tendência positiva na adoção da Indústria 4.0 através de praticas sustentáveis, principalmente em países desenvolvidos, e uma tendência crescente na abertura de dados, que pode ser positiva para uma indústria mais sustentável e transparente

Cyber-Physical Embedded Systems with Transient Supervisory Command and Control: A Framework for Validating Safety Response in Automated Collision Avoidance Systems

The ability to design and engineer complex and dynamical Cyber-Physical Systems (CPS) requires a systematic view that requires a definition of level of automation intent for the system. Since CPS covers a diverse range of systemized implementations of smart and intelligent technologies networked within a system of systems (SoS), the terms “smart” and “intelligent” is frequently used in describing systems that perform complex operations with a reduced need of a human-agent. The difference between this research and most papers in publication on CPS is that most other research focuses on the performance of the CPS rather than on the correctness of its design. However, by using both human and machine agency at different levels of automation, or autonomy, the levels of automation have profound implications and affects to the reliability and safety of the CPS. The human-agent and the machine-agent are in a tidal lock of decision-making using both feedforward and feedback information flows in similar processes, where a transient shift within the level of automation when the CPS is operating can have undesired consequences. As CPS systems become more common, and higher levels of autonomy are embedded within them, the relationship between human-agent and machine-agent also becomes more complex, and the testing methodologies for verification and validation of performance and correctness also become more complex and less clear. A framework then is developed to help the practitioner to understand the difficulties and pitfalls of CPS designs and provides guidance to test engineering design of soft computational systems using combinations of modeling, simulation, and prototyping