Description This book focuses on the design and testing of large-scale, distributed signal processing systems, with a special emphasis on systems architecture, tooling and best practices. Architecture modeling, model checking, model-based evaluation and model-based design optimization occupy central roles. Target systems with resource constraints on processing, communication or energy supply require non-trivial methodologies to model their non-functional requirements, such as timeliness, robustness, lifetime and "evolution" capacity.
Besides the theoretical foundations of the methodology, an engineering process and toolchain are described. The book can be used as a "cookbook" for designers and practitioners working with complex embedded systems like sensor networks for the structural integrity monitoring of steel bridges, and distributed micro-climate control systems for greenhouses and smart homes. Product details Format Hardback pages Dimensions x x Other books in this series. Add to basket. Internet of Everything Beniamino Di Martino.
Social Internet of Things Alessandro Soro. Back cover copy This book focuses on the design and testing of large-scale, distributed signal processing systems, with a special emphasis on systems architecture, tooling and best practices. His professional interest covered model-based signal processing and control, distributed real-time systems, multi-agent systems and sensor networks.
During the recent years he was involved in projects as system architect covering space robot arm path planning, a real-time simulator for multi-agent systems, control of intelligent transportation systems and wireless sensor network based monitoring. Georgios Exarchakos is an assistant professor of dependable communications.
His primary focus areas are complex network dynamics, internet of things, network resource management and smart cross layer optimizations. Georgios joined the Department of Electrical Engineering at Eindhoven University of Technology in as postdoctoral researcher of network management. Since , as assistant professor at the same department has managed two multinational EU projects and has been teaching computer networks and network overlays. George is the author of more than 50 journal articles and conference papers. He received his doctoral degree on peer-to-peer overlays from University of Surrey, Guildford in and his MSc degree from Imperial College London in Learn about new offers and get more deals by joining our newsletter.
Sign up now. An error-free CEI will consequently guarantee a system that fulfils its requirements at every time. Here we benefit from specific characteristics: we are able to reduce the relevant state space of the system to be tested tremendously and to gain a clear distinction between correct and incorrect behaviour out of the concepts of the CEI — essential for evaluating the tests.
SOAS are highly distributed systems, interacting locally to solve problems. This locality is exploited in our approach, since many test cases can focus on single agents or small agent groups the system is composed of; this focus makes it easier to execute and evaluate them. Furthermore, it is possible to use the test results of local agents and agent groups to make a statement about the whole system. Self-adaptation enables software to cope with changing and unpredictable environments. Control theory provides a variety of mathematically grounded techniques for adapting the behavior of dynamic systems.
While it has been applied to specific software control problems, it has proved difficult to define methodologies allowing non-experts to systematically apply control techniques to create adaptive software. However, at a convenient level of abstraction, broad classes of problems reveal similar behavioral properties, allowing for the automated identification of a suitable dynamic model and the synthesis of controllers with formally proved quality guarantees.
As many practical problems fall into treatable classes, automated control synthesis may draw in the near future a new path toward dependable software adaptation. To provide high performance under these changing conditions, distributed systems and communication systems have to implement adaptive behaviour. My research concentrates on design principles for such adaptive communication systems. Therefore, I am interested in the general description of adaptive behaviour, the possible configurations and compositions of mechanisms, as well as the concrete execution of transitions between these mechanisms.
I am focusing on different network layers, especially the application layer and network protocols. Modern software systems need to self-adapt while providing service in order to cope with the uncertain environment in which the systems operate, changing requirements and evolution occurring in the system themselves.
Consequently, trustworthy and dependable operation in these systems is very important. This is more evident when these systems are deployed in safety-critical or business-critical applications where they are expected to adhere to strict performance, resource usage and other quality-of-service QoS requirements. Runtime quantitative verification, a new approach for the analysis of systems exhibiting stochastic behaviour, is a key technology in achieving both adaptation and continual compliance with QoS requirements.
Nevertheless, existing runtime quantitative verification techniques suffer from significant overheads and cannot comply with the strict execution time and resource usage constraints that characterise most runtime scenarios.
The research we carry out aims at addressing some of these challenges. Ensemble-based component systems EBCS is a class of software component systems that features the modelling constructs of autonomous components and ad-hoc groups called ensembles. These constructs have proven suitable for the development of networked, highly dynamic cyber-physical systems, such as an intelligent decentralised car parking system or an emergency coordination system.
In this talk, I will focus on the problem of designing such systems so that their situation-specific system-level goals are consistently mapped to implementation-level artefacts. The method establishes traceability between requirements and architecture in EBCS, while the outcome of the method IRM-SA model can be used at runtime to provide system adaptation in form of architecture reconfiguration. Probabilistic verifiation play an important role for verifying the quality attributes such as reliability, safety and performance.
On problem however is, that system re-verification is needed whenever the software changes. Re-verifying of a changing model multiple times is expensive. However, substantial improvements have not yet been achieved for evaluating structural changes in the model. This talk discusses an incremental verification framework that allows efficient evaluation of the changing models.
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Then, I'll focus on software- energy-optimization in particular, including a technique to save energy by frequency scaling and an approach to assess and compare the energy consumption of mobile applications. Additionally, I'll present a concept and prototype to realize energy-neutral software systems. Finally, I'll discuss work in the area of model-driven robot software engineering.
The interest on distributed component-based software development is growing stronger due to, among other factors, the future Internet of Things IoT. In a vision of the IoT, the infrastructure is shared among many stakeholders who deploy their own components independently. As a result, no single stakeholder is able to centrally manage the computing resources and such a task must be carry on by the middleware. In a context where components share resources as memory and CPU cycles, the middleware must provide the each component with the resources it demands but it also must enforce quotas of consumption.
This research focuses in two aspects of the control loop, monitoring and execution of the plan. In particular, it aims at providing mechanisms for Java-based middleware to: i perform resource consumption monitoring, ii enforce resource reservation and, iii execute runtime reconfiguration to provided distributed resource reservation. We describe mechanisms to perform scalable resource consumption monitoring and reservation. We also discuss approaches to identify the source of a faulty behaviour regarding resource usage.
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Finally, we present our vision on distributed decision making about resource reservation and how the paradigm of Models runtime can be used as foundation for its implementation. The increased adoption of service-oriented technologies and cloud computing creates new challenges for the adaptation and evolution of long-living and software-intensive systems.
Software services and cloud platforms are owned and maintained by independent parties. Software engineers and system operators of these systems only have limited visibility and control over the third-party elements. Traditional monitoring provides software engineers and system operators with execution observation data which are used as basis to detect anomalies. If the services and the cloud platform are not owned and controlled by the system engineers, monitoring the execution is a challenging task. The aim of our research is to develop and validate advanced techniques which empower the system engineers to observe and detect upcoming quality flaws and anomalies of the execution of software-intensive systems.
For this, we extend and integrate previous work on adaptive monitoring, software system modeling and analysis. We apply models runtime as means to adjust the observation and anomaly detection techniques during system operation. For demonstrating the feasibility and potential benefits gained and for providing feedback to guide the research, we continuously evaluated our results using the established research benchmark CoCoME.
Increasingly complex socio-technical systems operate in continually changing environments. Such systems involve the interplay of human actors and technology. Changes in the context of the system might lead to changed user needs requiring a socio-technical system to adjust its behaviour. Due to uncertainty and increasingly changing operational environment such systems need to update their knowledge about end-user needs and context that influences the execution of end-user requirements.
While self-adaptive systems are designed to address the challenges of changing context, the uncertainty in the operational environment poses outstanding challenges to capture and evolve requirements at runtime.
By exploiting the concept of contextual requirements as composed of requirements and context as two entities that can evolve separately, my dissertation presents a framework for the analysis and discovery of contextual requirements at runtime. In three studies I explore the usefulness of existing techniques, i. In my presentation I will give a short overview of my PhD thesis. Control theory provides solid foundations and tools for designing and developing a reliable feedback control that drives software adaptations at runtime.
However, the integration of the resulting control mechanisms is usually left to an extensive handcrafting of a non-trivial implementation code. This is a challenging task when considering the variety and complexity of contemporary distributed computing systems. In our work, we aim to address this by providing flexible system-level abstractions in a form of runtime software models—i. Such models allow to seamlessly observe and modify the underlying systems without the need for coping with low-level implementation and infrastructure details.
Furthermore, techniques from model-driven engineering, such as model consistency checking and model transformations, can be used to systematically develop monitoring and reconfiguration parts of feedback control loops. As a result, this should allow researchers and engineers to experiment and to put easily in practice different self-adaptation mechanisms and policies.
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Self-adaptation is a promising approach to manage the complexity of modern software systems that are required to adapt themselves autonomously. The dynamic nature of changing and evolving requirements is an important aspect in these systems. This kind of systems must be able to adapt to these changes, being it necessary to have a representation of their own requirements definition and to process their changes at runtime.
Several results have shown that dynamic software product lines are feasible and useful in different software contexts including the modeling of requirements. From a literature review, we conclude that the modeling and characterization of requirements for SAS systems is an area that requires further exploration to be fully solved. With this project will work the in the definition of the requirements engineering phase in the software development life cycle for self-adaptive systems based on the language proposed by Sawyer et al.
With improvements on this language to propose a requirement framework aim at advancing the way of specifying requirements for SAS systems. Adaptive security systems aim to protect valuable assets in the face of changes in their operational environment.
A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems
They do so by monitoring and analysing this environment, and deploying security functions that satisfy some protection security, privacy, or forensic requirements. For adaptive security, topology expresses a rich representation of context that can provide a system with both structural and semantic awareness of important contextual characteristics. These include the location of assets being protected or the proximity of potentially threatening agents that might harm them. Security-related actions, such as the physical movement of an actor from a room to another in a building, may be viewed as topological changes.
By monitoring changes in topology at runtime one can identify new or changing threats and attacks, and deploy adequate security controls accordingly.
The talk elaborates on the notion of topology and provides a vision and research agenda on its role for systematically engineering systems that satisfy their security, privacy and forensic requirements. Self-organizing, adaptive systems SOAS such as those considered in Organic Computing benefit from redundancies introduced to assert a certain fault tolerance and the ability to stay functional in case of failures.
Resource allocation problems such as the distribution of a given predicted energy demand to a set of power plants are instances of tasks that can benefit from a self-organizing structure to deal with uncertainty and scalability. However, in order to control these systems or even understand their behaviour , we need to devise algorithms and techniques dealing with their complex nature.
Constraint programming and mathematical programming are successful paradigms designed to model a variety of satisfaction and optimization problems and solve them with generic algorithms. Therefore, a systematic integration of these declarative approaches into the engineering process of self-adaptive software is desirable.
We present several techniques that are designed to incorporate preferences by means of soft constraints, physical relational models to add a semantic layer over pure constraint models and abstraction techniques to scale systems using hierarchies. An overview of this approach has been published in the Proceedings of the Second Organic Computing Doctoral Dissertation Colloquium and is available online.
Cloud elasticity enables cloud services to automatically adapt to workload changes by dynamically allocating and de-allocating hardware resources. Hardware allocation is achieved by virtual machine replication and migration that adds, removes, and re-deploys service components within or across data centers and thus alternates components and their deployments during runtime. Dynamically replicated or migrated components being part of complex cloud services may transfer data even via intermediate components to locations that are excluded by privacy policies.
As dynamic adaptations are unpredictable during design time, changed compositions and deployments have to be observed and checked against privacy policies during runtime. In order to support the prevention of privacy violations, we focus on potential privacy violations that result from potential data transfers occurring once related cloud service functions are invoked. However, potential data flows are not observable, as they are not reflected in replication and migration events emitted by cloud infrastructures. This makes the detection of potential privacy violations problematic.
To tackle this problem, we explore the utilization of runtime models in order to reflect cloud services, their components, and interactions, which allows for reasoning on potential privacy violations among the cloud service composition. Based on the key idea of detecting privacy violations on runtime models, this talk will discuss two main technical challenges faced when putting the approach into practice and, further, points out how we address these challenges: a the challenge of observing cloud elasticity despite the limited visibility of cloud internals and b the challenge of checking realistic cloud services of large size and complexity.
The conception of efficient smart spaces requires a reliable framework for their design, implementation, testing, and deployment. The development life cycle of these smart spaces differs greatly from the conventional software systems, as it poses various challenges during each development phase such as: provision of appropriate design abstractions, integration and coordination of heterogeneous components, incremental evaluation and deployment of the system, and formation of ad-hoc network to cater dynamism in diverse smart spaces.
There have been numerous solutions proposed to solve different aspects related to smart spaces, but we still lack a conceptual framework that provides tools for the whole development life cycle. This work presents a generic and comprehensive development framework that allows the developer to move seamlessly from a fully virtual, simulated solution to a completely deployed system.
The framework uses a group-based organization for the coordination and cooperation among the heterogeneous components of a smart space. It also provides interfaces to surrogate certain sets of system components through external simulators, and ease the deployment of physical elements.
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