17,997 research outputs found

    Securing NextG networks with physical-layer key generation: A survey

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    As the development of next-generation (NextG) communication networks continues, tremendous devices are accessing the network and the amount of information is exploding. However, with the increase of sensitive data that requires confidentiality to be transmitted and stored in the network, wireless network security risks are further amplified. Physical-layer key generation (PKG) has received extensive attention in security research due to its solid information-theoretic security proof, ease of implementation, and low cost. Nevertheless, the applications of PKG in the NextG networks are still in the preliminary exploration stage. Therefore, we survey existing research and discuss (1) the performance advantages of PKG compared to cryptography schemes, (2) the principles and processes of PKG, as well as research progresses in previous network environments, and (3) new application scenarios and development potential for PKG in NextG communication networks, particularly analyzing the effect and prospects of PKG in massive multiple-input multiple-output (MIMO), reconfigurable intelligent surfaces (RISs), artificial intelligence (AI) enabled networks, integrated space-air-ground network, and quantum communication. Moreover, we summarize open issues and provide new insights into the development trends of PKG in NextG networks

    Typhoon cloud image prediction based on enhanced multi-scale deep neural network

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    Typhoons threaten individuals’ lives and property. The accurate prediction of typhoon activity is crucial for reducing those threats and for risk assessment. Satellite images are widely used in typhoon research because of their wide coverage, timeliness, and relatively convenient acquisition. They are also important data sources for typhoon cloud image prediction. Studies on typhoon cloud image prediction have rarely used multi-scale features, which cause significant information loss and lead to fuzzy predictions with insufficient detail. Therefore, we developed an enhanced multi-scale deep neural network (EMSN) to predict a 3-hour-advance typhoon cloud image, which has two parts: a feature enhancement module and a feature encode-decode module. The inputs of the EMSN were eight consecutive images, and a feature enhancement module was applied to extract features from the historical inputs. To consider that the images of different time steps had different contributions to the output result, we used channel attention in this module to enhance important features. Because of the spatially correlated and spatially heterogeneous information at different scales, the feature encode-decode module used ConvLSTMs to capture spatiotemporal features at different scales. In addition, to reduce information loss during downsampling, skip connections were implemented to maintain more low-level information. To verify the effectiveness and applicability of our proposed EMSN, we compared various algorithms and explored the strengths and limitations of the model. The experimental results demonstrated that the EMSN efficiently and accurately predicted typhoon cloud images with higher quality than in the literature

    Workflow to detect ship encounters at sea with GIS support

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceAccording to the United Nations, more than 80% of the global trade is currently transported by sea. The Portuguese EEZ has a very extensive area with high maritime traffic, among which illicit activities may occur. This work aims to contribute to the official control of illegal transshipment actions by studying and proposing a new way of detecting encounters between ships. Ships with specific characteristics use an Automatic Identification System (AIS) on board which transmits a signal via radio frequencies, allowing shore stations to receive static and dynamic data from the ship. Thus, there is an increase in maritime situational awareness and, consequently, in the safety of navigation. The methodology of this dissertation employs monthly and daily AIS data in the study area, which is located in southern mainland Portugal. A bibliometric and content analysis was performed in order to assess the state of the art concerning geospatial analysis models of maritime traffic, based on AIS data, and focus on anomalous behaviour detection. Maritime traffic density maps were created with the support of a GIS (QGIS software), which allowed to characterize the maritime traffic in the study area and, subsequently, to pattern the locations where ship encounters occur. The algorithm to detect ship-to-ship meetings at sea was developed using a rule-based methodology. After analysis and discussion of results, it was found that the areas where the possibility of ship encounters at sea is greatest are away from the main shipping lanes, but close to areas with fishing vessels. The study findings and workflow are useful for decision making by the competent authorities for patrolling the maritime areas, focusing on the detection of illegal transhipment actions.Segundo as Nações Unidas, mais de 80% do comércio global é, atualmente, transportado por via marítima. A ZEE portuguesa tem uma área muito extensa, com tráfego marítimo elevado, entre o qual podem ocorrer atividades ilícitas. Este trabalho pretende contribuir para o controlo oficial de ações de transbordo ilegal, estudando e propondo uma nova forma de deteção de encontros entre navios. Os navios com determinadas características, utilizam a bordo um Automatic Identification System (AIS) que transmite sinal através de frequências rádio, permitindo que estações em terra recebam dados estáticos e dinâmicos do navio. Deste modo, verifica-se um aumento do conhecimento situacional marítimo e, consequentemente, da segurança da navegação. Foi realizada uma análise bibliométrica e de conteúdo a fim de avaliar o estado da arte referente a modelos de análise geoespacial do tráfego marítimo, com base em dados AIS, e foco na deteção de comportamentos anómalos. Na metodologia desta dissertação, são utilizados dados AIS mensais e diários na área de estudo, situada a sul de Portugal Continental. Foram criados mapas de densidade de tráfego marítimo com o apoio de um SIG (software QGIS), o que permitiu caracterizar o tráfego marítimo na área de estudo e, posteriormente, padronizar os locais onde ocorrem encontros entre navios. O algoritmo para detetar encontros entre navios no mar foi desenvolvido através de uma metodologia baseada em regras. Após análise e discussão de resultados, constatou-se que as áreas onde a possibilidade de ocorrer encontros de navios no mar é maior, encontram-se afastadas dos corredores principais de navegação, mas próximas de zonas com embarcações de pesca. Os resultados do estudo e o workflow desenvolvidos são úteis à tomada de decisão pelas autoridades competentes por patrulhar as áreas marítimas, com incidência na deteção de ações de transbordo ilegal

    POINTNET++ TRANSFER LEARNING FOR TREE EXTRACTION FROM MOBILE LIDAR POINT CLOUDS

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    Trees are an essential part of the natural and urban environment due to providing crucial benefits such as increasing air quality and wildlife habitats. Therefore, various remote sensing and photogrammetry technologies, including Mobile Laser Scanner (MLS), have been recently introduced for precise 3D tree mapping and modeling. The MLS provides densely 3D LiDAR point clouds from the surrounding, which results in measuring applicable information of trees like stem diameter or elevation. In this paper, a transfer learning procedure on the PointNet++ has been proposed for tree extraction. Initially, two steps of converting the MLS point clouds into same-length smaller sections and eliminating ground points have been conducted to overcome the massive volume of MLS data. The algorithm was tested on four LiDAR datasets ranging from challengeable urban environments containing multiple objects like tall buildings to railway surroundings. F1-Score accuracy was gained at around 93% and 98%, which showed the feasibility and efficiency of the proposed algorithm. Noticeably, the algorithms also measured geometrical information of extracted trees such as 2D coordinate space, height, stem diameter, and 3D boundary tree locations

    Natural and Technological Hazards in Urban Areas

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    Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    AI-Oriented Two-Phase Multi-Factor Authentication in SAGINs: Prospects and Challenges

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    Space-air-ground integrated networks (SAGINs), which have emerged as an expansion of terrestrial networks, provide flexible access, ubiquitous coverage, high-capacity backhaul, and emergency/disaster recovery for mobile users (MUs). While the massive benefits brought by SAGIN may improve the quality of service, unauthorized access to SAGIN entities is potentially dangerous. At present, conventional crypto-based authentication is facing challenges, such as the inability to provide continuous and transparent protection for MUs. In this article, we propose an AI-oriented two-phase multi-factor authentication scheme (ATMAS) by introducing intelligence to authentication. The satellite and network control center collaborate on continuous authentication, while unique spatial-temporal features, including service features and geographic features, are utilized to enhance the system security. Our further security analysis and performance evaluations show that ATMAS has proper security characteristics which can meet various security requirements. Moreover, we shed light on lightweight and efficient authentication mechanism design through a proper combination of spatial-temporal factors.Comment: Accepted by IEEE Consumer Electronics Magazin

    Dataset Distillation with Convexified Implicit Gradients

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    We propose a new dataset distillation algorithm using reparameterization and convexification of implicit gradients (RCIG), that substantially improves the state-of-the-art. To this end, we first formulate dataset distillation as a bi-level optimization problem. Then, we show how implicit gradients can be effectively used to compute meta-gradient updates. We further equip the algorithm with a convexified approximation that corresponds to learning on top of a frozen finite-width neural tangent kernel. Finally, we improve bias in implicit gradients by parameterizing the neural network to enable analytical computation of final-layer parameters given the body parameters. RCIG establishes the new state-of-the-art on a diverse series of dataset distillation tasks. Notably, with one image per class, on resized ImageNet, RCIG sees on average a 108% improvement over the previous state-of-the-art distillation algorithm. Similarly, we observed a 66% gain over SOTA on Tiny-ImageNet and 37% on CIFAR-100
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