Today's Internet is seeing a massive shift from traditional client-server applications towards real-time, context-sensitive, and highly immersive applications. The fusion between Cyber-physical systems, The Internet of Things (IoT), Augmented/Virtual-Reality (AR/VR), and the Tactile Internet with the Human-in-the-Loop (TaHIL) means that Ultra-Reliable Low Latency Communication (URLLC) is a key functional requirement. Mobile Edge Computing (MEC) has emerged as a network architectural paradigm to address such ever-increasing resource demands. MEC leverages networking and computational resource pools that are closer to the end-users at the far edge of the network, eliminating the need to send and process large volumes of data over multiple distant hops at central cloud computing data centers. Multiple 'cloudlets' are formed at the edge, and the access to resources is shared and federated across them over multiple network domains that are distributed over various geographical locations. However, this federated access comes at the cost of a fuzzy and dynamically-changing network security perimeter because there are multiple sources of mobility. Not only are the end users mobile, but the applications themselves virtually migrate over multiple network domains and cloudlets to serve the end users, bypassing statically placed network security middleboxes and firewalls. This work aims to address this problem by proposing adaptive network security measures that can be dynamically changed at runtime, and are decoupled from the ever-changing network topology. In particular, we: 1) use the state of the art in programmable networking to protect MEC networks from internal adversaries that can adapt and laterally move, 2) Automatically infer application security contexts, and device vulnerabilities, then evolve the network access control policies to segment the network in such a way that minimizes the attack surface with minimal impact on its utility, 3) propose new metrics to assess the susceptibility of edge nodes to a new class of stealthy attacks that bypasses traditional statically placed Intrusion Detection Systems (IDS), and a probabilistic approach to pro-actively protect them.:Acknowledgments Acronyms & Abbreviations 1 Introduction 1.1 Prelude 1.2 Motivation and Challenges 1.3 Aim and objectives 1.4 Contributions 1.5 Thesis structure 2 Background 2.1 A primer on computer networks 2.2 Network security 2.3 Network softwarization 2.4 Cloudification of networks 2.5 Securing cloud networks 2.6 Towards Securing Edge Cloud Networks 2.7 Summary I Adaptive security in consumer edge cloud networks 3 Automatic microsegmentation of smarthome IoT networks 3.1 Introduction 3.2 Related work 3.3 Smart home microsegmentation 3.4 Software-Defined Secure Isolation 3.5 Evaluation 3.6 Summary 4 Smart home microsegmentation with user privacy in mind 4.1 Introduction 4.2 Related Work 4.3 Goals and Assumptions 4.4 Quantifying the security and privacy of SHIoT devices 4.5 Automatic microsegmentation 4.6 Manual microsegmentation 4.7 Experimental setup 4.8 Evaluation 4.9 Summary II Adaptive security in enterprise edge cloud networks 5 Adaptive real-time network deception and isolation 5.1 Introduction 5.2 Related work 5.3 Sandnet’s concept 5.4 Live Cloning and Network Deception 5.5 Evaluation 5.6 Summary 6 Localization of internal stealthy DDoS attacks on Microservices 6.1 Introduction 6.2 Related work 6.3 Assumptions & Threat model 6.4 Mitigating SILVDDoS 6.5 Evaluation 6.6 Summary III Summary of Results 7 Conclusion 7.1 Main outcomes 7.2 Future outlook Listings Bibliography List of Algorithms List of Figures List of Tables Appendix

<p>Next-generation (Next-Gen) mobile applications, Extended Reality (XR), which encompasses Virtual/Augmented/Mixed Reality (VR/AR/MR), promise to revolutionize how people interact with technology and the world, ushering in a new era of immersive experiences. However, the hardware capacity of mobile devices will not grow proportionally with the escalating resource demands of the mobile apps due to their battery constraint. To bridge the gap, edge computing has emerged as a promising approach. It is further boosted by emerging 5G cellular networks, which promise low latency and high bandwidth. However, realizing the full potential of edge computing faces several fundamental challenges.</p> <p><br></p> <p>In this thesis, we first discuss a set of fundamental design challenges in supporting Next-Gen mobile applications via edge computing. These challenges extend across the three key system components involved — mobile clients, edge servers, and cellular networks. We then present how we address several of these challenges, including (1) how to coordinate mobile clients and edge servers to achieve stringent QoE requirements for Next-Gen apps; (2) how to optimize energy consumption of running Next-Gen apps on mobile devices to ensure long-lasting user experience; and (3) how to model and generate control-plane traffic of cellular networks to enable innovation on mobile network architectural design to support Next-Gen apps not only over 4G but also over 5G and beyond.</p> <p><br></p> <p>First, we present how to optimize the latency in edge-assisted XR system via the mobile-client and edge-server co-design. Specifically, we exploit key insights about frame similarity in VR to build the first multiplayer edge-assisted VR design, Coterie. We demonstrate that compared with the prior work on single-player VR, Coterie reduces the per-player network load by 10.6X−25.7X, and can easily support 4 players for high-quality VR apps on Pixel 2 over 802.11ac running at 60 FPS and under 16ms responsiveness without exhausting the finite wireless bandwidth.</p> <p><br></p> <p>Second, we focus on the energy perspective of running Next-Gen apps on mobile devices. We study a major limitation of a classic and de facto app energy management technique, reactive energy-aware app adaptation, which was first proposed two decades ago. We propose, design, and validate a new solution, the first proactive energy-aware app adaptation, that effectively tackles the limitation and achieves higher app QoE while meeting a given energy drain target. Compared with traditional approaches, our proactive solution improves the QoE by 44.8% (Pixel 2) and 19.2% (Moto Z3) under low power budget.</p> <p><br></p> <p>Finally, we delve into the third system component, cellular networks. To facilitate innovation in mobile network architecture to better support Next-Gen apps, we characterize and model the control-plane traffic of cellular networks, which has been mostly overlooked by prior work. To model the control-plane traffic, we first prove that traditional probability distributions that have been widely used for modeling Internet traffic (e.g., Poisson, Pareto, and Weibull) cannot model the control-plane traffic due to the much higher burstiness and longer tails in the cumulative distributions of the control-plane traffic. We then propose a two-level state-machine-based traffic model based on the Semi-Markov model. We finally validate that the synthesized traces by using our model achieve small differences compared with the real traces, i.e., within 1.7%, 4.9% and 0.8%, for phones, connected cars, and tablets, respectively. We also show that our model can be easily adjusted from LTE to 5G, enabling further research on control-plane design and optimization for 4G/5G and beyond.</p>

[pt] A importância da Internet das Coisas (IoT) tem aumentado significativamente nos últimos anos, e dispositivos IoT têm sido usados em diferentes indústrias e tipos de aplicação, como casas inteligentes, sensores indutriais, veículos autonomos, wearables, etc. Apesar deste cenário trazer inovações tecnológicas, novas experiências para usuários, e novas soluções de negócio, também levanta preocupações relevantes relacionadas a segurança da informação e privacidade. Neste trabalho nós apresentamos o EdgeSec Framework, um novo framework de segurança para IoT desenvolvido como uma solução de segurança para os middlewares ContextNet e Mobile-Hub. O seu objetivo principal é estender e melhorar uma arquitetura e uma implementação já existentes para estes middlewares, criando uma solução mais genérica, robusta e flexível,e garantindo autenticação, autorização, integridade e confidencialidade de dados. O framework foi elaborado com foco na total extensiblidade através da introdução de interfaces de protocolos, que podem ser implementadas por plugins, tornando-o compatível com uma variedade de algoritmos de segurança e dispositivos IoT. Uma implementação completa foi realizada como prova de conceito, e testes de desempenho e experimentos foram realizados para avaliar a viabilidade da solução. Os resultados mostram que o EdgeSec Framework pode melhorar significativamente a segurança do Mobile-Hub e diversos tipos de aplicações IoT através de uma maior compatibilidade e flexibilidade, e garantindo todas as proteções básicas de segurança. / [en] The importance of the Internet of Things (IoT) has increased significantly in recent years, and IoT devices are being used in many different industries and types of applications, such as smart homes, industrial sensors, autonomous vehicles, personal wearables, and more. While this brings technology innovation, new user experiences, and new business solutions, it also raises important concerns related to information security and privacy. In this work we present EdgeSec Framework, a new IoT security framework, made concrete as a security solution for ContextNet and Mobile-Hub middlewares. Its main goal is to extend and improve on an existing security architecture and implementation, creating a more generic, robust, and flexible solution that ensures authentication, authorization, data integrity and confidentiality. The framework was designed with full extensibility in mind by introducing protocol interfaces that can be implemented by external plugins, making it compatible to a variety of security algorithms and edge devices. A complete implementation was developed as proof-of-concept, and performance tests and experiments were made to evaluate the feasibility of the solution. Results show that EdgeSec framework can greatly improve the security of Mobile-Hub and similar IoT middlewares by increasing its compatibility and flexibility, and ensuring all the basic security protections.

Fifth Generation (5G) cellular networks will experience a thousand-fold increase in data traffic with over 100 billion connected devices by 2020. In order to support this skyrocketing traffic demand, smaller base stations (BSs) are deployed to increase capacity. However, more BSs increase energy consumption which contributes to operational expenditure (OPEX) and CO2 emissions. Also, an introduction of a plethora of 5G applications running in the mobile devices cause a significant amount of energy consumption in the mobile devices. This thesis presents a novel framework for energy efficiency in 5G cloud radio access networks (C-RAN) by leveraging cloud computing technology. Energy efficiency is achieved in three ways; (i) at the radio side of H-C-RAN (Heterogeneous C-RAN), a dynamic BS switching off algorithm is proposed to minimise energy consumption while maintaining Quality of Service (QoS), (ii) in the BS cloud, baseband workload consolidation schemes are proposed based on simulated annealing and genetic algorithms to minimise energy consumption in the cloud, where also advanced fuzzy based admission control with pre-emption is implemented to improve QoS and resource utilisation (iii) at the mobile device side, Mobile Edge Computing (MEC) is used where computer intensive tasks from the mobile device are executed in the MEC server in the cloud. The simulation results show that the proposed framework effectively reduced energy consumption by up to 48% within RAN and 57% in the mobile devices, and improved network energy efficiency by a factor of 10, network throughput by a factor of 2.7 and resource utilisation by 54% while maintaining QoS.

Fifth Generation (5G) cellular networks will experience a thousand-fold increase in data traffic with over 100 billion connected devices by 2020. In order to support this skyrocketing traffic demand, smaller base stations (BSs) are deployed to increase capacity. However, more BSs increases energy consumption which contributes to operational expenditure (OPEX) and CO2 emissions. Also, an introduction of a plethora of 5G applications running in the mobile devices causes a significant amount of energy consumption in the mobile devices. This thesis presents a novel framework for energy efficiency in 5G cloud radio access networks (C-RAN) by leveraging cloud computing technology. Energy efficiency is achieved in three ways; (i) at the radio side of H-C-RAN (Heterogeneous C-RAN), a dynamic BS switching off algorithm is proposed to minimise energy consumption while maintaining Quality of Service (QoS), (ii) in the BS cloud, baseband workload consolidation schemes are proposed based on simulated annealing and genetic algorithms to minimise energy consumption in the cloud, where also advanced fuzzy based admission control with pre-emption is implemented to improve QoS and resource utilisation (iii) at the mobile device side, Mobile Edge Computing (MEC) is used where computer intensive tasks from the mobile device are executed in the MEC server in the cloud. The simulation results show that the proposed framework effectively reduced energy consumption by up to 48% within RAN and 57% in the mobile devices, and improved network energy efficiency by a factor of 10, network throughput by a factor of 2.7 and resource utilisation by 54% while maintaining QoS.

Industrial automation is critical in today's industry. The majority of new scientific and technological advancements are either enabling technologies or industrial automation application areas. In the past, the two main forms of control systems were distributed control systems (DCS) and programmable logic controllers (PLCs). PLCs have been referred as the "brain" of production systems because they provide the capacity to meet interoperability, reconfigurability, and portability criteria. Today's industrial automation systems rely heavily on control software to ensure that the automation process runs smoothly and efficiently. Furthermore, requirements like flexibility, adaptability, and robustness add to the control software's complexity. As a result, new approaches to building control software are required. The International Electrotechnical Commission attempted to meet these new and impending demands with the new IEC 61499 family of standards for distributed automation systems. The IEC 61499 standard specifies a high-level system design language for distributed data and control. With the advancement of these technologies like edge/fog computing and IIoT, how the control software in future smart factory managed is discussed here. This study aims to do a systematic literature review on PLC, software containers, edge/fog computing and IIoT for future industrial use. The objective is to identify the correspondence between the functional block (IEC 61499) and the container technology such as Docker. The impact of edge computing and the internet of things in industrial automation is also analysed. Since the aim is to do a comparative study, a qualitative explorative study is done, with the purpose to gather rich insight about the field. The analysis of the study mainly focused on four major areas such as deployment, run time, performance and security of these technologies. The result shows that containerisation or container based solutions is the basis for future automation as it outperforms virtual machines in terms of deployment, run time, performance and security.

The rapidly evolving Internet of Things (IoT) systems demands addressing new requirements. This particularly needs efficient deployment of IoT systems to meet the quality requirements such as latency, energy consumption, privacy, and bandwidth utilization. The increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage, known as edge computing. Edge computing may help and complement cloud computing to facilitate deployment of IoT systems and improve their quality. However, deciding where to deploy the various application components is not a straightforward task, and IoT system designer should be supported for the decision. To support the designers, in this thesis we focused on the system qualities, and aimed for three main contributions. First, by reviewing the literature, we identified the relevant and most used qualities and metrics. Moreover, to analyse how computer simulation can be used as a supporting tool, we investigated the edge computing simulators, and in particular the metrics they provide for modeling and analyzing IoT systems in edge computing. Finally, we introduced a method to represent how multiple qualities can be considered in the decision. In particular, we considered distributing Deep Neural Network layers as a use case and raked the deployment options by measuring the relevant metrics via simulation. / <p>Note: The papers are not included in the fulltext online</p>

This thesis tackles the topic of serving IoT applications in the computing continuum. It proposes an approach to place applications in the tiers of the continuum, considering latency and energy as predefined metrics. It presents a system model to represent the computing continuum environment, and then, defines an optimization function that is tailored to meet the specific requirements of the IoT applications. The optimization function addresses the relationship between latency and energy consumption in the framework of IoT service provision, and it is implemented in two different directions: (1) the first direction uses a modified Genetic algorithm, and (2) the second direction utilizes the Machine learning concept. To evaluate the performance of the proposed approach, we incorporate different testbed setups and network configurations. All the setups and configurations are designed to represent the diverse demands of IoT applications. Then, different algorithms (such as Non-dominated Sorting Genetic Algorithm (NSGA), Brute Force, and Machine Learning) are implemented to provide different application placement scenarios. The results highlight the efficiency of the proposed approach in comparison with the Brute Force optimal solution while meeting the application requirements. This thesis proposes an optimized solution for serving IoT applications in the computing continuum environment. It considers two essential metrics (latency and energy consumption) in the applications placement processes while meeting the diverse functional and non-functional requirements of these applications. The study provides insights and ideas for future research to refine strategies that will minimize latency and energy consumption. It also urges researchers to consider more metrics while developing and implementing IoT applications. The requirements related to computing resources and performance levels make the development and implementation of these applications complex and challenging. This study serves as a foundational stepping stone towards addressing those challenges.

<p dir="ltr">The onset of smart manufacturing signifies a crucial shift in the industrial landscape, underscoring the pressing need for systems capable of adapting to and managing the complex dynamics of modern production environments. In this context, the importance of smart monitoring becomes increasingly apparent, serving as a vital tool for ensuring operational efficiency and reliability. Inspired by the critical role of auditory perception in human decision-making, this study investigated the application of machine sound recognition for practical use in manufacturing environments. Addressing the challenge of utilizing machine sounds in the loud noises of factories, the study employed an Internal Sound Sensor (ISS).</p><p dir="ltr">The study examined how sound propagates through structures and further explored acoustic characteristics of the ISS, aiming to apply these findings in machine monitoring. To leverage the ISS effectively and achieve a higher level of monitoring, a smart sound monitoring framework was proposed to integrate sound monitoring with machine data and human-machine interface. Designed for applicability and cost effectiveness, this system employs real-time edge computing, making it adaptable for use in various industrial settings.</p><p dir="ltr">The proposed framework and ISS deployed across a diverse range of production environments, showcasing a leap forward in the integration of smart technologies in manufacturing. Their application extends beyond continuous manufacturing to include discrete manufacturing systems, demonstrating adaptability. By analyzing sound signals from various production equipment, this study delves into developing machine sound recognition models that predict operational states and productivity, aiming to enhance manufacturing efficiency and oversight on real factory floors. This comprehensive and practical approach underlines the framework's potential to revolutionize operational management and manufacturing productivity. The study progressed to integrating manufacturing context with sound data, advancing towards high-level monitoring for diagnostic predictions and digital twin. This approach confirmed sound recognition's role in manufacturing diagnostics, laying a foundation for future smart monitoring improvements.</p>

This dissertation presents novel approaches to optimizing convolutional neural network (CNN) architectures for connected autonomous vehicle (CAV) workload on edge, tailored to surmount the challenges inherent in cooperative perception under the stringent resource constraints of edge devices (an endpoint on the network, the interface between the data center and the real world). Employing a modular methodology, this research utilizes the insights from granular examination of CAV perception workloads on edge platforms, identifying and analyzing critical bottlenecks. Through memory contention-aware neural architecture search (NAS), coupled with multi-objective optimization (MOO) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II), this work dynamically optimizes CNN architectures, focusing on reducing memory cost, layer configuration and parameter optimization to reach set hardware constraints whilst maintaining a target precision performance. The results of this exploration are significant, achieving a 63% reduction in memory usage while maintaining a precision rate above 80% for CAV relevant object classes. This dissertation makes novel contributions to the field of edge computing in CAVs, offering a scalable and automated pipeline framework for dynamically obtaining an optimized model for given constraints, thus enabling CAV workloads on edge. In future research, this dissertation also opens multiple different venues for areas of integration. The modular aspect of the pipeline allows for security, privacy, scalability, and energy constraints to be added natively. Through detailed layer by layer analysis and refinement, this dissertation can ensure that CAVs can fully utilize any suitable edge device for the workload requested to realize autonomous driving for everyone.