898 research outputs found

    Deep generative models for network data synthesis and monitoring

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    Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network. Although networks inherently have abundant amounts of monitoring data, its access and effective measurement is another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset without leaking commercial sensitive information. Second, it could be very expensive to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources in the network element that can be applied to support the measurement function are too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex structure. Various emerging optimization-based solutions (e.g., compressive sensing) or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet meet the current network requirements. The contributions made in this thesis significantly advance the state of the art in the domain of network measurement and monitoring techniques. Overall, we leverage cutting-edge machine learning technology, deep generative modeling, throughout the entire thesis. First, we design and realize APPSHOT , an efficient city-scale network traffic sharing with a conditional generative model, which only requires open-source contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time network telemetry system with latent GANs and spectral-temporal networks. Finally, we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through this research are summarized, and interesting topics are discussed for future work in this domain. All proposed solutions have been evaluated with real-world datasets and applied to support different applications in real systems

    Joint Congestion and Contention Avoidance in a Scalable QoS-Aware Opportunistic Routing in Wireless Ad-Hoc Networks

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    Opportunistic routing (OR) can greatly increase transmission reliability and network throughput in wireless ad-hoc networks by taking advantage of the broadcast nature of the wireless medium. However, network congestion is a barrier in the way of OR\u27s performance improvement, and network congestion control is a challenge in OR algorithms, because only the pure physical channel conditions of the links are considered in forwarding decisions. This paper proposes a new method to control network congestion in OR, considering three types of parameters, namely, the backlogged traffic, the traffic flows\u27 Quality of Service (QoS) level, and the channel occupancy rate. Simulation results show that the proposed algorithm outperforms the state-of-the-art algorithms in the context of OR congestion control in terms of average throughput, end-to-end delay, and Packet Delivery Ratio (PDR). Due to the higher PDR at different traffic loads and different node densities, it can be concluded that the proposed algorithm also improves network scalability, which is very desirable given the recent changes in wireless networks

    20th SC@RUG 2023 proceedings 2022-2023

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    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Informationsströme in digitalen Kulturen

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    Wir sind umgeben von einer Vielzahl an Informationsströmen, die uns selbstverständlich erscheinen. Um diese digitalen Kulturen zu beschreiben, entwickeln medienwissenschaftliche Arbeiten Theorien einer Welt im Fluss. Dabei erliegen ihre Diagnosen oftmals einem Technikfetisch und vernachlässigen gesellschaftliche Strukturen. Mathias Denecke legt eine systematische Kritik dieser Theoriebildung vor. Dazu zeichnet er die Geschichte der Rede von strömenden Informationen in der Entwicklung digitaler Computer nach und diskutiert, wie der Begriff für Gegenwartsbeschreibungen produktiv gemacht werden kann

    20th SC@RUG 2023 proceedings 2022-2023

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    Digital Twins and Blockchain for IoT Management

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    We live in a data-driven world powered by sensors getting data from anywhere at any time. This advancement is possible thanks to the Internet of Things (IoT). IoT embeds common physical objects with heterogeneous sensing, actuating, and communication capabilities to collect data from the environment and people. These objects are generally known as things and exchange data with other things, entities, computational processes, and systems over the internet. Consequently, a web of devices and computational processes emerges involving billions of entities collecting, processing, and sharing data. As a result, we now have an internet of entities/things that process and produce data, an ever-growing volume that can easily exceed petabytes. Therefore, there is a need for novel management approaches to handle the previously unheard number of IoT devices, processes, and data streams. This dissertation focuses on solutions for IoT management using decentralized technologies. A massive number of IoT devices interact with software and hardware components and are owned by different people. Therefore, there is a need for decentralized management. Blockchain is a capable and promising distributed ledger technology with features to support decentralized systems with large numbers of devices. People should not have to interact with these devices or data streams directly. Therefore, there is a need to abstract access to these components. Digital twins are software artifacts that can abstract an object, a process, or a system to enable communication between the physical and digital worlds. Fog/edge computing is the alternative to the cloud to provide services with less latency. This research uses blockchain technology, digital twins, and fog/edge computing for IoT management. The systems developed in this dissertation enable configuration, self-management, zero-trust management, and data streaming view provisioning from a fog/edge layer. In this way, this massive number of things and the data they produce are managed through services distributed across nodes close to them, providing access and configuration security and privacy protection

    Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

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    Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the Communications Societ

    Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation

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    The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics
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