Abstract Adaptive systems are designed to modify their behaviour at runtime in response to dynamically changing and open-ended environments as well as evolving requirements. Such systems may operate as individual adaptive entities or as collective adaptive systems composed of multiple collaborating components. Rigorous engineering of these systems requires appropriate methods, models, and tools that ensure reliability, correctness, and alignment with their intended purpose. This paper introduces the second part of the special section on Rigorous Engineering of Collective Adaptive Systems. It presents seven selected contributions and positions them within four major research directions: (i) Large Ensembles and Collective Dynamics, (ii) Knowledge, Consciousness and Emergence, (iii) Automated Reasoning for Better Interaction, and (iv) Analysing Collective Adaptive Systems. Together, they illustrate current progress and emerging challenges in the rigorous engineering of collective adaptive systems.

Abstract Adaptive systems are designed to modify their behaviour at runtime in response to dynamically changing and open-ended environments as well as evolving requirements. Such systems may operate as individual adaptive entities or as collective adaptive systems composed of multiple collaborating components. Rigorous engineering of these systems requires appropriate methods, models, and tools that ensure reliability, correctness, and alignment with their intended purpose. This paper introduces the first part of the special section on Rigorous Engineering of Collective Adaptive Systems. It presents six of the thirteen selected contributions and positions them within two major research directions: (i) Modelling and Engineering Collective Adaptive Systems, and (ii) Analysing Collective Adaptive Systems. Together, these contributions illustrate current progress and emerging challenges in the rigorous engineering of collective adaptive systems.

Abstract An adaptive system is able to adapt at runtime to dynamically changing environments and to new requirements. Adaptive systems can be single adaptive entities or collective ones that consist of several collaborating entities. Rigorous engineering requires appropriate methods and tools that help guaranteeing that an adaptive system lives up to its intended purpose. This paper introduces the special section on “Rigorous Engineering of Collective Adaptive Systems.” It presents the 11 contributions of the section categorizing them into five distinct research lines: correctness by design and synthesis, computing with bio-inspired communication, new system models, machine learning, and programming and analyzing ensembles.

Abstract An adaptive system is able to adapt at runtime to dynamically changing environments and to new requirements. Adaptive systems can be single adaptive entities or collective ones that consist of several collaborating entities. Rigorous engineering requires appropriate methods and tools that help guaranteeing that an adaptive system lives up to its intended purpose. This paper introduces the special section on “Rigorous Engineering of Collective Adaptive Systems.” It presents the seven contributions of the section and gives a short overview of the field of rigorously engineering collective adaptive systems by structuring it according to three topics: systematic development, methods and theories for modelling and analysis, and techniques for programming and operating collective adaptive systems.

The usage of large amounts of data has an immense potential for global economic growth and the competitiveness of countries with high technological standards. Vast amounts of data from different sources are collected and analyzed in order to seek economic profit and competitive advantages for companies and society in general. To gain profit from such data, it needs to be analyzed, processed, and interpreted. Thus, knowledge can be created and such generation of knowledge within the analysis and interpretation process constitutes the difference between "Big" and "Smart" Data. In this paper we present a taxonomy to develop standards in the field of Smart Data. It consists of 8 challenges that need to be addressed by standards and 13 fields of standardization.

In this paper, we present an object-oriented approach to the specification of discrete software controllers that are embedded in discrete-continuous (or hybrid) environments.The structure of the controller and its environment is specified using object notations extended to include continuous and hybrid objects.Control behavior is specified with state automata and pre/postconditions using the statechart notation and constructive Z-schemata.The behavior of the environment is specified '\\ith systems of differential equations using an object-oriented extension of Z for the specification of hybrid systems.We use a case study on control of a high-pressure steam boiler to illustrate how the environment structure can help to design the controller and how environment simulation can be used to derive control parameters.