This dissertation presents the theory, design, implementation and successful deployment of a cognitive engine decision algorithm by which a cognitive radio-equipped mobile robot may adapt its motion and radio parameters through multi-objective optimization. This provides a proof-of-concept prototype cognitive system that is aware of its environment, its user's needs, and the rules governing its operation. It is to take intelligent action based on this awareness to optimize its performance across both the mobility and radio domains while learning from experience and responding intelligently to ongoing environmental mission changes. The prototype combines the key features of cognitive radios and autonomous vehicles into a single package whose behavior integrates the essential features of both. The use case for this research is a scenario where a small unmanned aerial vehicle (UAV) is traversing a nominally cyclic or repeating flight path (an â •orbitâ •) seeking to observe targets and where possible avoid hostile agents. As the UAV traverses the path, it experiences varying RF effects, including multipath propagation and terrain shadowing. The goal is to provide the capability for the UAV to learn the flight path with respect both to motion and RF characteristics and modify radio parameters and flight characteristics proactively to optimize performance. Using sensor fusion techniques to develop situational awareness, the UAV should be able to adapt its motion or communication based on knowledge of (but not limited to) physical location, radio performance, and channel conditions. Using sensor information from RF and mobility domains, the UAV uses the mission objectives and its knowledge of the world to decide on a course of action. The UAV develops and executes a multi-domain action; action that crosses domains, such as changing RF power and increasing its speed. This research is based on a simple observation, namely that cognitive radios and autonomous vehicles perform similar tasks, albeit in different domains. Both analyze their environment, make and execute a decision, evaluate the result (learn from experience), and repeat as required. This observation led directly to the creation of a single intelligent agent combining cognitive radio and autonomous vehicle intelligence with the ability to leverage flexibility in the radio frequency (RF) and motion domains. Using a single intelligent agent to optimize decision making across both mobility and radio domains is unified multi-domain decision making (UMDDM). / Ph. D.

Since the advent of the computer in the late 1950s, scientists and engineers have pushed the limits of the computing power available to them to solve physical problems via computational simulations. Early computer languages evaluated program logic in a sequential manner, thereby forcing the designer to think of the problem solution in terms of a sequential process. Object-oriented analysis and design have introduced new concepts for solving systems of engineering problems. The term object-oriented was first introduced by Alan Kay [1] in the late 1960s; however, mainstream incorporation of object-oriented programming did not occur until the mid- to late 1990s. The principles and methods underlying object-oriented programming center around objects that communicate with one another and work together to model the physical system. Program functions and data are grouped together to represent the objects. This dissertation extends object-oriented modeling concepts to model algorithms in a generic manner for solving interconnected, multi-domain problems. This work is based on an extension of Graph Trace Analysis (GTA) which was originally developed in the 1990's for power distribution system design. Because of GTA's ability to combine and restructure analysis methodologies from a variety of problem domains, it is now being used for integrated power distribution and transmission system design, operations and control. Over the last few years research has begun to formalize GTA into a multidiscipline approach that uses generic algorithms and a common model-based analysis framework. This dissertation provides an overview of the concepts used in GTA, and then discusses the main problems and potential generic algorithm based solutions associated with design and control of interdependent reconfigurable systems. These include: • Decoupling analysis into distinct component and system level equations. • Using iterator based topology management and algorithms instead of matrices. • Using composition to implement polymorphism and simplify data management. • Using dependency components to structure analysis across different systems types. • Defining component level equations for power, gas and fluid systems in terms of across and though variables. This dissertation presents a methodology for solving interdependent, multi-domain networks with generic algorithms. The methodology enables modeling of very large systems and the solution of the systems can be accomplished without the need for matrix solvers. The solution technique incorporates a binary search algorithm for accelerating the solution of looped systems. Introduction of generic algorithms enables the system solver to be written such that it is independent of the system type. Example fluid and electrical systems are solved to illustrate the generic nature of the approach. / Ph. D.

5G and beyond mobile network technology promises to deliver unprecedented ultra-low latency and high data rates, paving the way for many novel applications and services. Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC) are two technologies expected to play a vital role in achieving ambitious Quality of Service requirements of such applications. While NFV provides flexibility by enabling network functions to be dynamically deployed and inter-connected to realize Service Function Chains (SFC), MEC brings the computing capability to the mobile network's edges, thus reducing latency and alleviating the transport network load. However, adequate mechanisms are needed to meet the dynamically changing network service demands (i.e., in single and multiple domains) and optimally utilize the network resources while ensuring that the end-to-end latency requirement of services is always satisfied. In this dissertation work, we break the problem into three separate stages and present the solutions for each one of them.Firstly, we apply Artificial Intelligence (AI) techniques to drive NFV resource orchestration in MEC-enabled 5G architectures for single and multi-domain scenarios. We propose three deep learning approaches to perform horizontal and vertical Virtual Network Function (VNF) auto-scaling: (i) Multilayer Perceptron (MLP) classification and regression (single-domain), (ii) Centralized Artificial Neural Network (ANN), centralized Long-Short Term Memory (LSTM) and centralized Convolutional Neural Network-LSTM (CNN-LSTM) (single-domain), and (iii) Federated ANN, federated LSTM and federated CNN-LSTM (multi-domain). We evaluate the performance of each of these deep learning models trained over a commercial network operator dataset and investigate the pros and cons of different approaches for VNF auto-scaling. For the first approach, our results show that both MLP classifier and MLP regressor models have strong predicting capability for auto-scaling. However, MLP regressor outperforms MLP classifier in terms of accuracy. For the second approach (one-step prediction), CNN-LSTM performs the best for the QoS-prioritized objective and LSTM performs the best for the cost-prioritized objective. For the second approach (multi-step prediction), the encoder-decoder CNN-LSTM model outperforms the encoder-decoder LSTM model for both QoS and Cost prioritized objectives. For the third approach, both federated LSTM and federated CNN-LSTM models perform equally better than the federated ANN model. It was also noted that in general federated learning approaches performs poorly compared to centralized learning approaches. Secondly, we employ Integer Linear Programming (ILP) techniques to formulate and solve a joint user association and SFC placement problem, where each SFC represents a service requested by a user with end-to-end latency and data rate requirements. We also develop a comprehensive end-to-end latency model considering radio delay, backhaul network delay and SFC processing delay for 5G mobile networks. We evaluated the proposed model using simulations based on real-operator network topology and real-world latency values. Our results show that the average end-to-end latency reduces significantly when SFCs are placed at the ME hosts according to their latency and data rate demands. Furthermore, we propose an heuristic algorithm to address the issue of scalability in ILP, that can solve the above association/mapping problem in seconds rather than hours.Finally, we introduce lightMEC - a lightweight MEC platform for deploying mobile edge computing functionalities which allows hosting of low-latency and bandwidth-intensive applications at the network edge. Measurements conducted over a real-life test demonstrated that lightMEC could actually support practical MEC applications without requiring any change to existing mobile network nodes' functionality in the access and core network segments. The significant benefits of adopting the proposed architecture are analyzed based on a proof-of-concept demonstration of the content caching use case. Furthermore, we introduce the AI-driven Kubernetes orchestration prototype that we implemented by leveraging the lightMEC platform and assess the performance of the proposed deep learning models (from stage 1) in an experimental setup. The prototype evaluations confirm the simulation results achieved in stage 1 of the thesis.

No description available.

No description available.

This research investigates a novel arrangement to pneumatic suspensions that are commonly used in heavy trucks, toward providing a dynamically balanced system that resists body roll and provides added roll stability to the vehicle. The new suspension, referred to as "balanced suspension," is implemented by retrofitting a conventional pneumatic suspension with two leveling valves and a symmetric plumbing arrangement to provide a balanced airflow and air pressure in the airsprings. This new design contributes to a balanced force distribution among the axles, which enables the suspension to maintain the body in a leveled position both statically and dynamically. This is in contrast to conventional heavy truck pneumatic suspensions that are mainly adjusted quasi-statically to level the body in response to load variations. The main objectives of the research are to discover and analyze the effects of various pneumatic components on the suspension dynamic response and numerically study the benefits of the pneumatically balanced suspension system. A pneumatic suspension model is established to capture the details of airsprings, leveling valves, check valves, pipes, and air tank based on the laws of fluid mechanics and thermodynamics. Experiments are designed and conducted to help determine and verify the modeling parameters and components. Co-simulation technique is applied to establish a multi-domain model that couples highly non-linear fluid dynamics of the pneumatic suspension with complex multi-body dynamics of an articulated vehicle. The model is used to extensively study effects of pneumatic balanced control of the suspensions on the tractor and trailer combination dynamics. The simulations indicate that the dual leveling valve arrangement of the balanced suspension provides better adjustments to the body roll by charging the airsprings on the jounce side, while purging air from the rebound side. Such an adjustment allows maintaining a larger difference in suspension force from side to side, which resists the vehicle sway and levels the truck body during cornering. Additionally, the balanced suspension better equalizes the front and rear drive axle air pressures, for a better dynamic load sharing and pitch control. It is evident from the simulation results that the balanced suspension increases roll stiffness without affecting vertical stiffness, and thereby it can serve as an anti-roll bar that results in a more stable body roll during steering maneuvers. Moreover, the Failure Mode and Effects Analysis (FMEA) study suggests that when one side of the balanced suspension fails, the other side acts to compensate for the failure. On the other hand, if the trailer is also equipped with dual leveling valves, such an arrangement will bring an additional stabilizing effect to the vehicle in case of the tractor suspension failure. The overall research results presented show that significant improvements on vehicle roll dynamics and suspension dynamic responsiveness can be achieved from the balanced suspension system. / PHD

Many critical systems are based on the combination of components from different physical domains (e.g. mechanical, electrical, hydraulic), and are mathematically modeled as Switched Multi-Domain Kirchhoff Networks (SMDKN). In this thesis, we tackle a major obstacle to formal verification of SMDKN, namely devising a global model amenable to verification in the form of a Hybrid Automaton. This requires the combination of the local dynamics of the components, expressed as Differential Algebraic Equations, according to Kirchhoff's laws, depending on the (exponentially many) operation modes of the network. We propose an automated SMT-based method to analyze networks from multiple physical domains, detecting which modes induce invalid (i.e. inconsistent) constraints, and to produce a Hybrid Automaton model that accurately describes, in terms of Ordinary Differential Equations, the system evolution in the valid modes, catching also the possible non-deterministic behaviors. The experimental evaluation demonstrates that the proposed approach allows several complex multi-domain systems to be formally analyzed and model checked against various system requirements.

Software development is a complex task. Executable applications comprise a mutlitude of diverse components that are developed with various frameworks, libraries, or communication platforms. The technical complexity in development retains resources, hampers efficient problem solving, and thus increases the overall cost of software production. Another significant challenge in market-driven software engineering is the variety of customer needs. It necessitates a maximum of flexibility in software implementations to facilitate the deployment of different products that are based on one single core. To reduce technical complexity, the paradigm of Model-Driven Software Development (MDSD) facilitates the abstract specification of software based on modeling languages. Corresponding models are used to generate actual programming code without the need for creating manually written, error-prone assets. Modeling languages that are tailored towards a particular domain are called domain-specific languages (DSLs). Domain-specific modeling (DSM) approximates technical solutions with intentional problems and fosters the unfolding of specialized expertise. To cope with feature diversity in applications, the Software Product Line Engineering (SPLE) community provides means for the management of variability in software products, such as feature models and appropriate tools for mapping features to implementation assets. Model-driven development, domain-specific modeling, and the dedicated management of variability in SPLE are vital for the success of software enterprises. Yet, these paradigms exist in isolation and need to be integrated in order to exhaust the advantages of every single approach. In this thesis, we propose a way to do so. We introduce the paradigm of Multi-Domain Engineering (MDE) which means model-driven development with multiple domain-specific languages in variability-intensive scenarios. MDE strongly emphasize the advantages of MDSD with multiple DSLs as a neccessity for efficiency in software development and treats the paradigm of SPLE as indispensable means to achieve a maximum degree of reuse and flexibility. We present HybridMDSD as our solution approach to implement the MDE paradigm. The core idea of HybidMDSD is to capture the semantics of particular DSLs based on properly defined semantics for software models contained in a central upper ontology. Then, the resulting semantic foundation can be used to establish references between arbitrary domain-specific models (DSMs) and sophisticated instance level reasoning ensures integrity and allows to handle partiucular change adaptation scenarios. Moreover, we present an approach to automatically generate composition code that integrates generated assets from separate DSLs. All necessary development tasks are arranged in a comprehensive development process. Finally, we validate the introduced approach with a profound prototypical implementation and an industrial-scale case study. / Softwareentwicklung ist komplex: ausführbare Anwendungen beinhalten und vereinen eine Vielzahl an Komponenten, die mit unterschiedlichen Frameworks, Bibliotheken oder Kommunikationsplattformen entwickelt werden. Die technische Komplexität in der Entwicklung bindet Ressourcen, verhindert effiziente Problemlösung und führt zu insgesamt hohen Kosten bei der Produktion von Software. Zusätzliche Herausforderungen entstehen durch die Vielfalt und Unterschiedlichkeit an Kundenwünschen, die der Entwicklung ein hohes Maß an Flexibilität in Software-Implementierungen abverlangen und die Auslieferung verschiedener Produkte auf Grundlage einer Basis-Implementierung nötig machen. Zur Reduktion der technischen Komplexität bietet sich das Paradigma der modellgetriebenen Softwareentwicklung (MDSD) an. Software-Spezifikationen in Form abstrakter Modelle werden hier verwendet um Programmcode zu generieren, was die fehleranfällige, manuelle Programmierung ähnlicher Komponenten überflüssig macht. Modellierungssprachen, die auf eine bestimmte Problemdomäne zugeschnitten sind, nennt man domänenspezifische Sprachen (DSLs). Domänenspezifische Modellierung (DSM) vereint technische Lösungen mit intentionalen Problemen und ermöglicht die Entfaltung spezialisierter Expertise. Um der Funktionsvielfalt in Software Herr zu werden, bietet der Forschungszweig der Softwareproduktlinienentwicklung (SPLE) verschiedene Mittel zur Verwaltung von Variabilität in Software-Produkten an. Hierzu zählen Feature-Modelle sowie passende Werkzeuge, um Features auf Implementierungsbestandteile abzubilden. Modellgetriebene Entwicklung, domänenspezifische Modellierung und eine spezielle Handhabung von Variabilität in Softwareproduktlinien sind von entscheidender Bedeutung für den Erfolg von Softwarefirmen. Zur Zeit bestehen diese Paradigmen losgelöst voneinander und müssen integriert werden, damit die Vorteile jedes einzelnen für die Gesamtheit der Softwareentwicklung entfaltet werden können. In dieser Arbeit wird ein Ansatz vorgestellt, der dies ermöglicht. Es wird das Multi-Domain Engineering Paradigma (MDE) eingeführt, welches die modellgetriebene Softwareentwicklung mit mehreren domänenspezifischen Sprachen in variabilitätszentrierten Szenarien beschreibt. MDE stellt die Vorteile modellgetriebener Entwicklung mit mehreren DSLs als eine Notwendigkeit für Effizienz in der Entwicklung heraus und betrachtet das SPLE-Paradigma als unabdingbares Mittel um ein Maximum an Wiederverwendbarkeit und Flexibilität zu erzielen. In der Arbeit wird ein Ansatz zur Implementierung des MDE-Paradigmas, mit dem Namen HybridMDSD, vorgestellt.

Software development is a complex task. Executable applications comprise a mutlitude of diverse components that are developed with various frameworks, libraries, or communication platforms. The technical complexity in development retains resources, hampers efficient problem solving, and thus increases the overall cost of software production. Another significant challenge in market-driven software engineering is the variety of customer needs. It necessitates a maximum of flexibility in software implementations to facilitate the deployment of different products that are based on one single core. To reduce technical complexity, the paradigm of Model-Driven Software Development (MDSD) facilitates the abstract specification of software based on modeling languages. Corresponding models are used to generate actual programming code without the need for creating manually written, error-prone assets. Modeling languages that are tailored towards a particular domain are called domain-specific languages (DSLs). Domain-specific modeling (DSM) approximates technical solutions with intentional problems and fosters the unfolding of specialized expertise. To cope with feature diversity in applications, the Software Product Line Engineering (SPLE) community provides means for the management of variability in software products, such as feature models and appropriate tools for mapping features to implementation assets. Model-driven development, domain-specific modeling, and the dedicated management of variability in SPLE are vital for the success of software enterprises. Yet, these paradigms exist in isolation and need to be integrated in order to exhaust the advantages of every single approach. In this thesis, we propose a way to do so. We introduce the paradigm of Multi-Domain Engineering (MDE) which means model-driven development with multiple domain-specific languages in variability-intensive scenarios. MDE strongly emphasize the advantages of MDSD with multiple DSLs as a neccessity for efficiency in software development and treats the paradigm of SPLE as indispensable means to achieve a maximum degree of reuse and flexibility. We present HybridMDSD as our solution approach to implement the MDE paradigm. The core idea of HybidMDSD is to capture the semantics of particular DSLs based on properly defined semantics for software models contained in a central upper ontology. Then, the resulting semantic foundation can be used to establish references between arbitrary domain-specific models (DSMs) and sophisticated instance level reasoning ensures integrity and allows to handle partiucular change adaptation scenarios. Moreover, we present an approach to automatically generate composition code that integrates generated assets from separate DSLs. All necessary development tasks are arranged in a comprehensive development process. Finally, we validate the introduced approach with a profound prototypical implementation and an industrial-scale case study. / Softwareentwicklung ist komplex: ausführbare Anwendungen beinhalten und vereinen eine Vielzahl an Komponenten, die mit unterschiedlichen Frameworks, Bibliotheken oder Kommunikationsplattformen entwickelt werden. Die technische Komplexität in der Entwicklung bindet Ressourcen, verhindert effiziente Problemlösung und führt zu insgesamt hohen Kosten bei der Produktion von Software. Zusätzliche Herausforderungen entstehen durch die Vielfalt und Unterschiedlichkeit an Kundenwünschen, die der Entwicklung ein hohes Maß an Flexibilität in Software-Implementierungen abverlangen und die Auslieferung verschiedener Produkte auf Grundlage einer Basis-Implementierung nötig machen. Zur Reduktion der technischen Komplexität bietet sich das Paradigma der modellgetriebenen Softwareentwicklung (MDSD) an. Software-Spezifikationen in Form abstrakter Modelle werden hier verwendet um Programmcode zu generieren, was die fehleranfällige, manuelle Programmierung ähnlicher Komponenten überflüssig macht. Modellierungssprachen, die auf eine bestimmte Problemdomäne zugeschnitten sind, nennt man domänenspezifische Sprachen (DSLs). Domänenspezifische Modellierung (DSM) vereint technische Lösungen mit intentionalen Problemen und ermöglicht die Entfaltung spezialisierter Expertise. Um der Funktionsvielfalt in Software Herr zu werden, bietet der Forschungszweig der Softwareproduktlinienentwicklung (SPLE) verschiedene Mittel zur Verwaltung von Variabilität in Software-Produkten an. Hierzu zählen Feature-Modelle sowie passende Werkzeuge, um Features auf Implementierungsbestandteile abzubilden. Modellgetriebene Entwicklung, domänenspezifische Modellierung und eine spezielle Handhabung von Variabilität in Softwareproduktlinien sind von entscheidender Bedeutung für den Erfolg von Softwarefirmen. Zur Zeit bestehen diese Paradigmen losgelöst voneinander und müssen integriert werden, damit die Vorteile jedes einzelnen für die Gesamtheit der Softwareentwicklung entfaltet werden können. In dieser Arbeit wird ein Ansatz vorgestellt, der dies ermöglicht. Es wird das Multi-Domain Engineering Paradigma (MDE) eingeführt, welches die modellgetriebene Softwareentwicklung mit mehreren domänenspezifischen Sprachen in variabilitätszentrierten Szenarien beschreibt. MDE stellt die Vorteile modellgetriebener Entwicklung mit mehreren DSLs als eine Notwendigkeit für Effizienz in der Entwicklung heraus und betrachtet das SPLE-Paradigma als unabdingbares Mittel um ein Maximum an Wiederverwendbarkeit und Flexibilität zu erzielen. In der Arbeit wird ein Ansatz zur Implementierung des MDE-Paradigmas, mit dem Namen HybridMDSD, vorgestellt.

La virtualisation de réseau permet de créer des réseaux logiques, dits virtuels sur un réseau physique partagé dit substrat. Pour ce faire, le problème d’allocation des ressources aux réseaux virtuels doit être résolu efficacement. Appelé VNE (Virtual Network Embedding), ce problème consiste à faire correspondre à chaque nœud virtuel un nœud substrat d’un côté, et de l’autre, à tout lien virtuel un ou plusieurs chemins substrat, de manière à optimiser un objectif tout en satisfaisant un ensemble de contraintes. Les ressources de calcul des nœuds et les ressources de bande passante des liens sont souvent optimisées dans un seul réseau substrat. Dans le contexte multi-domaine où la connaissance de l’information de routage est incomplète, l’optimisation des ressources de nœuds et de liens est difficile et souvent impossible à atteindre. Par ailleurs, pour assurer la continuité de service même après une panne, le VNE doit être réalisé de manière à faire face aux pannes. Dans cette thèse, nous étudions le problème d’allocation de ressources (VNE) sous diverses exigences. Pour offrir la virtualisation dans le contexte de réseau substrat multi-domaines, nous proposons une méthode de mappage conjoint des liens inter-domaines et intra-domaines. Avec une information réduite et limitées annoncées par les domaines, notre méthode est capable de mapper simultanément les liens intra-domaines et les liens inter-domaines afin d’optimiser les ressources. De plus, pour améliorer la robustesse des réseaux virtuels, nous proposons un algorithme d’évitement des pannes qui minimise la probabilité de panne des réseaux virtuels. Des solutions exactes et heuristiques sont proposées et détaillées pour des liens à bande passante infinie ou limitée. En outre, nous combinons l’algorithme d’évitement des pannes avec la protection pour proposer un VNE robuste et résistant aux pannes. Avec cette nouvelle approche, les liens protégeables puis les liens les moins vulnérables sont prioritairement sélectionnés pour le mappage des liens. Pour déterminer les liens protégeables, nous proposons une heuristique qui utilise l’algorithme du maxflow afin de vérifier etdedéterminerlesliensprotégeablesàl’étapedumappagedesliensprimaires. Encasd’insuffisance de ressources pour protéger tous les liens primaires, notre approche sélectionne les liens réduisant la probabilité de panne. / Network virtualization allows to create logical or virtual networks on top of a shared physical or substrate network. The resource allocation problem is an important issue in network virtualization. It corresponds to a well known problem called virtual network embedding (VNE). VNE consists in mapping each virtual node to one substrate node and each virtual link to one or several substrate paths in a way that the objective is optimized and the constraints verified. The objective often corresponds to the optimization of the node computational resources and link bandwidth whereas the constraints generally include geographic location of nodes, CPU, bandwidth, etc. In the multi-domain context where the knowledge of routing information is incomplete, the optimization of node and link resources are difficult and often impossible to achieve. Moreover, to ensure service continuity even upon failure, VNE should cope with failures by selecting the best and resilient mappings. Inthisthesis,westudytheVNEresourceallocationproblemunderdifferentrequirements. To embed a virtual network on multi-domain substrate network, we propose a joint peering and intra domain link mapping method. With reduced and limited information disclosed by the domains, our downsizing algorithm maps the intra domain and peering links in the same stage so that the resource utilization is optimized. To enhance the reliability of virtual networks, we propose a failure avoidance approach that minimizes the failure probability of virtual networks. Exact and heuristic solutions are proposed and detailed for the infinite and limited bandwidth link models. Moreover, we combine the failure avoidance with the failure protection in our novel protection-level-aware survivable VNE in order to improve the reliability. With this last approach, the protectable then the less vulnerable links are first selected for link mapping. To determine the protectable links, we propose a maxflow based heuristic that checks for the existence of backup paths during the primary mapping stage. In case of insufficient backup resources, the failure probability is reduced.