187 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

    Low Power Memory/Memristor Devices and Systems

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    This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within

    AI/ML Algorithms and Applications in VLSI Design and Technology

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    An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

    Spatial Queries for Indoor Location-based Services

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    Indoor Location-based Services (LBS) facilitate people in indoor scenarios such as airports, train stations, shopping malls, and office buildings. Indoor spatial queries are the foundation to support indoor LBSs. However, the existing techniques for indoor spatial queries are limited to support more advanced queries that consider semantic information, temporal variations, and crowd influence. This work studies indoor spatial queries for indoor LBSs. Some typical proposals for indoor spatial queries are compared theoretically and experimentally. Then, it studies three advanced indoor spatial queries, a) Indoor Keyword-aware Routing Query. b) Indoor Temporal-variation aware Routing Query. c) Indoor Crowd-aware Routing Query. A series of techniques are proposed to solve these problems.</p

    Spatial data analysis for intelligent buildings: awareness of context and data uncertainty

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    Intelligent buildings are among the most active Internet-of-Things (IoT) verticals, encompassing various IoT-enabled devices and sensing technologies for digital transformation. Analysis of spatial data, a very common type of data collected in intelligent buildings, offers a lot of insights for many purposes such as facilitating space management and enhancing the utilization efficiency of buildings. In this paper, we recognize two major challenges in spatial data analysis for intelligent buildings (SDAIB): (1) the complicated analytical contexts that are related to the building space and internal entities and (2) the uncertainty of spatial data due to the limitations of positioning and other sensing technologies. To address these challenges, we identify and categorize different kinds of analytical contexts and spatial data uncertainties in SDAIB, and propose a unified modeling framework for handling them. Furthermore, we showcase how the proposed framework and the associated modeling techniques are used to enable context-aware and uncertainty-aware SDAIB, in the tasks of hotspot discovery, path planning, semantic trajectory generation, and distance monitoring. Finally, we offer several research directions of SDAIB, in line with the emerging trends of the IoT

    Energy Efficiency and Indoor Environment Quality

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    This Special Issue addresses a topic of great relevance. In developed countries, there is a higher prevalence of people choosing to spend time indoors. Data show that the time a person spends at home ranges from 60% to 90% of the day, and 30% of that time is spent sleeping, though this varies depending on the individual. Taking into account these data, indoor residential environments have a direct influence on human health. Furthermore, in developing countries, significant levels of indoor pollution make housing unsafe, impacting the health of its inhabitants. Housing is therefore a key health factor for people all over the world: various parameters such as air quality, ventilation, hygrothermal comfort, lighting, physical environment, and building efficiency can contribute to healthy architecture; poor application of these parameters can result in conditions that negatively impact health
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