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Full Description
This book provides an overview of the many approaches that can adopted for programming and deploying highly-adaptive and large-scale IoT systems, with a specific focus on the key results achieved within the Fluidware research project. The authors start by presenting Fluidware, which develops a novel programming model for IoT services and applications, along with the associated and robust large-scale IoT services and applications. Starting from previous findings in the areas of field-based coordination, collective adaptive systems, stream computing and aggregate computing, the authors address the complexity of building modern, large-scale IoT systems, by a full-fledged engineering approach revolving around a new notion of distributed programming. The authors show how the key innovative idea of Fluidware is to abstract collectives of devices of the IoT fabric as sources, digesters, and targets of distributed "flows" of contextualized events, carrying information about data produced and actuating commands. The book is divided into three parts: (i) the first part is a general introduction to the Fluidware ideas and to the key problems associated with programming IoT systems, (ii) the second part presents the key results achieved within the Fluidware project; (iii) the third part identified open challenges and research directions.
Contents
Part 1 Overview.- Chapter 1 Fluidware: an Approach towards Adaptive and Scalable IoT Systems.- Chapter 2 Programming approaches for large-scale IoT systems development: state of the art.- Part 2 Fluidware Technologies.- Chapter 3 Middleware Architectures for Fluid Computing.- Chapter 4 Towards an IoT-Oriented Software Engineering Methodology.- Chapter 5 A BPMN-based Approach for IoT Systems Engineering.- Chapter 6 Space-fluid and Time-fluid Programming.- Part 3 Visions.- Chapter 7 Fluidware Meets Digital Twins.- Chapter 8 Smart Collective Services in the Edge-Cloud Continuum based on a Simulation-driven
Toolchain.- Chapter 9 Envisioning Unpredictability in Smart Environments.- Chapter 10 Learning Opportunities in Collective Adaptive Systems.
