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Blockchain-Assisted Privacy-Preserving and Synchronized Key Agreement for VDTNs

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Chenhao Ming, Yang Liu, Hang Feng, Jie Yang, Mengmeng Xiang, Yang Chang'an University School of Information Engineering Xi'an710064 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Commonwealth Scientific and Industrial Research Organization Australia Swinburne University of Technology Department of Computing Technologies VIC3122 Australia

With the continuous development of digitization evolutions, vehicular digital twin networks (VDTNs) facilitate traffic data and optimization results to be exchanged between the vehicle and digital twin as well as shared among a group of digital twins. However, the data exchange and group sharing processes take place in real-time over public communication channels, which suffer from various security and privacy threats. Key agreement technologies are promising to enable secure data communications for entities, but the existing key agreement schemes generally fail to fulfill the requirements of synchronization, privacy, and entity management for VDTNs. Therefore, we propose a blockchain-assisted privacy-preserving and synchronized key agreement scheme for VDTNs. In the proposed scheme, the anonymous vehicle and digital twin can negotiate a secret session key in the case of synchronization to achieve secure data exchange. Meanwhile, digital twins are capable of utilizing synchronized state information to dynamically establish a common group encryption key but hold individual decryption keys, which guarantee the security of group sharing. Additionally, the proposed scheme is able to protect identity privacy and manage vehicles and digital twins with the assistance of blockchain and smart contract. The security analysis demonstrates that the proposed scheme provides security and privacy assurances for VDTNs. The performance evaluation indicates that it has excellent expressions in terms of efficiency, practicality, and smart contract consumption. © 2004-2012 IEEE.

关键词： Differential privacy

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Global and local complementary multi-path feature fusion network for the classification of crop remote sensing images 4

Global and local complementary multi-path feature fusion net...

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2024 4th International Conference on Artificial Intelligence and Industrial technology Applications, AIITA 2024

作者： Zhang, Keqian Cui, Tengfei Wu, Wei Zhang, Yichi Zhang, Ziqian Si, Qiliang Song, Xiangyang Cheng, Gang Henan Polytechnic University College of Surveying and Land Information Engineering 2001 Century Road Jiaozuo Henan China Boston University Metropolitan College 1010 Commonwealth Avenue BostonMA United States Guilin University of Technology College of Geomatics and Geoinformation No. 319 Yanshan Street Guangxi Guilin China Hohai University School of Artificial Intelligence and Automation No. 8 Focheng West Road Jiangning District Jiangsu Nanjing China Henan University of Science and Technology School of Software No. 48 Xiyuan Road Jianxi District Henan Luoyang China

The accuracy and efficiency of crop distribution information extraction are pivotal in ensuring global food security. In long-time-series optical satellite data, most existing methods focus on extracting spatial features using Convolutional Neural Networks (CNNs), which do not adequately mine and model the spatial-temporal information. The development of the attention mechanism allows for the extraction of global features in remote-sensing images of long temporal sequences. To extract global attentional features with complementary features in crop remote sensing images, we propose a Global and Local Complementary Multi-path Feature Fusion Network (GLMP), which is capable of extracting global features from remote sensing images of long temporal sequence, that enhances the local characteristics of crop images derived from CNNs, thus obtaining more effective multi-scale complementary features. This extraction of features enhances the comprehension of crop images, thereby boosting the performance of associated tasks. Within GLMP, we introduce two pivotal modules: the Hybrid Attention and Convolutional Paths Module (HACM) and the Multi-path Feature Fusion Module (MPFM). These modules synergistically converge multi-path features, yielding more discriminative feature information. Experimental results on the ZueriCrop dataset show that the proposed GLMP technique is effective;it performs promisingly having a total accuracy of 90.2% and an F1 value of 62.5%. Furthermore, the ablation study verifies the substantial improvement in classification accuracy for remote sensing crop images of long-time series in nature, specifically attributed to the HACM and MPFM modules. © Published under licence by IOP Publishing Ltd.

关键词： Convolutional neural networks

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Silicon Waveguide-Based Photonic Switch with drop cast Thermally Tunable Vanadium Oxide Nanoparticles

Silicon Waveguide-Based Photonic Switch with drop cast Therm...

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OptoElectronics and Communications Conference, OECC

作者： Gregory Tanyi Daniel Peace Han-Hao Cheng Guanghui Ren Xuan Hiep Dinh Mohammed Taha Arnan Mitchell Karsten Hoehn Christina Lim Ranjith Unnithan Department of Electrical and Electronic Engineering Faculty of Engineering and Information Technology The University of Melbourne Parkville VIC Australia Mineral Resources CSIRO Commonwealth Scientific Industry and Research Organiasation Australian Research Council Centre of Excellence for Engineered Quantum Systems & School of Mathematics and Physics The University of Queensland St Lucia Queensland Australia Centre for Microscopy and Microanalysis The University of Queensland St Lucia Australia Mineral Resources Commonwealth Scientific Industry and Research Organiasation Integrated Photonics and Applications Centre School of Engineering RMIT University Melbourne Victoria Australia

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

Photonic switches are crucial for optical communications, facilitating the connection and disconnection of optical signals. The demand for compact, high-speed switches has made thermally reconfigurable switches increasingly popular due to their simplicity and compatibility with various thermo-optic materials. Silicon, favored for its CMOS compatibility and cost-effectiveness, serves as an excellent base for these devices. This paper introduces a compact, efficient optical switch combining drop-cast sub-stoichiometric vanadium oxide (VO2-x) nanoparticles with a silicon ridge waveguide with a minimum extinction ratio of 7dB in the C band. The switch is characterized by a broad wavelength of operation as well as a simple and scalable fabrication methodology. The research highlights the effectiveness of VO2-x nanoparticles and silicon waveguides in creating versatile optical switches for diverse applications.

关键词： Nanoparticles C-band Phase modulation Optical switches Vanadium Extinction ratio Silicon photonics Optical fiber communication Optical waveguides Photonics

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Passive collection of geolocation data by older people with cognitive impairment: Feasibility and user experiences for use in research

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Gerontechnology 2023年第1期22卷 1-8页

作者： Liddle, Jacki Ireland, David Krysinska, Karolina Lamont, Robyn Kang, Kristan Reppermund, Simone Sachdev, Perminder S. Gustafsson, Louise Brauer, Sandra Pachana, Nancy A. Brodaty, Henry School of Information Technology and Electrical Engineering The University of Queensland Brisbane QLD Australia School of Health and Rehabilitation Sciences The University of Queensland Brisbane QLD Australia Princess Alexandra Hospital Brisbane QLD Australia Australian e-Health Research Centre Commonwealth Scientific and Industrial Research Organisation Brisbane QLD Australia School of Public Health and Preventive Medicine Monash University Melbourne Australia Centre for Healthy Brain Ageing University of New South Wales Sydney NSW Australia School of Psychiatry University of New South Wales Sydney NSW Australia School of Allied Health Sciences Griffith University Brisbane QLD Australia School of Psychology University of Queensland Brisbane QLD Australia

Background: The community mobility and engagement of older people is an important aspect of health and quality of life. Population studies and clinical trials are important for understanding ageing-related experiences, needs, and impacts of interventions. Passive collection of data through sensors could help to build the understanding of community lives, potentially without adding to participant burden or requiring an accurate recall. There are assumptions that technology-based data collection may not be feasible or acceptable for older people with cognitive impairment. technology development and evaluation needs to engage with the direct perspectives of users with cognitive impairment to build an understanding of usability, acceptability, and ethical considerations. Objective: To explore the feasibility, user experiences, and processes needed to support the smartphone-based collection of older people with cognitive impairment. Method: An exploratory study, embedded within a longitudinal observational study, invited older adults to collect geolocation data for one week using smartphones and Bluetooth beacons. A process to support consent, transparency of data collection, and accessibility eredeveloped. Experiences and reflections on data collection were gathered via audio-recorded semi-structured interviews and field notes. Inductive content analysis was conducted. Results: Eighteen older adults (15 with mild cognitive impairment, 3 with probable dementia;age M=86.7 years;8 men) collected geolocation data. Most completed a week of data collection and considered it a positive experience. On average 161.5 hours (SD 31.7) of geolocation data were collected over a week. Reported user experiences included technical issues, consent and concerns, acceptability, and accessibility. Some support and reassurance were required, along with time to gain familiarity. Participants reportedly enjoyed engaging with their collected lifespace data. Conclusion: Older adults with cogniti

关键词： community gerontology mobility restriction smartphone usability testing

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Pathways to the use of concentrated solar heat for high temperature industrial processes

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Solar Compass 2023年 5卷

作者： ‘Gus’ Nathan, G.J. Lee, Leok Ingenhoven, Philip Tian, Zhao Sun, Zhiwei Chinnici, Alfonso Jafarian, Mehdi Ashman, Peter Potter, Daniel Saw, Woei Heavy Industry Low-carbon Transition Cooperative Research Centre Centre for Energy Technology The University of Adelaide Australia School of Electrical and Mechanical Engineering The University of Adelaide Australia School of Chemical Engineering The University of Adelaide Australia Commonwealth Scientific and Industrial Research Organisation

New analysis is presented identifying strong potential for Concentrating Solar Thermal technology (CST) to be a cost-effective contributor to future sources of net zero-emissions, high temperature industrial process heat relative to other emerging options. Nevertheless, significant further development of the technology is needed to realise this potential because the majority of previous investment in CST has targeted lower temperature applications in power generation, which employs different working fluids and typically operates at different scales. A comparison with the flame temperatures typically employed in current industrial processes, together with an allowance for thermal storage, suggests that receiver temperatures in the range of 1100 − 1500 °C will be needed to drive many current high-temperature industrial processes, which is far above the range of temperatures employed commercially and also above the range at which most pilot-scale work for CST has been undertaken to date. technology development will therefore be needed to realise this potential, both for the solar thermal plant and for the industrial processing plant, since current reactors have been developed to utilise fossil fuels. More work is also needed to advance understanding of the best options with which to hybridise CST with another back-up energy source, such as hydrogen, since it is uneconomical to seek to manage seasonal variability with thermal storage alone. A case study is then presented of the techno-economic performance of a system based on the solar expanding-vortex receiver, which has proven potential to operate at the required temperature range and also employs air as the Heat Transfer Media (HTM) to facilitate integration into existing industrial processes. This analysis summarises the first major assessment of a fully integrated system that considers the full path from the solar plant to the industrial processes with thermal storage and combustion back-up, using a transient model

关键词： Concentrated solar thermal energy Decarbonisation High temperature process heat Levelised cost of heat (LCOH)

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Generative AI-aided Reinforcement Learning for Computation Offloading and Privacy Protection in VR-based Multi-Access Edge Computing

Generative AI-aided Reinforcement Learning for Computation O...

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Smart World Congress (SWC), IEEE

作者： Feiran You Hongyang Du Jiawen Kang Wei Ni Dusit Niyato Abbas Jamalipour School of Electrical and Computer Engineering The University of Sydney Sydney NSW Australia Data61 Commonwealth Scientific and Industrial Research Organization (CSIRO) Sydney NSW Australia Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Automation Guangdong University of Technology China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The rapid growth of Artificial Intelligence-Generated Content (AIGC) services has led to increased mobile user participation in related computations and interactions. This development has enabled AI-generated characters to interact with Virtual Reality (VR) users in real time, making the VR experience more interactive and personalized. In this paper, we consider an MEC system where VR users engage in AIGC services, focusing on the Generative Diffusion Model (GDM)based image generation tasks. Specifically, VR users initiate requests for computing resources, while computation offloading distributes the processing load across the MEC system. To manage AIGC edge computation offloading and cloudlet-VR user connections jointly, a Data Center Operator (DCO) employs a centralized Proximal Policy Optimization (PPO) algorithm. To protect VR users’ privacy while preserving PPO functionality, we employ the Generative Diffusion Model (GDM), specifically the Denoising Diffusion Implicit Model (DDIM), which first introduces noise to the PPO state, then conducts a denoising process to recover the state information. We further employ Inverse Reinforcement Learning (IRL) to infer rewards for the recovered states, using expert demonstrations trained by the PPO. The similarity between PPO-generated rewards and IRL-inferred rewards is then computed. Simulation results demonstrate that our proposed approach successfully achieves computation offloading while protecting VR users’ privacy within the PPO centralized management framework.

关键词： Privacy Solid modeling Simulation Noise reduction Noise Virtual reality Reinforcement learning Diffusion models Protection Optimization

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Single Vanadium Dioxide Nanoparticle-Enabled Plasmonic switch with Thermal and Electronic Reconfigurability

Single Vanadium Dioxide Nanoparticle-Enabled Plasmonic switc...

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OptoElectronics and Communications Conference, OECC

作者： Gregory Tanyi Daniel Peace Han-Hao Cheng Guanghui Ren Xuan Hiep Dinh Mohammed Taha Arnan Mitchell Karsten Hoehn Christina Lim Ranjith Unnithan Department of Electrical and Electronic Engineering Faculty of Engineering and Information Technology The University of Melbourne Parkville VIC Australia Mineral Resources Commonwealth Scientific Industry and Research Organiasation Australian Research Council Centre of Excellence for Engineered Quantum Systems & School of Mathematics and Physics The University of Queensland St Lucia Queensland Australia Centre for Microscopy and Microanalysis The University of Queensland St Lucia Australia Integrated Photonics and Applications Centre School of Engineering RMIT University Melbourne Victoria Australia

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

We present an integrated switch that combines plasmonic and neuromorphic technologies with a single sub-stoichiometric VO2-x nanoparticle. The presented device acts as a versatile plasmonic switch with dual thermal and electrical reconfigurability leveraging the near-room temperature phase transition of the VO2-x nanoparticles combined with the rapid phase recovery to drive the device. The change in both the optical and electrical properties of the VO2-x nanoparticle enables simultaneous optical and electrical readouts making the plasmonic device suitable as a phase change memory cell which is crucial in the convergence of computing and communication technologies. Our demonstration of reversible electrical switching, evidenced by a 6dB modulation depth and concurrent optical and electrical outputs, signifies a major stride in merging electronic and photonic functionalities within phase-change material devices. This novel strategy not only harmonizes optical communication with electronic computing but also advances the development of sophisticated integrated neuromorphic devices.

关键词： Phase change materials Nanoparticles Neuromorphic engineering Optical switches Vanadium Plasmons Nanoscale devices Optical modulation Optical devices Switching circuits

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NaNO3 monolayer: A stable graphenelike supersalt with strong four-phonon scattering and low lattice thermal conductivity insensitive to temperature

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Physical Review Materials 2022年第6期6卷 064009-064009页

作者： Tingwei Li (李廷炜) Peng-Hu Du (杜鹏虎) Ling Bai (白羚) Qiang Sun (孙强) Puru Jena School of Materials Science and Engineering Peking University Beijing 100871 China Center for Applied Physics and Technology Peking University Beijing 100871 China Department of Physics Virginia Commonwealth University Richmond Virginia 23284 USA

Since the discovery of graphene, numerous efforts have been made to seek new two-dimensional (2D) graphenelike materials with intriguing properties. Here,we report a stable graphenelike supersalt NaNO3 monolayer composed of superhalogen NO3. The 2D NaNO3 monolayer is found to possess an ultralow isotropic Young's modulus (2.983 N/m) and a low thermal conductivity (1.65 W/mK @300 K), which is much lower than that of 2D NaCl monolayer (3.72 W/mK @300 K). We attribute this behavior to the strong anharmonicity induced by the weak bonding between Na and NO3 cluster and the large atomic displacements of O in the NO3 cluster. A giant four-phonon scattering leads to a 60% reduction in lattice thermal conductivity at 300 K compared to that of three-phonon scattering. Furthermore, temperature-induced phonon hardening causes the lattice thermal conductivity to be nearly temperature independent in the studied temperature range (100–350 K), displaying significant difference from the atom-based 2D materials.

关键词： Electrical properties First-principles calculations Mechanical & acoustical properties Phonons Thermal conductivity

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Reconfigurable Intelligent Surface-Assisted Localization in OFDM Systems with Carrier Frequency Offset and Phase Noise

arXiv

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arXiv 2023年

作者： Zhang, Hanfu Liu, Erwu Wang, Rui Ni, Wei Xing, Zhe Liu, Yan Jamalipour, Abbas College of Electronic and Information Engineering Tongji University Shanghai201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai201804 China Data61 Commonwealth Science and Industrial Research Organisation MarsfieldNSW2122 Australia School of Computer Science and Engineering University of New South Wales KensingtonNSW2052 Australia School of Information Science and Technology North China University of Technology Beijing100144 China School of Electrical and Computer Engineering University of Sydney SydneyNSW2006 Australia

Reconfigurable intelligent surface (RIS)-assisted communication systems have been extensively studied for providing high-precision location services. However, most studies have overlooked the impact of carrier frequency offset (CFO) and phase noise (PN) resulting from hardware impairments on localization. This paper presents a novel, alternating optimization (AO)-based algorithm to jointly estimate the CFO, PN, and user position, where, provided the user position, closed-form expressions for the CFO and PN are derived per iteration, significantly reducing the complexity and enhancing the stability of the algorithm. Another important aspect is a new RIS phase shift optimization algorithm developed to minimize the analytical lower bound of localization accuracy, hence benefiting localization. The semidefinite relaxation method and Schur complement are utilized to convexify this challenging non-convex optimization problem to a semidefinite program. Simulations demonstrate the effectiveness of the proposed algorithms, with the localization accuracy enhanced by two orders of magnitude. The localization accuracy of the proposed algorithm is close to the analytical lower bound, with a root mean square error of lower than 10−2 m. Copyright © 2023, The Authors. All rights reserved.

关键词： Mean square error

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Evidence-Aware Multi-Modal Data Fusion and its Application to Total Knee Replacement Prediction

Evidence-Aware Multi-Modal Data Fusion and its Application t...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Xinwen Liu Jing Wang S. Kevin Zhou Craig Engstrom Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia The Commonwealth Scientific and Industrial Research Organisation Canberra Australia Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) School of Biomedical Engineering & Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Deep neural networks have been widely studied to predict a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables, and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be in conflict, and the final prediction may fail in the presence of such a conflict. Therefore, it is important to account for the reliability of each source data and the prediction output when making a final decision. In this paper, we propose an evidence-aware multimodal data fusion framework based on the Dempster-Shafer theory (DST). The backbone models contain an image branch, a non-image branch and a fusion branch. For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch. The output probabilities along with the evidence scores from multiple branches are combined with the Dempster's combination rule to make a final prediction. Experimental results on the public OA initiative (OAI) dataset for the TKR prediction task show that the proposed method has better performance by accounting for conflicts from various modalities.

关键词： Medical conditions Soft sensors Digital images Data integration Imaging Artificial neural networks Reliability theory Feature extraction Reliability engineering Knee replacement

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