9,093 research outputs found

    Internet of things enabled parking management system using long range wide area network for smart city

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    As the Internet of Things (IoT) evolves, it paves the way for vital smart city applications, with the Smart Parking Management System (SPMS) standing as a prime example. This research introduces a novel IoT-driven SPMS that leverages Long Range Wide Area Network (LoRaWAN) technology, termed as IoT-SPMS-LoRaWAN, to surmount typical restrictions related to communication range, energy usage, and implementation cost seen in traditional systems. IoT-SPMS-LoRaWAN features intelligent sensing nodes that incorporate an Arduino UNO microcontroller and two sensors—a triaxial magnetic sensor and a waterproof ultrasonic sensor. These components collaboratively detect vehicle occupancy and transmit this data to the server via a LoRaWAN gateway. Notably, the integration of LoRa technology enables extensive network coverage and energy efficiency. Users are provided with real-time updates on parking availability via the accessible AllThingsTalk Maker graphical user interface. Additionally, the system operates independently, sustained by a solar-powered rechargeable battery. Practical testing of IoT-SPMS-LoRaWAN under various scenarios validates its merits in terms of functionality, ease of use, reliable data transmission, and precision. Its urban implementation is expected to alleviate traffic congestion, optimize parking utilization, and elevate awareness about available parking spaces among users. Primarily, this study enriches the realm of smart city solutions by enhancing the efficiency of parking management and user experience via IoT

    Graduate Catalog of Studies, 2023-2024

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    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

    Review of Peer-to-Peer (P2P) Lending Based on Blockchain

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    Peer-to-Peer (P2P) lending is a financing business model that has gained popularity in recent years due to the ease of loan application, disbursement, and repayment processes. The volume of Peer-to-Peer (P2P) Lending transactions have a significant growth. One of the reasons for the popularity of Peer-to-Peer (P2P) lending is its utilization of technology in both the application and loan repayment processes. One such technology gaining traction in Peer-to-Peer (P2P) lending is blockchain technology. The popularity of blockchain technology lies in its ability to enhance the transparency of the transaction process. This literature study aims to address three main questions: What are the characteristics of blockchain suitable for Peer-to-Peer (P2P) lending , the benefits of implementing blockchain technology in Peer-to-Peer (P2P) lending and the challenges of Peer-to-Peer (P2P) lending based on blockchain. The findings reveal that there are characteristics of blockchain that can be applied to Peer-to-Peer (P2P) lending, bringing numerous benefits to the overall Peer-to-Peer (P2P) lending process. However, challenges persist in the implementation of blockchain technology in Peer-to-Peer (P2P) lending. The insights gained from this literature review are intended to guide researchers interested in studying the application of blockchain technology in the context of Peer-to-Peer (P2P) lending

    Generative AI

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