3,630 research outputs found

    A Survey on Forensics and Compliance Auditing for Critical Infrastructure Protection

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    The broadening dependency and reliance that modern societies have on essential services provided by Critical Infrastructures is increasing the relevance of their trustworthiness. However, Critical Infrastructures are attractive targets for cyberattacks, due to the potential for considerable impact, not just at the economic level but also in terms of physical damage and even loss of human life. Complementing traditional security mechanisms, forensics and compliance audit processes play an important role in ensuring Critical Infrastructure trustworthiness. Compliance auditing contributes to checking if security measures are in place and compliant with standards and internal policies. Forensics assist the investigation of past security incidents. Since these two areas significantly overlap, in terms of data sources, tools and techniques, they can be merged into unified Forensics and Compliance Auditing (FCA) frameworks. In this paper, we survey the latest developments, methodologies, challenges, and solutions addressing forensics and compliance auditing in the scope of Critical Infrastructure Protection. This survey focuses on relevant contributions, capable of tackling the requirements imposed by massively distributed and complex Industrial Automation and Control Systems, in terms of handling large volumes of heterogeneous data (that can be noisy, ambiguous, and redundant) for analytic purposes, with adequate performance and reliability. The achieved results produced a taxonomy in the field of FCA whose key categories denote the relevant topics in the literature. Also, the collected knowledge resulted in the establishment of a reference FCA architecture, proposed as a generic template for a converged platform. These results are intended to guide future research on forensics and compliance auditing for Critical Infrastructure Protection.info:eu-repo/semantics/publishedVersio

    Serverless Strategies and Tools in the Cloud Computing Continuum

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    Tesis por compendio[ES] En los últimos años, la popularidad de la computación en nube ha permitido a los usuarios acceder a recursos de cómputo, red y almacenamiento sin precedentes bajo un modelo de pago por uso. Esta popularidad ha propiciado la aparición de nuevos servicios para resolver determinados problemas informáticos a gran escala y simplificar el desarrollo y el despliegue de aplicaciones. Entre los servicios más destacados en los últimos años se encuentran las plataformas FaaS (Función como Servicio), cuyo principal atractivo es la facilidad de despliegue de pequeños fragmentos de código en determinados lenguajes de programación para realizar tareas específicas en respuesta a eventos. Estas funciones son ejecutadas en los servidores del proveedor Cloud sin que los usuarios se preocupen de su mantenimiento ni de la gestión de su elasticidad, manteniendo siempre un modelo de pago por uso de grano fino. Las plataformas FaaS pertenecen al paradigma informático conocido como Serverless, cuyo propósito es abstraer la gestión de servidores por parte de los usuarios, permitiéndoles centrar sus esfuerzos únicamente en el desarrollo de aplicaciones. El problema del modelo FaaS es que está enfocado principalmente en microservicios y tiende a tener limitaciones en el tiempo de ejecución y en las capacidades de computación (por ejemplo, carece de soporte para hardware de aceleración como GPUs). Sin embargo, se ha demostrado que la capacidad de autoaprovisionamiento y el alto grado de paralelismo de estos servicios pueden ser muy adecuados para una mayor variedad de aplicaciones. Además, su inherente ejecución dirigida por eventos hace que las funciones sean perfectamente adecuadas para ser definidas como pasos en flujos de trabajo de procesamiento de archivos (por ejemplo, flujos de trabajo de computación científica). Por otra parte, el auge de los dispositivos inteligentes e integrados (IoT), las innovaciones en las redes de comunicación y la necesidad de reducir la latencia en casos de uso complejos han dado lugar al concepto de Edge computing, o computación en el borde. El Edge computing consiste en el procesamiento en dispositivos cercanos a las fuentes de datos para mejorar los tiempos de respuesta. La combinación de este paradigma con la computación en nube, formando arquitecturas con dispositivos a distintos niveles en función de su proximidad a la fuente y su capacidad de cómputo, se ha acuñado como continuo de la computación en la nube (o continuo computacional). Esta tesis doctoral pretende, por lo tanto, aplicar diferentes estrategias Serverless para permitir el despliegue de aplicaciones generalistas, empaquetadas en contenedores de software, a través de los diferentes niveles del continuo computacional. Para ello, se han desarrollado múltiples herramientas con el fin de: i) adaptar servicios FaaS de proveedores Cloud públicos; ii) integrar diferentes componentes software para definir una plataforma Serverless en infraestructuras privadas y en el borde; iii) aprovechar dispositivos de aceleración en plataformas Serverless; y iv) facilitar el despliegue de aplicaciones y flujos de trabajo a través de interfaces de usuario. Además, se han creado y adaptado varios casos de uso para evaluar los desarrollos conseguidos.[CA] En els últims anys, la popularitat de la computació al núvol ha permès als usuaris accedir a recursos de còmput, xarxa i emmagatzematge sense precedents sota un model de pagament per ús. Aquesta popularitat ha propiciat l'aparició de nous serveis per resoldre determinats problemes informàtics a gran escala i simplificar el desenvolupament i desplegament d'aplicacions. Entre els serveis més destacats en els darrers anys hi ha les plataformes FaaS (Funcions com a Servei), el principal atractiu de les quals és la facilitat de desplegament de petits fragments de codi en determinats llenguatges de programació per realitzar tasques específiques en resposta a esdeveniments. Aquestes funcions són executades als servidors del proveïdor Cloud sense que els usuaris es preocupen del seu manteniment ni de la gestió de la seva elasticitat, mantenint sempre un model de pagament per ús de gra fi. Les plataformes FaaS pertanyen al paradigma informàtic conegut com a Serverless, el propòsit del qual és abstraure la gestió de servidors per part dels usuaris, permetent centrar els seus esforços únicament en el desenvolupament d'aplicacions. El problema del model FaaS és que està enfocat principalment a microserveis i tendeix a tenir limitacions en el temps d'execució i en les capacitats de computació (per exemple, no té suport per a maquinari d'acceleració com GPU). Tot i això, s'ha demostrat que la capacitat d'autoaprovisionament i l'alt grau de paral·lelisme d'aquests serveis poden ser molt adequats per a més aplicacions. A més, la seva inherent execució dirigida per esdeveniments fa que les funcions siguen perfectament adequades per ser definides com a passos en fluxos de treball de processament d'arxius (per exemple, fluxos de treball de computació científica). D'altra banda, l'auge dels dispositius intel·ligents i integrats (IoT), les innovacions a les xarxes de comunicació i la necessitat de reduir la latència en casos d'ús complexos han donat lloc al concepte d'Edge computing, o computació a la vora. L'Edge computing consisteix en el processament en dispositius propers a les fonts de dades per millorar els temps de resposta. La combinació d'aquest paradigma amb la computació en núvol, formant arquitectures amb dispositius a diferents nivells en funció de la proximitat a la font i la capacitat de còmput, s'ha encunyat com a continu de la computació al núvol (o continu computacional). Aquesta tesi doctoral pretén, doncs, aplicar diferents estratègies Serverless per permetre el desplegament d'aplicacions generalistes, empaquetades en contenidors de programari, a través dels diferents nivells del continu computacional. Per això, s'han desenvolupat múltiples eines per tal de: i) adaptar serveis FaaS de proveïdors Cloud públics; ii) integrar diferents components de programari per definir una plataforma Serverless en infraestructures privades i a la vora; iii) aprofitar dispositius d'acceleració a plataformes Serverless; i iv) facilitar el desplegament d'aplicacions i fluxos de treball mitjançant interfícies d'usuari. A més, s'han creat i s'han adaptat diversos casos d'ús per avaluar els desenvolupaments aconseguits.[EN] In recent years, the popularity of Cloud computing has allowed users to access unprecedented compute, network, and storage resources under a pay-per-use model. This popularity led to new services to solve specific large-scale computing challenges and simplify the development and deployment of applications. Among the most prominent services in recent years are FaaS (Function as a Service) platforms, whose primary appeal is the ease of deploying small pieces of code in certain programming languages to perform specific tasks on an event-driven basis. These functions are executed on the Cloud provider's servers without users worrying about their maintenance or elasticity management, always keeping a fine-grained pay-per-use model. FaaS platforms belong to the computing paradigm known as Serverless, which aims to abstract the management of servers from the users, allowing them to focus their efforts solely on the development of applications. The problem with FaaS is that it focuses on microservices and tends to have limitations regarding the execution time and the computing capabilities (e.g. lack of support for acceleration hardware such as GPUs). However, it has been demonstrated that the self-provisioning capability and high degree of parallelism of these services can be well suited to broader applications. In addition, their inherent event-driven triggering makes functions perfectly suitable to be defined as steps in file processing workflows (e.g. scientific computing workflows). Furthermore, the rise of smart and embedded devices (IoT), innovations in communication networks and the need to reduce latency in challenging use cases have led to the concept of Edge computing. Edge computing consists of conducting the processing on devices close to the data sources to improve response times. The coupling of this paradigm together with Cloud computing, involving architectures with devices at different levels depending on their proximity to the source and their compute capability, has been coined as Cloud Computing Continuum (or Computing Continuum). Therefore, this PhD thesis aims to apply different Serverless strategies to enable the deployment of generalist applications, packaged in software containers, across the different tiers of the Cloud Computing Continuum. To this end, multiple tools have been developed in order to: i) adapt FaaS services from public Cloud providers; ii) integrate different software components to define a Serverless platform on on-premises and Edge infrastructures; iii) leverage acceleration devices on Serverless platforms; and iv) facilitate the deployment of applications and workflows through user interfaces. Additionally, several use cases have been created and adapted to assess the developments achieved.Risco Gallardo, S. (2023). Serverless Strategies and Tools in the Cloud Computing Continuum [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202013Compendi

    Risk and threat mitigation techniques in internet of things (IoT) environments: a survey

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    Security in the Internet of Things (IoT) remains a predominant area of concern. Although several other surveys have been published on this topic in recent years, the broad spectrum that this area aims to cover, the rapid developments and the variety of concerns make it impossible to cover the topic adequately. This survey updates the state of the art covered in previous surveys and focuses on defences and mitigations against threats rather than on the threats alone, an area that is less extensively covered by other surveys. This survey has collated current research considering the dynamicity of the IoT environment, a topic missed in other surveys and warrants particular attention. To consider the IoT mobility, a life-cycle approach is adopted to the study of dynamic and mobile IoT environments and means of deploying defences against malicious actors aiming to compromise an IoT network and to evolve their attack laterally within it and from it. This survey takes a more comprehensive and detailed step by analysing a broad variety of methods for accomplishing each of the mitigation steps, presenting these uniquely by introducing a “defence-in-depth” approach that could significantly slow down the progress of an attack in the dynamic IoT environment. This survey sheds a light on leveraging redundancy as an inherent nature of multi-sensor IoT applications, to improve integrity and recovery. This study highlights the challenges of each mitigation step, emphasises novel perspectives, and reconnects the discussed mitigation steps to the ground principles they seek to implement

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Towards a centralized multicore automotive system

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    Today’s automotive systems are inundated with embedded electronics to host chassis, powertrain, infotainment, advanced driver assistance systems, and other modern vehicle functions. As many as 100 embedded microcontrollers execute hundreds of millions of lines of code in a single vehicle. To control the increasing complexity in vehicle electronics and services, automakers are planning to consolidate different on-board automotive functions as software tasks on centralized multicore hardware platforms. However, these vehicle software services have different and contrasting timing, safety, and security requirements. Existing vehicle operating systems are ill-equipped to provide all the required service guarantees on a single machine. A centralized automotive system aims to tackle this by assigning software tasks to multiple criticality domains or levels according to their consequences of failures, or international safety standards like ISO 26262. This research investigates several emerging challenges in time-critical systems for a centralized multicore automotive platform and proposes a novel vehicle operating system framework to address them. This thesis first introduces an integrated vehicle management system (VMS), called DriveOS™, for a PC-class multicore hardware platform. Its separation kernel design enables temporal and spatial isolation among critical and non-critical vehicle services in different domains on the same machine. Time- and safety-critical vehicle functions are implemented in a sandboxed Real-time Operating System (OS) domain, and non-critical software is developed in a sandboxed general-purpose OS (e.g., Linux, Android) domain. To leverage the advantages of model-driven vehicle function development, DriveOS provides a multi-domain application framework in Simulink. This thesis also presents a real-time task pipeline scheduling algorithm in multiprocessors for communication between connected vehicle services with end-to-end guarantees. The benefits and performance of the overall automotive system framework are demonstrated with hardware-in-the-loop testing using real-world applications, car datasets and simulated benchmarks, and with an early-stage deployment in a production-grade luxury electric vehicle

    Techno-Economic Assessment in Communications: New Challenges

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    This article shows a brief history of Techno-Economic Assessment (TEA) in Communications, a proposed redefinition of TEA as well as the new challenges derived from a dynamic context with cloud-native virtualized networks, the Helium Network & alike blockchain-based decentralized networks, the new network as a platform (NaaP) paradigm, carbon pricing, network sharing, and web3, metaverse and blockchain technologies. The authors formulate the research question and show the need to improve TEA models to integrate and manage all this increasing complexity. This paper also proposes the characteristics TEA models should have and their current degree of compliance for several use cases: 5G and beyond, software-defined wide area network (SD-WAN), secure access service edge (SASE), secure service edge (SSE), and cloud cybersecurity risk assessment. The authors also present TEA extensibility to request for proposals (RFP) processes and other industries, to conclude that there is an urgent need for agile and effective TEA in Comms that allows industrialization of agile decision-making for all market stakeholders to choose the optimal solution for any technology, scenario and use case.Comment: 18 pages, 1 figure, 2 table

    On Age-of-Information Aware Resource Allocation for Industrial Control-Communication-Codesign

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    Unter dem Überbegriff Industrie 4.0 wird in der industriellen Fertigung die zunehmende Digitalisierung und Vernetzung von industriellen Maschinen und Prozessen zusammengefasst. Die drahtlose, hoch-zuverlässige, niedrig-latente Kommunikation (engl. ultra-reliable low-latency communication, URLLC) – als Bestandteil von 5G gewährleistet höchste Dienstgüten, die mit industriellen drahtgebundenen Technologien vergleichbar sind und wird deshalb als Wegbereiter von Industrie 4.0 gesehen. Entgegen diesem Trend haben eine Reihe von Arbeiten im Forschungsbereich der vernetzten Regelungssysteme (engl. networked control systems, NCS) gezeigt, dass die hohen Dienstgüten von URLLC nicht notwendigerweise erforderlich sind, um eine hohe Regelgüte zu erzielen. Das Co-Design von Kommunikation und Regelung ermöglicht eine gemeinsame Optimierung von Regelgüte und Netzwerkparametern durch die Aufweichung der Grenze zwischen Netzwerk- und Applikationsschicht. Durch diese Verschränkung wird jedoch eine fundamentale (gemeinsame) Neuentwicklung von Regelungssystemen und Kommunikationsnetzen nötig, was ein Hindernis für die Verbreitung dieses Ansatzes darstellt. Stattdessen bedient sich diese Dissertation einem Co-Design-Ansatz, der beide Domänen weiterhin eindeutig voneinander abgrenzt, aber das Informationsalter (engl. age of information, AoI) als bedeutenden Schnittstellenparameter ausnutzt. Diese Dissertation trägt dazu bei, die Echtzeitanwendungszuverlässigkeit als Folge der Überschreitung eines vorgegebenen Informationsalterschwellenwerts zu quantifizieren und fokussiert sich dabei auf den Paketverlust als Ursache. Anhand der Beispielanwendung eines fahrerlosen Transportsystems wird gezeigt, dass die zeitlich negative Korrelation von Paketfehlern, die in heutigen Systemen keine Rolle spielt, für Echtzeitanwendungen äußerst vorteilhaft ist. Mit der Annahme von schnellem Schwund als dominanter Fehlerursache auf der Luftschnittstelle werden durch zeitdiskrete Markovmodelle, die für die zwei Netzwerkarchitekturen Single-Hop und Dual-Hop präsentiert werden, Kommunikationsfehlerfolgen auf einen Applikationsfehler abgebildet. Diese Modellierung ermöglicht die analytische Ableitung von anwendungsbezogenen Zuverlässigkeitsmetriken wie die durschnittliche Dauer bis zu einem Fehler (engl. mean time to failure). Für Single-Hop-Netze wird das neuartige Ressourcenallokationsschema State-Aware Resource Allocation (SARA) entwickelt, das auf dem Informationsalter beruht und die Anwendungszuverlässigkeit im Vergleich zu statischer Multi-Konnektivität um Größenordnungen erhöht, während der Ressourcenverbrauch im Bereich von konventioneller Einzelkonnektivität bleibt. Diese Zuverlässigkeit kann auch innerhalb eines Systems von Regelanwendungen, in welchem mehrere Agenten um eine begrenzte Anzahl Ressourcen konkurrieren, statistisch garantiert werden, wenn die Anzahl der verfügbaren Ressourcen pro Agent um ca. 10 % erhöht werden. Für das Dual-Hop Szenario wird darüberhinaus ein Optimierungsverfahren vorgestellt, das eine benutzerdefinierte Kostenfunktion minimiert, die niedrige Anwendungszuverlässigkeit, hohes Informationsalter und hohen durchschnittlichen Ressourcenverbrauch bestraft und so das benutzerdefinierte optimale SARA-Schema ableitet. Diese Optimierung kann offline durchgeführt und als Look-Up-Table in der unteren Medienzugriffsschicht zukünftiger industrieller Drahtlosnetze implementiert werden.:1. Introduction 1 1.1. The Need for an Industrial Solution . . . . . . . . . . . . . . . . . . . 3 1.2. Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2. Related Work 7 2.1. Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2. Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3. Codesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.1. The Need for Abstraction – Age of Information . . . . . . . . 11 2.4. Dependability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3. Deriving Proper Communications Requirements 17 3.1. Fundamentals of Control Theory . . . . . . . . . . . . . . . . . . . . 18 3.1.1. Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.2. Performance Requirements . . . . . . . . . . . . . . . . . . . 21 3.1.3. Packet Losses and Delay . . . . . . . . . . . . . . . . . . . . . 22 3.2. Joint Design of Control Loop with Packet Losses . . . . . . . . . . . . 23 3.2.1. Method 1: Reduced Sampling . . . . . . . . . . . . . . . . . . 23 3.2.2. Method 2: Markov Jump Linear System . . . . . . . . . . . . . 25 3.2.3. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3. Focus Application: The AGV Use Case . . . . . . . . . . . . . . . . . . 31 3.3.1. Control Loop Model . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.2. Control Performance Requirements . . . . . . . . . . . . . . . 33 3.3.3. Joint Modeling: Applying Reduced Sampling . . . . . . . . . . 34 3.3.4. Joint Modeling: Applying MJLS . . . . . . . . . . . . . . . . . 34 3.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4. Modeling Control-Communication Failures 43 4.1. Communication Assumptions . . . . . . . . . . . . . . . . . . . . . . 43 4.1.1. Small-Scale Fading as a Cause of Failure . . . . . . . . . . . . 44 4.1.2. Connectivity Models . . . . . . . . . . . . . . . . . . . . . . . 46 4.2. Failure Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2.1. Single-hop network . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2.2. Dual-hop network . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.3.1. Mean Time to Failure . . . . . . . . . . . . . . . . . . . . . . . 54 4.3.2. Packet Loss Ratio . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.3.3. Average Number of Assigned Channels . . . . . . . . . . . . . 57 4.3.4. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5. Single Hop – Single Agent 61 5.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 61 5.2. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 5.3. Erroneous Acknowledgments . . . . . . . . . . . . . . . . . . . . . . 67 5.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6. Single Hop – Multiple Agents 71 6.1. Failure Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.1.1. Admission Control . . . . . . . . . . . . . . . . . . . . . . . . 72 6.1.2. Transition Probabilities . . . . . . . . . . . . . . . . . . . . . . 73 6.1.3. Computational Complexity . . . . . . . . . . . . . . . . . . . 74 6.1.4. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . 75 6.2. Illustration Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6.3.1. Verification through System-Level Simulation . . . . . . . . . 78 6.3.2. Applicability on the System Level . . . . . . . . . . . . . . . . 79 6.3.3. Comparison of Admission Control Schemes . . . . . . . . . . 80 6.3.4. Impact of the Packet Loss Tolerance . . . . . . . . . . . . . . . 82 6.3.5. Impact of the Number of Agents . . . . . . . . . . . . . . . . . 84 6.3.6. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 84 6.3.7. Channel Saturation Ratio . . . . . . . . . . . . . . . . . . . . 86 6.3.8. Enforcing Full Channel Saturation . . . . . . . . . . . . . . . 86 6.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 7. Dual Hop – Single Agent 91 7.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 91 7.2. Optimization Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 7.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 7.3.1. Extensive Simulation . . . . . . . . . . . . . . . . . . . . . . . 96 7.3.2. Non-Integer-Constrained Optimization . . . . . . . . . . . . . 98 7.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 8. Conclusions and Outlook 105 8.1. Key Results and Conclusions . . . . . . . . . . . . . . . . . . . . . . . 105 8.2. Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 A. DC Motor Model 111 Bibliography 113 Publications of the Author 127 List of Figures 129 List of Tables 131 List of Operators and Constants 133 List of Symbols 135 List of Acronyms 137 Curriculum Vitae 139In industrial manufacturing, Industry 4.0 refers to the ongoing convergence of the real and virtual worlds, enabled through intelligently interconnecting industrial machines and processes through information and communications technology. Ultrareliable low-latency communication (URLLC) is widely regarded as the enabling technology for Industry 4.0 due to its ability to fulfill highest quality-of-service (QoS) comparable to those of industrial wireline connections. In contrast to this trend, a range of works in the research domain of networked control systems have shown that URLLC’s supreme QoS is not necessarily required to achieve high quality-ofcontrol; the co-design of control and communication enables to jointly optimize and balance both quality-of-control parameters and network parameters through blurring the boundary between application and network layer. However, through the tight interlacing, this approach requires a fundamental (joint) redesign of both control systems and communication networks and may therefore not lead to short-term widespread adoption. Therefore, this thesis instead embraces a novel co-design approach which keeps both domains distinct but leverages the combination of control and communications by yet exploiting the age of information (AoI) as a valuable interface metric. This thesis contributes to quantifying application dependability as a consequence of exceeding a given peak AoI with the particular focus on packet losses. The beneficial influence of negative temporal packet loss correlation on control performance is demonstrated by means of the automated guided vehicle use case. Assuming small-scale fading as the dominant cause of communication failure, a series of communication failures are mapped to an application failure through discrete-time Markov models for single-hop (e.g, only uplink or downlink) and dual-hop (e.g., subsequent uplink and downlink) architectures. This enables the derivation of application-related dependability metrics such as the mean time to failure in closed form. For single-hop networks, an AoI-aware resource allocation strategy termed state-aware resource allocation (SARA) is proposed that increases the application reliability by orders of magnitude compared to static multi-connectivity while keeping the resource consumption in the range of best-effort single-connectivity. This dependability can also be statistically guaranteed on a system level – where multiple agents compete for a limited number of resources – if the provided amount of resources per agent is increased by approximately 10 %. For the dual-hop scenario, an AoI-aware resource allocation optimization is developed that minimizes a user-defined penalty function that punishes low application reliability, high AoI, and high average resource consumption. This optimization may be carried out offline and each resulting optimal SARA scheme may be implemented as a look-up table in the lower medium access control layer of future wireless industrial networks.:1. Introduction 1 1.1. The Need for an Industrial Solution . . . . . . . . . . . . . . . . . . . 3 1.2. Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2. Related Work 7 2.1. Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2. Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3. Codesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.1. The Need for Abstraction – Age of Information . . . . . . . . 11 2.4. Dependability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3. Deriving Proper Communications Requirements 17 3.1. Fundamentals of Control Theory . . . . . . . . . . . . . . . . . . . . 18 3.1.1. Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.2. Performance Requirements . . . . . . . . . . . . . . . . . . . 21 3.1.3. Packet Losses and Delay . . . . . . . . . . . . . . . . . . . . . 22 3.2. Joint Design of Control Loop with Packet Losses . . . . . . . . . . . . 23 3.2.1. Method 1: Reduced Sampling . . . . . . . . . . . . . . . . . . 23 3.2.2. Method 2: Markov Jump Linear System . . . . . . . . . . . . . 25 3.2.3. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3. Focus Application: The AGV Use Case . . . . . . . . . . . . . . . . . . 31 3.3.1. Control Loop Model . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.2. Control Performance Requirements . . . . . . . . . . . . . . . 33 3.3.3. Joint Modeling: Applying Reduced Sampling . . . . . . . . . . 34 3.3.4. Joint Modeling: Applying MJLS . . . . . . . . . . . . . . . . . 34 3.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4. Modeling Control-Communication Failures 43 4.1. Communication Assumptions . . . . . . . . . . . . . . . . . . . . . . 43 4.1.1. Small-Scale Fading as a Cause of Failure . . . . . . . . . . . . 44 4.1.2. Connectivity Models . . . . . . . . . . . . . . . . . . . . . . . 46 4.2. Failure Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2.1. Single-hop network . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2.2. Dual-hop network . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.3.1. Mean Time to Failure . . . . . . . . . . . . . . . . . . . . . . . 54 4.3.2. Packet Loss Ratio . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.3.3. Average Number of Assigned Channels . . . . . . . . . . . . . 57 4.3.4. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5. Single Hop – Single Agent 61 5.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 61 5.2. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 5.3. Erroneous Acknowledgments . . . . . . . . . . . . . . . . . . . . . . 67 5.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6. Single Hop – Multiple Agents 71 6.1. Failure Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.1.1. Admission Control . . . . . . . . . . . . . . . . . . . . . . . . 72 6.1.2. Transition Probabilities . . . . . . . . . . . . . . . . . . . . . . 73 6.1.3. Computational Complexity . . . . . . . . . . . . . . . . . . . 74 6.1.4. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . 75 6.2. Illustration Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6.3.1. Verification through System-Level Simulation . . . . . . . . . 78 6.3.2. Applicability on the System Level . . . . . . . . . . . . . . . . 79 6.3.3. Comparison of Admission Control Schemes . . . . . . . . . . 80 6.3.4. Impact of the Packet Loss Tolerance . . . . . . . . . . . . . . . 82 6.3.5. Impact of the Number of Agents . . . . . . . . . . . . . . . . . 84 6.3.6. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 84 6.3.7. Channel Saturation Ratio . . . . . . . . . . . . . . . . . . . . 86 6.3.8. Enforcing Full Channel Saturation . . . . . . . . . . . . . . . 86 6.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 7. Dual Hop – Single Agent 91 7.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 91 7.2. Optimization Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 7.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 7.3.1. Extensive Simulation . . . . . . . . . . . . . . . . . . . . . . . 96 7.3.2. Non-Integer-Constrained Optimization . . . . . . . . . . . . . 98 7.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 8. Conclusions and Outlook 105 8.1. Key Results and Conclusions . . . . . . . . . . . . . . . . . . . . . . . 105 8.2. Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 A. DC Motor Model 111 Bibliography 113 Publications of the Author 127 List of Figures 129 List of Tables 131 List of Operators and Constants 133 List of Symbols 135 List of Acronyms 137 Curriculum Vitae 13

    Design and Implementation of a Portable Framework for Application Decomposition and Deployment in Edge-Cloud Systems

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    The emergence of cyber-physical systems has brought about a significant increase in complexity and heterogeneity in the infrastructure on which these systems are deployed. One particular example of this complexity is the interplay between cloud, fog, and edge computing. However, the complexity of these systems can pose challenges when it comes to implementing self-organizing mechanisms, which are often designed to work on flat networks. Therefore, it is essential to separate the application logic from the specific deployment aspects to promote reusability and flexibility in infrastructure exploitation. To address this issue, a novel approach called "pulverization" has been proposed. This approach involves breaking down the system into smaller computational units, which can then be deployed on the available infrastructure. In this thesis, the design and implementation of a portable framework that enables the "pulverization" of cyber-physical systems are presented. The main objective of the framework is to pave the way for the deployment of cyber-physical systems in the edge-cloud continuum by reducing the complexity of the infrastructure and exploit opportunistically the heterogeneous resources available on it. Different scenarios are presented to highlight the effectiveness of the framework in different heterogeneous infrastructures and devices. Current limitations and future work are examined to identify improvement areas for the framework
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