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Software engineers and researchers in the field are constantly developing new technologies to manage the complexity of current software systems. There is an increasing need for mechanisms that can deal with dynamics in the systems' environment, goals, and requirements. Self-adaptive software systems are a solution to manage the complexity caused by dynamics or runtime variations. Software reuse is a classical solution to deal with complexity and increase the quality of a system in a systematic and efficient way. Despite the large amount of research on self-adaptation, no systematic study has been found, which surveys and reports the application of reuse methods and techniques for the development of self-adaptive software systems. A systematic analysis of reuse methods and techniques for the development of self-adaptive systems is interesting as it provides useful insights for researchers and practitioners in the self-adaptive area. This study systematically reviews relevant research work published between the years 2000 and 2020 at eight well-known venues on self-adaptation and software engineering. By following the systematic literature review method, 97 studies were reviewed and 40 primary studies identified for addressing the research questions. The main objectives of the review are 1) to collect and analyse the reuse-based methods studied and applied for the design and development of self-adaptive software systems, 2) analyse the challenges in the application of reuse-based methods for the development of self-adaptive software systems. The review shows that most of the analysed studies support reuse with component-based software engineering. The primary studies propose different reuse-based methods to allow faster and simpler development of self-adaptive systems. Furthermore, the analysis shows that the reviewed studies report several challenges related to the configuration process, design, performance and uncertainty in the application of reuse methods for the development of self-adaptive systems.

Software engineers and researchers in the field are constantly developing new technologies to manage the complexity of current software systems. There is an increasing need for mechanisms that can deal with dynamics in the systems’ environment, goals, and requirements. Self-adaptive software systems are a solution to manage the complexity caused by dynamics or runtime variations. Software reuse is a classical solution to deal with complexity and increase the quality of a system in a systematic and efficient way. Despite the large amount of research on self-adaptation, no systematic study has been found, which surveys and reports the application of reuse methods and techniques for the development of self-adaptive software systems. A systematic analysis of reuse methods and techniques for the development of self-adaptive systems is interesting as it provides useful insights for researchers and practitioners in the self-adaptive area. This study systematically reviews relevant research work published between the years 2000 and 2020 at eight well-known venues on self-adaptation and software engineering. By following the systematic litera-ture review method, 97 studies were reviewed and 40 primary studies identi-fied for addressing the research questions. The main objectives of the review are 1) to collect and analyse the reuse-based methods studied and applied for the design and development of self-adaptive software systems, 2) analyse the challenges in the application of reuse-based methods for the development of self-adaptive software systems. The review shows that most of the analysed studies support reuse with component-based software engineering. The pri-mary studies propose different reuse-based methods to allow faster and sim-pler development of self-adaptive systems. Furthermore, the analysis shows that the reviewed studies report several challenges related to the configura-tion process, design, performance and uncertainty in the application of reuse methods for the development of self-adaptive systems.

Software-Defined Radio (SDR) is a large field of research, and is rapidly expanding in terms of capabilities and applications. As the number of SDR platforms, deployments, and use-cases grow, interoperability, compatibility, and software re-use becomes more difficult. Additionally, advanced SDR applications require more advanced hardware and software platforms to support them, necessitating intelligent management of resources and functionality. Realizing these goals can be done using the paradigms of Component-Based Design (CBD) and Service-Oriented Architectures (SOAs). Component-based design has been applied to the field of SDR in the past to varying levels of success. We discuss the benefits of CBD, and how to successfully use CBD for SDR. We assert that by strictly enforcing the principles of CBD, we can achieve a high level of independence from both the hardware and software platforms, and enable component compatibility and interoperability between SDR platforms and deployments. Using CBD, we also achieve the use-case of a fully distributed SDR, where multiple hardware nodes act as one cohesive radio unit. Applying the concept of service-orientation to SDR is a novel idea, and we discuss how this enables a new radio paradigm in the form of goal-oriented autonomic radios. We define SOAs in the context of SDR, explain how our vision is different than middle-wares like CORBA, describe how SOAs can be used, and discuss the possibilities of autonomic radio systems. This thesis also presents our work on the Cognitive Radio Open Source Systems (CROSS) project. CROSS is a free and open-source prototype architecture that uses CBD to achieve platform independence and distributed SDR deployments. CROSS also provides an experimental system for using SOAs in SDRs. Using our reference implementation of CROSS, we successfully demonstrated a distributed cognitive radio performing dynamic spectrum access to communicate with another SDR while avoiding an interferer operating in the spectrum. / Master of Science

Generative Multimedia (GM) applications are an increasingly popular way to implement interactive media performances. Our contributions include creating a metric for evaluating Generative Multimedia performances, designing a model for accepting perceiver preferences, and using those preferences to adapt GM performances. The metric used is imprecision, which is the ratio of the actual computation time of a GM element to the computation time of a complete version of that GM element. By taking a perceiver's preferences into account when making adaptation decisions, applications can produce GM performances that meet soft real-time and resource constraints while allocating imprecision to the GM elements the perceiver least cares about. Compared to other approaches, perceiver-directed imprecision best allocates impreciseness while minimizing delay.

Market globalization and mass customization requirements are forcing companies towards automation of their product development processes. Many task-specific software solutions provide localized automation. Coordinating these local solutions to automate higher-level processes requires significant software maintenance costs due to the incompatibility of the software tools and the dynamic nature of the product development environment. Current automation methods do not provide the required level of flexibility to operate in this dynamic environment. An autonomic product development process automation strategy is proposed in order to provide a flexible, standardized approach to product development process automation and to significantly reduce the software maintenance costs associated with traditional automation methods. Key elements of the strategy include a formal approach to decompose product development processes into services, a method to describe functional and quality attributes of services, a process modeling algorithm to configure processes composed of services, a method to evaluate process utility based on quality metrics and user preferences, and an implementation that allows a user to instantiate the optimal process. Because the framework allows a user to rapidly reconfigure and select optimal processes as new services are introduced or as requirements change, the framework should reduce burdensome software maintenance costs associated with traditional automation methods and provide a more flexible approach.

L’Intelligence Ambiamte (AmI) est un domaine de recherche investigant les techniques d’intelligence artificielle pour créer des environnements réactifs. Les réseaux de capteurs et effecteurs sans-fils sont les supports de communication entre les appareils ménagers, les services installés et les interfaces homme-machine. Cette thèse s’intéresse à la conception d’Environements Réactifs avec des propriétés autonomiques i.e. des systèmes qui ont la capacité de se gérer eux-même. De tels environements sont ouverts, à grande échelle, dynamique et hétérogène, ce qui induit certains problèmes pour leur gestion par des systèmes monolithiques. L’approche proposée est bio-inspirée en considérant chacune des plate-formes comme une cellule indépendente formant un organisme intelligent distribué. Chaque cellule est programmée par un processus ADN-RNA décrit par des règles réactives décrivant leur comportement interne et externe. Ces règles sont modelées par des agents mobiles ayant des capacités d’auto-réécriture et offrant ainsi des possibilités de reprogrammation dynamique. Le framework EMMA est composé d’un middleware modulaire avec une architecture orientée ressource basée sur la technologie 6LoWPAN et d’une architecture MAPE-K pour concevoir des AmI à plusieurs échelles. Les différentes relations entre les problèmes techniques et les besoins théoriques sont discutées dans cette thèse depuis les plate-formes, le réseau, le middleware, les agents mobiles, le déploiement des applications jusqu’au système intelligent. Deux algorithmes pour AmI sont proposés : un modèle de contrôleur neuronal artificiel pour le contrôle automatique des appareils ménagers avec des processus d’apprentissage ainsi qu’une procédure de vote distribuée pour synchroniser les décisions de plusieurs composants systèmes. / AThe Ambient Intelligence (AmI) is a research area investigating AI techniques to create Responsive Environments (RE). Wireless Sensor and Actor Network (WSAN) are the supports for communications between the appliances, the deployed services and Human Computer Interface (HCI). This thesis focuses on the design of RE with autonomic properties i.e. system that have the ability to manage themselves. Such environments are open, large scale, dynamic and heterogeneous which induce some difficulties in their management by monolithic system. The bio-inspired proposal considers all devices like independent cells forming an intelligent distributed organism. Each cell is programmed by a DNA-RNA process composed of reactive rules describing its internal and external behaviour. These rules are modelled by reactive agents with self-rewriting features offering dynamic reprogramming abilities. The EMMA framework is composed of a modular Resource Oriented Architecture (ROA) Middleware based on IPv6 LoW Power Wireless Area Networks (6LoWPAN) technology and a MAPE-K architecture to design multi-scale AmI. The different relations between technical issues and theoretical requirements are discussed through the platforms, the network, the middleware, the mobile agents, the application deployment to the intelligent system. Two algorithms for AmI are proposed: an Artificial Neural Controller (ANC) model for automatic control of appliances with learning processes and a distributed Voting Procedures (VP) to synchronize the decisions of several system components over the WSAN.

Cyber-physical systems are networked through IT infrastructure and susceptible to malware. Threats targeting process control are much more safety-critical than traditional computing systems since they jeopardize the integrity of physical infrastructure. Existing defence mechanisms address security at the network nodes but do not protect the physical infrastructure if network integrity is compromised. An interface guardian architecture is implemented on cyber-physical control leaf nodes to maintain process integrity by enforcing high-level safety and stability policies. Preemptive detection schemes are implemented to monitor process behavior and anticipate malicious activity before process safety and stability are compromised. Autonomic properties are employed to automatically protect process integrity by initiating switch-over to a verified backup controller. Subsystems adhere to strict trust requirements safeguarding them from adversarial intrusion. The preemptive detection schemes, switch-over logic, backup controller, and process communication are all trusted components that are separated from the untrusted production controller. The proposed architecture is applied to a rotary inverted pendulum experiment and implemented on a Xilinx Zynq-7000 configurable SoC. The leaf node implementation is integrated into a cyber-physical control topology. Simulated attack scenarios show strengthened resilience to both network integrity and reconfiguration attacks. Threats attempting to disrupt process behavior are successfully thwarted by having a backup controller maintain process stability. The system ensures both safety and liveness properties even under adversarial conditions. / Master of Science