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Blockchain-Assisted Trust Framework for Increased Survivability in Internet of Vehicles

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Internet Technology Letters 2025年第3期8卷

作者： Sharma, Kanta Prasad Al-Rashed, Atheer Johar, M.G.M. Alsoud, Anas Ratib Singh, Amrita Dey, Protyay Pant, Shivani Amity University Uttar Pradesh Greater Noida India College of Administrative Sciences Applied Science University Al Eker Bahrain Management and Science University Selangor Shah Alam Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali India NIMS School of Computing Science and Artificial Intelligence NIMS University Rajasthan Jaipur India Uttaranchal Institute of Technology Uttaranchal University Dehradun India

VANETs enhance traffic efficiency and road safety, but they can be attacked by malicious vehicles. These malicious vehicles can cause accidents or endanger lives by broadcasting false event messages and disrupting Internet of Vehicles applications. Before responding to sender messages, receiver vehicles must estimate the legitimacy and trustworthiness of the source vehicles. Existing solutions struggle to balance security and efficiency effectively. This paper introduces a model that combines the advantages of blockchain technology and trust model models to improve the trustworthiness, efficacy, and security of vehicular networks, alongside secure trust-based optimized routing. Extensive experiments demonstrate the security and efficiency of the proposed model. The proposed model is adaptable to diverse VANET scenarios, addressing all security and privacy needs more comprehensively than current trust schemes. Efficiency analysis and simulation results show that our proposed framework outperforms baseline models, highlighting its security, effectiveness, and robustness in enhancing IoV communication security. © 2025 John Wiley & Sons Ltd.

关键词： Highway accidents

Dynamics of stratified-convected Eyring-Powell nanoliquid featuring chemically reactive species and Ohmic dissipation: Application of Levenberg-Marquardt artificial neural networks(ALM-ANNs)

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International Journal of Heat and Fluid Flow 2024年 108卷

作者： Shah, Zahoor Waqas, M. Asif Zahoor Raja, Muhammad Shahzad, Faisal Zamri, Nurnadiah Juraev, Nizomiddin Alanazi, Meznah M. Department of Mathematics COMSATS University Islamabad Islamabad Campus Pakistan Department of Computer Science National University of Technology Islamabad44000 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Mathematics Mohi-Ud-Din Islamic University Nerian Sharif AJK Pakistan Faculty of Chemical Engineering New Uzbekistan University Tashkent Uzbekistan Scientific and Innovation Department Tashkent State Pedagogical University Tashkent Uzbekistan Department of Physics College of Science Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut Malaysia

Magnetic nanoliquids are pondered cutting-edge heat transference liquids, representing a modern generation because of their aptitude to function as smart liquids. The deployed magnetism externally exerts a readily manageable impact, escalating their adaptableness and practicality in various applications. In this analysis, electro magnetized Powell-Eyring rheological nanoliquid is modeled considering stretching cylinder based buoyancy-driven flow. Transport expressions include thermal radiation, Brownian diffusion, chemical reaction, thermophoresis, and thermal source effect. Prandtl theory of boundary-layer (BL) assists to develop the governing problems. Appropriate transformations are deployed to obtain the dimensionless problems which are then computed numerically through the novel Levenberg-Marquardt artificial neural networks(ALM-ANNs). It is worth to mention that the close agreement between the error analysis of the reference dataset and the proposed datasets roughly from E−10 to E−03, further justifies the approaches utilized in this study. Besides the excellent measures of performance in terms of MSE are achieved at level 5.77E−11, 4.41E−11, 2.09E−12, 2.16E−11, 1.03E−11, 1.65E−11, 2.00E−11, and 1.91E−11against 272, 416, 266, 391, 142, 366, 425, and 508 epochs for first instance of all eight various scenarios. The acquired outcomes are compared with alternate analytical (homotopy) scheme and good agreement is witnessed. Furthermore it is noticed that as the values of Pr,RandS1 increase in size, so does the EPFM temperature profile θ, and the evaluation of AE is between E-04 to E-10. © 2024

关键词： Boundary layers

Statistical Analysis of Intrinsic High-Frequency Characteristic Fluctuation of Emerging Silicon Gate-All-Around Nanosheet (NS) MOSFETs at Sub-3-nm Nodes

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Statistical Analysis of Intrinsic High-Frequency Characteris...

IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sekhar Reddy Kola Yiming Li Min-Hui Chuang Kazuhiko Endo Seiji Samukawa Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Institute of Communications Engineering Department of Electronics and Electrical Engineering Institute of Biomedical Engineering Center for mm Wave Smart Radar System and Technologies National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Fluid Science Tohoku University 2-1-1 Katahira Aoba-ku Sendai Japan National Institute of Advanced Industrial Science and Technology Tsukuba Japan

In recent years, the GAA NS Si MOSFET has been explored as a leading technology. However, the intrinsic parameters of GAA NS Si MOSFETs are affected to varying degrees by various fluctuation sources, Statistically independent and identically distributed $(iid)$ assumptions on the aforementioned random variables overestimate the variability of high-frequency characteristics, compared with considering all fluctuation factors simultaneously. Notably, the random nanosized metal grains dominates the variations of voltage gain, cut-off frequency, and 3dB frequency because the random work functions strongly alter the channel surface potential.

关键词： MOSFET Fluctuations Statistical analysis Gallium arsenide Cutoff frequency Metals Logic gates

Automatic Scatterplot Design Optimization for Clustering Identification

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arXiv 2022年

作者： Quadri, Ghulam Jilani Nieves, Jennifer Adorno Wiernik, Brenton M. Rosen, Paul Department of Computer Science University of North Carolina Chapel HillNC27599 United States Department of Computer Science and Engineering University of South Florida TampaFL33620 United States Department of Psychology University of South Florida TampaFL33620 United States Scientific Computing and Imaging Institute University of Utah Salt Lake CityUT84112 United States

Scatterplots are among the most widely used visualization techniques. Compelling scatterplot visualizations improve understanding of data by leveraging visual perception to boost awareness when performing specific visual analytic tasks. Design choices in scatterplots, such as graphical encodings or data aspects, can directly impact decision-making quality for low-level tasks like clustering. Hence, constructing frameworks that consider both the perceptions of the visual encodings and the task being performed enables optimizing visualizations to maximize efficacy. In this paper, we propose an automatic tool to optimize the design factors of scatterplots to reveal the most salient cluster structure. Our approach leverages the merge tree data structure to identify the clusters and optimize the choice of subsampling algorithm, sampling rate, marker size, and marker opacity used to generate a scatterplot image. We validate our approach with user and case studies that show it efficiently provides high-quality scatterplot designs from a large parameter space. © 2022, CC BY.

关键词： Visualization

Assessing the use cases of persistent memory in high-performance scientific computing

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arXiv 2021年

作者： Fridman, Yehonatan Snir, Yaniv Rusanovsky, Matan Zvi, Kfir Levin, Harel Hendler, Danny Attiya, Hagit Oren, Gal Department of Computer Science Ben-Gurion University of the Negev Israel Israel Atomic Energy Commission Israel Google Scientific Computing Center Nuclear Research Center – Negev Israel Department of Computer Science Technion – Israel Institute of Technology Israel

As the High Performance computing (HPC) world moves towards the Exa-Scale era, huge amounts of data should be analyzed, manipulated and stored. In the traditional storage/memory hierarchy, each compute node retains its data objects in its local volatile DRAM. Whenever the DRAM’s capacity becomes insufficient for storing this data, the computation should either be distributed between several compute nodes, or some portion of these data objects must be stored in a non-volatile block device such as a hard disk drive (HDD) or an SSD storage device. These standard block devices offer large and relatively cheap non-volatile storage, but their access times are orders-of-magnitude slower than those of DRAM. Optane™ DataCenter Persistent Memory Module (DCPMM) [1], a new technology introduced by Intel, provides non-volatile memory that can be plugged into standard memory bus slots (DDR DIMMs) and therefore be accessed much faster than standard storage devices. In this work, we present and analyze the results of a comprehensive performance assessment of several ways in which DCPMM can 1) replace standard storage devices, and 2) replace or augment DRAM for improving the performance of HPC scientific computations. To achieve this goal, we have configured an HPC system such that DCPMM can service I/O operations of scientific applications, replace standard storage devices and file systems (specifically for diagnostics and checkpoint-restarting), and serve for expanding applications’ main memory. We focus on keeping the scientific codes with as few changes as possible, while allowing them to access the NVM transparently as if they access persistent storage. Our results show that DCPMM allows scientific applications to fully utilize nodes’ locality by providing them with sufficiently-large main memory. Moreover, it can also be used for providing a high-performance replacement for persistent storage. Thus, the usage of DCPMM has the potential of replacing standard HDD and SSD storage

关键词： Dynamic random access storage

A pragmatic workflow for research software engineering in computational science

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arXiv 2023年

作者： Maric, Tomislav Gläser, Dennis Lehr, Jan-Patrick Papagiannidis, Ioannis Lambie, Benjamin Bischof, Christian Bothe, Dieter Mathematical modeling and analysis Mathematics department TU Darmstadt Darmstadt64287 Germany Institute for Modelling Hydraulic and Environmental Systems Department of Hydromechanics and Modelling of Hydrosystems University of Stuttgart Stuttgart70569 Germany Institute for Scientific Computing Department of Computer Science TU Darmstadt Darmstadt64289 Germany Research Field Energy & Environment TU Darmstadt Darmstadt64287 Germany

University research groups in Computational science and Engineering (CSE) lack funding and personnel for Research Software Engineering (RSE), which leads to a focus on journal publications over sustainable software development and reproducible results. This negatively impacts CSE research output, with data and software related to publications being difficult to find, reproduce, or reuse. We propose an RSE workflow for CSE that addresses these challenges and improves research output quality by applying established software engineering practices such as version control and testing, as well as cross-linking software with data and publications. The workflow is minimal in terms of overhead and produces tested software cross-linked with publications and reproducible results. We define research software quality as the ability to quickly find and understand related publications, data, and software, and to extend existing code to realize new research ideas. © 2023, CC BY.

关键词： computer software reusability

OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching

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arXiv 2021年

作者： Hoshino, Hana Ota, Kei Kanezaki, Asako Yokota, Rio School of Computing Department of Computer Science Tokyo Institute of Technology Japan Information Technology R&D Center Mitsubishi Electric Corporation Japan Global Scientific Information and Computing Center Tokyo Institute of Technology Japan

Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering can be tedious. However, prior IRL algorithms use on-policy transitions, which require intensive sampling from the current policy for stable and optimal performance. This limits IRL applications in the real world, where environment interactions can become highly expensive. To tackle this problem, we present Off-Policy Inverse Reinforcement Learning (OPIRL), which (1) adopts off-policy data distribution instead of on-policy and enables significant reduction of the number of interactions with the environment, (2) learns a reward function that is transferable with high generalization capabilities on changing dynamics, and (3) leverages mode-covering behavior for faster convergence. We demonstrate that our method is considerably more sample efficient and generalizes to novel environments through the experiments. Our method achieves better or comparable results on policy performance baselines with significantly fewer interactions. Furthermore, we empirically show that the recovered reward function generalizes to different tasks where prior arts are prone to fail. © 2021, CC BY.

关键词： Inverse problems

Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations

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arXiv 2024年

作者： Lee, Jae Yong Schotthöfer, Steffen Xiao, Tianbai Krumscheid, Sebastian Frank, Martin Center for Artificial Intelligence and Natural Sciences Korea Institute for Advanced Study Seoul Korea Republic of Oak Ridge National Laboratory Computer Science and Mathematics Division Oak RidgeTN United States State Key Laboratory of High Temperature Gas Dynamics Centre for Interdisciplinary Research in Fluids Institute of Mechanics Chinese Academy of Sciences Beijing China School of Engineering Science University of Chinese Academy of Sciences Beijing China Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany

This work explores the application of deep operator learning principles to a problem in statistical physics. Specifically, we consider the linear kinetic equation, consisting of a differential advection operator and an integral collision operator, which is a powerful yet expensive mathematical model for interacting particle systems with ample applications, e.g., in radiation transport. We investigate the capabilities of the Deep Operator network (DeepONet) approach to modelling the high dimensional collision operator of the linear kinetic equation. This integral operator has crucial analytical structures that a surrogate model, e.g., a DeepONet, needs to preserve to enable meaningful physical simulation. We propose several DeepONet modifications to encapsulate essential structural properties of this integral operator in a DeepONet model. To be precise, we adapt the architecture of the trunk-net so the DeepONet has the same collision invariants as the theoretical kinetic collision operator, thus preserving conserved quantities, e.g., mass, of the modeled many-particle system. Further, we propose an entropy-inspired data-sampling method tailored to train the modified DeepONet surrogates without requiring an excessive expensive simulation-based data generation. © 2024, CC BY-NC-SA.

关键词： Kinetic energy

POST-DISASTER RECOLONIZATION of MANGROVE FORESTS with A STOCHASTIC AGENT-BASED MODEL

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POST-DISASTER RECOLONIZATION of MANGROVE FORESTS with A STOC...

2019 Geomatics and Data science: Towards Adaptive Management in a Changing World, PhilGEOS x GeoAdvances 2019

作者： Bongolan, V.P. Branzuela, J.E. Torres, G.M. Scientific Computing Laboratory Department of Computer Science University of the Philippines Diliman Philippines

Mangrove forests in the Philippine coastline are susceptible to severe damage due to tropical storms. These mangrove forests provide a home for other plants and animals as well as providing resources for people living in coastal areas. Thus, it is important to promote proper conservation and judicious replanting in areas affected by storms. Since different species vary on their tolerance to physical conditions such as water salinity and soil composition, the appropriate genus must be used in reforestation efforts. This study aims to model the change in soil composition due to the introduction of a non-native species, Rhizophora mucronata, and restoring soil condition to aid recolonization of the existing native species, Avicennia and Sonneratia. The study uses an agent-based model for the prediction of the regenerative behaviour of mangrove stands consisting of the native species and the planted or non-native species in a fragmented habitat, with the use of spatio-temporal coloured noise to simulate stochastic seedling dispersal and subject to storm damage. The model uses Salmo and Juanico's model for mangrove growth. Stochastic experiments were carried out in a shoreline habitat with an existing native population of varying ages and a larger population of planted, non-native seedlings. The GIS data of Bangrin Marine Protected Area was used to simulate the recovery trajectory of the stand after typhoon Chan-hom of 2009. © 2020 Authors.

关键词： Storms