Titlebook: Industrial Networks and Intelligent Systems; 10th EAI Internation Nguyen-Son Vo,Dac-Binh Ha,Haejoon Jung Conference proceedings 2024 ICST I

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Optimal Task Scheduling in 6G Networks: A Variational Quantum Computing Approachomous vehicles, and the Internet of Things (IoT). With the network landscape becoming a more complex and diverse ecosystem, it is critical to advance conventional scheduling algorithms in order to guarantee the necessary efficiency and performance. In this regard, quantum computing can significantly

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Hybrid Federated and Multi-agent DRL-Based Resource Allocation in Digital Twin-IoV Networkstasks in UAV-supported Internet-of-Vehicles networks. By merging asynchronous federated learning with multi-agent deep reinfo- rcement learning, we aim to boost the rate of task completion while simultaneously reducing consumed energy and delay, and consequently improving the system’s overall perfor

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Performance Comparison in Traffic Sign Recognition Using Deep Learning studies have been conducted on Traffic Sign Recognition (TSR) to enhance road safety and driver assistance systems by enabling vehicles to autonomously detect and interpret traffic signs, providing crucial information to drivers in real-time. This paper examines various traffic sign detection and c

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Enhancing Energy Harvesting Efficiency for IRS-Aided TS-SWIPT Network with Practical Phase Shiftsw cost and low energy consumption. In this work, we investigate the simultaneous wireless information and power transfer (SWIPT) network with time-switching (TS) receivers where the base station (BS) sends both information and energy signals to the users with the aid of IRS. The phase shifts at the