ASAS: An Approach to Support Simulation of Smart Systems

Smart systems, such as smart cities, smart buildings, and autonomous cars, have recently gained increasing popularity. Each such system is essentially a System-of-Systems (SoS). SoS are dynamically established as alliances among independent and heterogeneous software systems to offer complex functionalities as a result of constituents interoperability. An SoS often supports critical application domains, and, as such, must be reliable. Many SoS have been specified and evaluated for their correct operation using static models. However, speciï¬cation languages have not supported to capture their inherent dynamic nature nor enabled to monitor their operation. The main contribution of this paper is to present ASAS, an approach to Automatically generate Simulation models for smArt Systems (ASAS) in order to support evaluation of their operation. In particular, our approach makes it possible to transform formal models of the SoS architecture (expressed in SoSADL) into simulation models (expressed in DEVS). We evaluated our approach by conducting two case studies using a flood monitoring system that is intended to be part of a smart city. Results indicate that ASAS can successfully generate functional simulations for the SoS operation, which in turn can enable to reason and monitor an SoS operation, taking into account its dynamic nature

Capturing functional and non-functional connector

The CONNECT Integrated Project aims to develop a novel networking infrastructure that will support composition of networked systems with on-the-fly connector synthesis. The role of this work package is to investigate the foundations and verification methods for composable connectors. In this deliverable, we set the scene for the formulation of the modelling framework by surveying existing connector modelling formalisms. We covered not only classical connector algebra formalisms, but also, where appropriate, their corresponding quantitative extensions. All formalisms have been evaluated against a set of key dimensions of interest agreed upon in the CONNECT project. Based on these investigations, we concluded that none of the modelling formalisms available at present satisfy our eight dimensions. We will use the outcome of the survey to guide the formulation of a compositional modelling formalism tailored to the specific requirements of the CONNECT project. Furthermore, we considered the range of non-functional properties that are of interest to CONNECT, and reviewed existing specification formalisms for capturing them, together with the corresponding modelchecking algorithms and tool support. Consequently, we described the scientific advances concerning model-checking algorithms and tools, which are partial contribution towards future deliverables: an approach for online verification (part of D2.2), automated abstraction-refinement for probabilistic realtime systems (part of D2.2 and D2.4), and compositional probabilistic verification within PRISM, to serve as a foundation of future research on quantitative assume-guarantee compositional reasoning (part of D2.2 and D2.4)

Increasing the Resilience of Cyber Physical Systems in Smart Grid Environments using Dynamic Cells

Resilience is an important system property that relies on the ability of a system to automatically recover from a degraded state so as to continue providing its services. Resilient systems have the means of detecting faults and failures with the added capability of automatically restoring their normal operations. Mastering resilience in the domain of Cyber-Physical Systems is challenging due to the interdependence of hybrid hardware and software components, along with physical limitations, laws, regulations and standards, among others. In order to overcome these challenges, this paper presents a modeling approach, based on the concept of Dynamic Cells, tailored to the management of Smart Grids. Additionally, a heuristic algorithm that works on top of the proposed modeling approach, to find resilient configurations, has been defined and implemented. More specifically, the model supports a flexible representation of Smart Grids and the algorithm is able to manage, at different abstraction levels, the resource consumption of individual grid elements on the presence of failures and faults. Finally, the proposal is evaluated in a test scenario where the effectiveness of such approach, when dealing with complex scenarios where adequate solutions are difficult to find, is shown

Designing Digital Work

Combining theory, methodology and tools, this open access book illustrates how to guide innovation in today’s digitized business environment. Highlighting the importance of human knowledge and experience in implementing business processes, the authors take a conceptual perspective to explore the challenges and issues currently facing organizations. Subsequent chapters put these concepts into practice, discussing instruments that can be used to support the articulation and alignment of knowledge within work processes. A timely and comprehensive set of tools and case studies, this book is essential reading for those researching innovation and digitization, organization and business strategy

Agoric computation: trust and cyber-physical systems

In the past two decades advances in miniaturisation and economies of scale have led to the emergence of billions of connected components that have provided both a spur and a blueprint for the development of smart products acting in specialised environments which are uniquely identifiable, localisable, and capable of autonomy. Adopting the computational perspective of multi-agent systems (MAS) as a technological abstraction married with the engineering perspective of cyber-physical systems (CPS) has provided fertile ground for designing, developing and deploying software applications in smart automated context such as manufacturing, power grids, avionics, healthcare and logistics, capable of being decentralised, intelligent, reconfigurable, modular, flexible, robust, adaptive and responsive. Current agent technologies are, however, ill suited for information-based environments, making it difficult to formalise and implement multiagent systems based on inherently dynamical functional concepts such as trust and reliability, which present special challenges when scaling from small to large systems of agents. To overcome such challenges, it is useful to adopt a unified approach which we term agoric computation, integrating logical, mathematical and programming concepts towards the development of agent-based solutions based on recursive, compositional principles, where smaller systems feed via directed information flows into larger hierarchical systems that define their global environment. Considering information as an integral part of the environment naturally defines a web of operations where components of a systems are wired in some way and each set of inputs and outputs are allowed to carry some value. These operations are stateless abstractions and procedures that act on some stateful cells that cumulate partial information, and it is possible to compose such abstractions into higher-level ones, using a publish-and-subscribe interaction model that keeps track of update messages between abstractions and values in the data. In this thesis we review the logical and mathematical basis of such abstractions and take steps towards the software implementation of agoric modelling as a framework for simulation and verification of the reliability of increasingly complex systems, and report on experimental results related to a few select applications, such as stigmergic interaction in mobile robotics, integrating raw data into agent perceptions, trust and trustworthiness in orchestrated open systems, computing the epistemic cost of trust when reasoning in networks of agents seeded with contradictory information, and trust models for distributed ledgers in the Internet of Things (IoT); and provide a roadmap for future developments of our research

Second Generation General System Theory: Perspectives in Philosophy and Approaches in Complex Systems

Following the classical work of Norbert Wiener, Ross Ashby, Ludwig von Bertalanffy and many others, the concept of System has been elaborated in different disciplinary fields, allowing interdisciplinary approaches in areas such as Physics, Biology, Chemistry, Cognitive Science, Economics, Engineering, Social Sciences, Mathematics, Medicine, Artificial Intelligence, and Philosophy. The new challenge of Complexity and Emergence has made the concept of System even more relevant to the study of problems with high contextuality. This Special Issue focuses on the nature of new problems arising from the study and modelling of complexity, their eventual common aspects, properties and approaches—already partially considered by different disciplines—as well as focusing on new, possibly unitary, theoretical frameworks. This Special Issue aims to introduce fresh impetus into systems research when the possible detection and correction of mistakes require the development of new knowledge. This book contains contributions presenting new approaches and results, problems and proposals. The context is an interdisciplinary framework dealing, in order, with electronic engineering problems; the problem of the observer; transdisciplinarity; problems of organised complexity; theoretical incompleteness; design of digital systems in a user-centred way; reaction networks as a framework for systems modelling; emergence of a stable system in reaction networks; emergence at the fundamental systems level; behavioural realization of memoryless functions

Visual analytics for relationships in scientific data

Domain scientists hope to address grand scientific challenges by exploring the abundance of data generated and made available through modern high-throughput techniques. Typical scientific investigations can make use of novel visualization tools that enable dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters. These general tools should be applicable to many disciplines: allowing biologists to develop an intuitive understanding of the structure of coexpression networks and discover genes that reside in critical positions of biological pathways, intelligence analysts to decompose social networks, and climate scientists to model extrapolate future climate conditions. By using a graph as a universal data representation of correlation, our novel visualization tool employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool integrates techniques such as graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by querying an optimized B-tree, dynamic level-of-detail graph abstraction, and template-based fuzzy classification using neural networks. We demonstrate our system using real-world workflows from several large-scale studies. Parallel coordinates has proven to be a scalable visualization and navigation framework for multivariate data. However, when data with thousands of variables are at hand, we do not have a comprehensive solution to select the right set of variables and order them to uncover important or potentially insightful patterns. We present algorithms to rank axes based upon the importance of bivariate relationships among the variables and showcase the efficacy of the proposed system by demonstrating autonomous detection of patterns in a modern large-scale dataset of time-varying climate simulation