This paper addresses the significant challenges posed by road safety due to rapid urbanization and increasing vehicular traffic. High-definition (HD) semantic maps are essential for improving decision-making and safet...
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This paper critically analyzes essential management techniques in carried out cryptography. It critiques diverse answers and challenges confronted in coping with virtual keys related to cryptographic programs and the ...
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Low Earth orbit (LEO) satellite networks have attracted extensive research due to their potential to provide high-quality Internet access services. However, the existing TCP variants, which are designed for terrestria...
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ISBN:
(纸本)9798350339864
Low Earth orbit (LEO) satellite networks have attracted extensive research due to their potential to provide high-quality Internet access services. However, the existing TCP variants, which are designed for terrestrial networks, can hardly work in LEO satellite networks with characteristics such as error-prone, bandwidth variations, and link switching. To address these challenges, in this paper we present a new information-centric transport layer protocol LEOTP to guarantee reliable, high-throughput, and low-latency data transmission in LEO satellite networks. It leverages the idea of information-Centric Networking (ICN) with a Request-Response transmission model and in-network caching. The connectionless transmission paradigm in LEOTP makes it resilient to dynamic topology changes. The caches equipped in intermediate nodes help to recover packet loss while the hop-by-hop congestion control mechanism provides a fast reaction to time-varying network conditions. We evaluate the performance of LEOTP in emulated Starlink constellation, which shows that it increases the throughput by 8%-12% with 40%-60% delay reduction compared with the state-of-the-art TCP variants in the transcontinental data transmission.
Cloud computing has become a prevalent technology in recent years, with various types and deployment models available. Although it has become ubiquitous in most applications, there is still a significant number of ind...
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With the advancement of IoT technology, an increasing number of devices and sensors are connecting to the network, leading to data fragmentation due to diverse data standards adopted by different enterprises. Typicall...
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This study introduces the Medical Unique Identification, a secure patient verification process designed for accessing medical records. Utilizing data mining techniques and machine learning algorithms, our focus is on ...
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Agent-based solutions of various architectures have been increasingly been applied to the Railway Traffic Management problem. This paper proposes a decentralised solution to this problem utilising Belief-Desire-Intent...
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To address urban traffic congestion simulation after Traffic Management Initiatives (TMI) implementation, a method combining deep reinforcement learning with environmental optimization is proposed, optimizing the envi...
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This paper is based on the Internet of Things technology, combined with image recognition technology, the construction of intelligent three-dimensional virtual information management system. The system is visually con...
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To improve agricultural production prediction in the context of precision agriculture, this research investigates the combination of machine learning algorithms with IoT technologies. Using cutting-edge technology, pr...
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To improve agricultural production prediction in the context of precision agriculture, this research investigates the combination of machine learning algorithms with IoT technologies. Using cutting-edge technology, precision agriculture provides creative ways to meet the rising need for food production to feed a growing global population. IoT devices, such as sensors and drones, gather large volumes of data on crop health, temperature, humidity, and soil moisture. Accurate crop production projections are produced by processing this data using machine learning techniques. These algorithms can find patterns and connections in historical and current data, which helps farmers make well-informed choices about pest control, fertilization, and irrigation. The uses of machine learning methods, including neural networks, decision trees, and regression models, in agricultural production prediction are covered in this research. The potential for revolutionizing agricultural methods via the combination of IoT and machine learning is enormous, as it might enhance sustainability, efficiency, and crop yields to satisfy the needs of an expanding population.
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