Results analysis and validation - D5.3

Deliverable D5.3 del projecte OneFITPostprint (author’s final draft

Mission programming for flying ensembles: combining planning with self-organization

The application of autonomous mobile robots can improve many situations of our daily lives. Robots can enhance working conditions, provide innovative techniques for different research disciplines, and support rescue forces in an emergency. In particular, flying robots have already shown their potential in many use-cases when cooperating in ensembles. Exploiting this potential requires sophisticated measures for the goal-oriented, application-specific programming of flying ensembles and the coordinated execution of so defined programs. Because different goals require different robots providing different capabilities, several software approaches emerged recently that focus on specifically designed robots. These approaches often incorporate autonomous planning, scheduling, optimization, and reasoning attributable to classic artificial intelligence. This allows for the goal-oriented instruction of ensembles, but also leads to inefficiencies if ensembles grow large or face uncertainty in the environment. By leaving the detailed planning of executions to individuals and foregoing optimality and goal-orientation, the selforganization paradigm can compensate for these drawbacks by scalability and robustness. In this thesis, we combine the advantageous properties of autonomous planning with that of self-organization in an approach to Mission Programming for Flying Ensembles. Furthermore, we overcome the current way of thinking about how mobile robots should be designed. Rather than assuming fixed-design robots, we assume that robots are modifiable in terms of their hardware at run-time. While using such robots enables their application in many different use cases, it also requires new software approaches for dealing with this flexible design. The contributions of this thesis thus are threefold. First, we provide a layered reference architecture for physically reconfigurable robot ensembles. Second, we provide a solution for programming missions for ensembles consisting of such robots in a goal-oriented fashion that provides measures for instructing individual robots or entire ensembles as desired in the specific use case. Third, we provide multiple self-organization mechanisms to deal with the system’s flexible design while executing such missions. Combining different self-organization mechanisms ensures that ensembles satisfy the static requirements of missions. We provide additional self-organization mechanisms for coordinating the execution in ensembles ensuring they meet the dynamic requirements of a mission. Furthermore, we provide a solution for integrating goal-oriented swarm behavior into missions using a general pattern we have identified for trajectory-modification-based swarm behavior. Using that pattern, we can modify, quantify, and further process the emergent effect of varying swarm behavior in a mission by changing only the parameters of its implementation. We evaluate results theoretically and practically in different case studies by deploying our techniques to simulated and real hardware.Der Einsatz von autonomen mobilen Robotern kann viele Abläufe unseres täglichen Lebens erleichtern. Ihr Einsatz kann Arbeitsbedingungen verbessern, als innovative Technik für verschiedene Forschungsdisziplinen dienen oder Rettungskräfte im Einsatz unterstützen. Insbesondere Flugroboter haben ihr Potenzial bereits in vielerlei Anwendungsfällen gezeigt, gerade wenn mehrere in Ensembles eingesetzt werden. Das Potenzial fliegender Ensembles zielgerichtet und anwendungsspezifisch auszuschöpfen erfordert ausgefeilte Programmiermethoden und Koordinierungsverfahren. Zu diesem Zweck sind zuletzt viele unterschiedliche und auf speziell entwickelte Roboter zugeschnittene Softwareansätze entstanden. Diese verwenden oft klassische Planungs-, Scheduling-, Optimierungs- und Reasoningverfahren. Während dies vor allem den zielgerichteten Einsatz von Ensembles ermöglicht, ist es jedoch auch oft ineffizient, wenn die Ensembles größer oder deren Einsatzumgebungen unsicher werden. Die genannten Nachteile können durch das Paradigma der Selbstorganisation kompensiert werden: Falls Anwendungen nicht zwangsläufig auf Optimalität und strikte Zielorientierung ausgelegt sind, kann so Skalierbarkeit und Robustheit im System erreicht werden. In dieser Arbeit werden die vorteilhaften Eigenschaften klassischer Planungstechniken mit denen der Selbstorganisation in einem Ansatz zur Missionsprogrammierung für fliegende Ensembles kombiniert. In der dafür entwickelten Lösung wird von der aktuell etablierten Ansicht einer unveränderlichen Roboterkonstruktion abgewichen. Stattdessen wird die Hardwarezusammenstellung der Roboter als zur Laufzeit modifizierbar angesehen. Der Einsatz solcher Roboter erfordert neue Softwareansätze um mit genannter Flexibilität umgehen zu können. Die hier vorgestellten Beiträge zu diesem Thema lassen sich in drei Punkten zusammenfassen: Erstens wird eine Schichtenarchitektur als Referenz für physikalisch konfigurierbare Roboterensembles vorgestellt. Zweitens wird eine Lösung zur zielorientierten Missions-Programmierung für derartige Ensembles präsentiert, mit der sowohl einzelne Roboter als auch ganze Ensembles instruiert werden können. Drittens werden mehrere Selbstorganisationsmechanismen vorgestellt, die die autonome Ausführung so erstellter Missionen ermöglichen. Durch die Kombination verschiedener Selbstorganisationsmechanismen wird sichergestellt, dass Ensembles die missionsspezifischen Anforderungen erfüllen. Zusätzliche Selbstorganisationsmechanismen ermöglichen die koordinierte Ausführung der Missionen durch die Ensembles. Darüber hinaus bietet diese Lösung die Möglichkeit der Integration zielorientierten Schwarmverhaltens. Durch ein allgemeines algorithmisches Verfahren für auf Trajektorien-Modifikation basierendes Schwarmverhalten können allein durch die Änderung des Parametersatzes unterschiedliche emergente Effekte in einer Mission erzielt, quantifiziert und weiterverarbeitet werden. Zur theoretischen und praktischen Evaluierung der Ergebnisse dieser Arbeit wurden die vorgestellten Techniken in verschiedenen Fallstudien auf simulierter sowie realer Hardware zum Einsatz gebracht

On demonstrating spectrum selection functionality for opportunistic networks

This paper presents a testbed platform to demonstrate and validate spectrum opportunity identification and spectrum selection functionalities in Opportunistic Networks (ONs). The hardware component of the testbed is based on reconfigurable devices able to transmit and receive data at different operating frequencies, which are dynamically configured. The software component has been developed to perform the creation and maintenance of ON radio links, including spectrum opportunity identification and selection decision making as well as all the necessary signaling to support the ON operation. Therefore, the presented platform provides a powerful tool for testing different algorithms in real operational radio environments under various interference conditions, thus enabling to gain deeper insight into the performance of algorithmic solutions, beyond the purely theoretical analyses based on models and/or simulations. Results presented in the paper validate the implementation conducted at the laboratory and illustrate the reconfigurability capabilities of the ON links under different conditions.Peer ReviewedPostprint (published version

On Demonstrating Spectrum Selection Functionality for Opportunistic Networks

This paper presents a testbed platform to demonstrate and validate spectrum opportunity identification and spectrum selection functionalities in Opportunistic Networks (ONs). The hardware component of the testbed is based on reconfigurable devices able to transmit and receive data at different operating frequencies, which are dynamically configured. The software component has been developed to perform the creation and maintenance of ON radio links, including spectrum opportunity identification and selection decision making as well as all the necessary signaling to support the ON operation. Therefore, the presented platform provides a powerful tool for testing different algorithms in real operational radio environments under various interference conditions, thus enabling to gain deeper insight into the performance of algorithmic solutions, beyond the purely theoretical analyses based on models and/or simulations. Results presented in the paper validate the implementation conducted at the laboratory and illustrate the reconfigurability capabilities of the ON links under different conditions

FPGA based technical solutions for high throughput data processing and encryption for 5G communication: A review

The field programmable gate array (FPGA) devices are ideal solutions for high-speed processing applications, given their flexibility, parallel processing capability, and power efficiency. In this review paper, at first, an overview of the key applications of FPGA-based platforms in 5G networks/systems is presented, exploiting the improved performances offered by such devices. FPGA-based implementations of cloud radio access network (C-RAN) accelerators, network function virtualization (NFV)-based network slicers, cognitive radio systems, and multiple input multiple output (MIMO) channel characterizers are the main considered applications that can benefit from the high processing rate, power efficiency and flexibility of FPGAs. Furthermore, the implementations of encryption/decryption algorithms by employing the Xilinx Zynq Ultrascale+MPSoC ZCU102 FPGA platform are discussed, and then we introduce our high-speed and lightweight implementation of the well-known AES-128 algorithm, developed on the same FPGA platform, and comparing it with similar solutions already published in the literature. The comparison results indicate that our AES-128 implementation enables efficient hardware usage for a given data-rate (up to 28.16 Gbit/s), resulting in higher efficiency (8.64 Mbps/slice) than other considered solutions. Finally, the applications of the ZCU102 platform for high-speed processing are explored, such as image and signal processing, visual recognition, and hardware resource management

Achieving Autonomic Computing through the Use of Variability Models at Run-time

Increasingly, software needs to dynamically adapt its behavior at run-time in response to changing conditions in the supporting computing infrastructure and in the surrounding physical environment. Adaptability is emerging as a necessary underlying capability, particularly for highly dynamic systems such as context-aware or ubiquitous systems. By automating tasks such as installation, adaptation, or healing, Autonomic Computing envisions computing environments that evolve without the need for human intervention. Even though there is a fair amount of work on architectures and their theoretical design, Autonomic Computing was criticised as being a \hype topic" because very little of it has been implemented fully. Furthermore, given that the autonomic system must change states at runtime and that some of those states may emerge and are much less deterministic, there is a great challenge to provide new guidelines, techniques and tools to help autonomic system development. This thesis shows that building up on the central ideas of Model Driven Development (Models as rst-order citizens) and Software Product Lines (Variability Management) can play a signi cant role as we move towards implementing the key self-management properties associated with autonomic computing. The presented approach encompass systems that are capable of modifying their own behavior with respect to changes in their operating environment, by using variability models as if they were the policies that drive the system's autonomic recon guration at runtime. Under a set of recon guration commands, the components that make up the architecture dynamically cooperate to change the con guration of the architecture to a new con guration. This work also provides the implementation of a Model-Based Recon guration Engine (MoRE) to blend the above ideas. Given a context event, MoRE queries the variability models to determine how the system should evolve, and then it provides the mechanisms for modifying the system.Cetina Englada, C. (2010). Achieving Autonomic Computing through the Use of Variability Models at Run-time [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7484Palanci

Supporting policy-based contextual reconfiguration and adaptation in ubiquitous computing

In order for pervasive computing systems to be able to perform tasks which support us in everyday life without requiring attention from the users of the environment, they need to adapt themselves in response to context. This makes context-awareness in general, and context-aware adaptation in particular, an essential requirement for pervasive computing systems. Two of the features of context-awareness are: contextual reconfiguration and contextual adaptation in which applications adapt their behaviour in response to context. We combine both these features of context-awareness to provide a broad scope of adaptation and put forward a system, called Policy-Based Contextual Reconfiguration and Adaptation (PCRA) that provides runtime support for both. The combination of both context-aware reconfiguration and context-aware adaptation provides a broad scope of adaptation and hence allows the development of diverse adaptive context-aware applications. However, another important issue is the choice of an effective means for developing, modifying and extending such applications. The main argument forming the basis of this thesis is that we advocate the use of a policy-based programming model and argue that it provides more effective means for developing, modifying and extending such applications. This thesis addresses other important surrounding issues which are associated with adaptive context-aware applications. These include the management of invalid bindings and the provision of seamless caching support for remote services involved in bindings for improved performance. The bindings may become invalid due to failure conditions that can arise due to network problems or migration of software components, causing bindings between the application component and remote service to become invalid. We have integrated reconfiguration support to manage bindings, and seamless caching support for remote services in PCRA. This thesis also describes the design and implementation of PCRA, which enables development of adaptive context-aware applications using policy specifications. Within PCRA, adaptive context-aware applications are modelled by specifying binding policies and adaptation policies. The use of policies within PCRA simplifies the development task because policies are expressed at a high-level of abstraction, and are expressed independently of each other. PCRA also allows the dynamic modification of applications since policies are independent units of execution and can be dynamically loaded and removed from the system. This is a powerful and useful capability as applications may evolve over time, i.e. the user needs and preferences may change, but re-starting is undesirable. We evaluate PCRA by comparing its features to other systems in the literature, and by performance measures

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Cognitive management frameworks and spectrum management strategies exploiting cognitive radio paradigm

Cognitive Radio (CR) paradigm represents an innovative solution to mitigate the spectrum scarcity problem by enabling Dynamic Spectrum Access (DSA), defined in order to conciliate the existing conflicts between the ever-increasing spectrum demand growth and the currently inefficient spectrum utilization. The basic idea of DSA is to provide proper solutions that allow sharing radio spectrum among several radio communication systems with sake of optimizing the overall spectrum utilization. This dissertation addressed the problem of modelling cognitive management frameworks that provide innovative strategies for spectrum management suitable to different scenarios and use cases in the context of DSA/CR Networks (CRNs). The first solution presented in this dissertation initially addressed the development of a framework that provides spectrum management strategies for Opportunistic Networks (ONs) defined as extended infrastructures created temporarily to serve specific regions following the policies dictated by the operator. The development of systems based on the CR paradigm to support the ONs is considered a key aspect to allow autonomous decisions and reconfiguration ability mechanisms because of the temporarily nature of these networks and the highly dynamic nature of the radio environment. Then, in order to expand the design of cognitive management frameworks providing spectrum management solutions that have applicability in a number of different scenarios and use cases, a cognitive management framework that exploits the Partially Observable Markov Decision Process (POMDP) concept has been proposed to combine the CR capabilities of radio environment awareness with a statistical characterization of the system dynamic. Finally, the framework based on POMDPs has been further extended with new functionalities able to characterize the environment dynamic through long-term predictions carried out exploiting the so-called belief vector. These frameworks as a whole aimed at demonstrating that a reliable characterization of the radio environment that combines awareness of its surrounding with a statistical evaluation of the system dynamics is able to guarantee an effcient utilization of the available spectrum resources. From a methodological point of view, the development and assessment of the proposed cognitive management frameworks and the corresponding spectrum management solutions involved analytical studies, system-level simulations and a real-time platform implementation. Overall, the research conducted in the context of this dissertation has revealed that proper cognitive management functionalities can be extremely beneficial to support spectrum management in a wide variety of scenarios and use cases.El paradigma de radio cognitiva (CR) representa una solución innovadora para mitigar el problema de escasez de los recursos radio, permitiendo el acceso dinámico al espectro (DSA), definido con el fin de conciliar los conflictos existentes entre el crecimiento de la demanda de espectro, cada vez mayor, y la utilización de los recursos radio actualmente ineficiente. La idea básica del DSA es proporcionar soluciones adecuadas que permitan compartir el espectro radioeléctrico entre varios sistemas de comunicaciones radio con el objetivo de optimizar la utilización general del espectro. Esta tesis doctoral aborda el problema de la modelización de marcos de gestión cognitiva que proporcionan estrategias innovadoras y adecuadas para la gestión del espectro en diferentes escenarios y casos de uso en el contexto de las redes de radio cognitiva (CRN). La primera solución que se presenta en esta tesis aborda inicialmente el desarrollo de un marco que ofrece estrategias de gestión del espectro para redes oportunistas (ONs) definidas como infraestructuras extendidas, creadas temporalmente para servir a regiones específicas siguiendo las políticas dictadas por el operador. Debido a la naturaleza temporal de estas redes y a la naturaleza altamente dinámica del entorno radio, el desarrollo de sistemas basados en el paradigma de CR para apoyar las ONs se considera un aspecto clave que permite decisiones autónomas y mecanismos de reconfiguración. Luego, con el fin de ampliar el diseño de los marcos de gestión cognitiva para proporcionar soluciones de gestión del espectro con aplicabilidad en una serie de diferentes escenarios y casos de uso, se ha propuesto un marco de gestión cognitiva que explota el concepto de Partially Observable Markov Decision Process (POMDP) para combinar las capacidades de conocimiento del entorno radio del CR, con una caracterización estadística de la dinámica del sistema. Finalmente, el marco basado en el POMDP se ha ampliado con nuevas funcionalidades capaces de caracterizar el entorno dinámico a través de predicciones a largo plazo llevadas a cabo explotando el concepto de belief vector. Estos marcos en su conjunto tienen el objetivo de demostrar que una caracterización fiable del entorno radio que combina el conocimiento de su entorno con una evaluación estadística de la dinámica del sistema, es capaz de garantizar una utilización eficiente de los recursos disponibles del espectro. Desde un punto de vista de la metodología, el desarrollo y la evaluación de los marcos de gestión cognitiva propuestos y las correspondientes soluciones de gestión del espectro han involucrado estudios analíticos, simulaciones y la implementación de una plataforma que permite evaluaciones en tiempo real. En general, la investigación llevada a cabo en el contexto de esta tesis doctoral ha revelado que funcionalidades adecuadas de gestión cognitiva pueden ser extremadamente eficientes para apoyar la gestión del espectro en una amplia variedad de escenarios y casos de estudio