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Energy-efficient and Privacy-preserving Edge Intelligence for 6G-Empowered Intelligent Transportation systems

IEEE Network 2025年

作者： Zhu, Chenlu Fan, Xiaoxuan Deng, Xianjun Xiao, Ziheng Guo, Tingting Liu, Shenghao Sun, Jiaqi Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Wuhan Kedao Geographic Information Engineering Co. Wuhan China Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of

6G-empowered Intelligent Transportation systems (ITS) generate large amounts of data through millions of devices and sensors at the terminal and network edge. Edge intelligence advances the frontier of data-driven artificial intelligence (AI) to the network edge, which enables AI applications to better utilize edge data. Broader connectivity, higher device density, and stronger data exchange present challenges for reliable device communication and privacy data protection in 6G-empowered ITS. To address these challenges, this paper proposes an energy-efficient and privacy-preserving edge intelligence framework named EP-EI. EP-EI deploys a deep reinforcement learning-based cluster head selection model at the edge to ensure energy-efficient communication for terminal devices. Meanwhile, EP-EI designs a privacy-aware hierarchical computing framework for terminal-edge-cloud to prevent privacy leakage. Furthermore, the application scenarios, challenges, and future directions of EP-EI are discussed. Finally, simulation experiments in a specific intelligent transportation case study demonstrate that EP-EI can provide energy-efficient and privacy-protecting services. © 2025 IEEE.

关键词： Differential privacy

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Comparison Between Gramian Angular Fields (GAF) and Markov Transition Field (MTF) Images Data by Using them as Input to the Deep Learning Neural Network Solar Flare Production Platform

Comparison Between Gramian Angular Fields (GAF) and Markov T...

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2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Nagem, Tarek A M Hamad Alshareef, Sohil Mohamed, Abdelhafid Gihedan, Akram Albargathe, Salma Libyan British University Dept. Computer System Design Benghazi Libya University of Benghazi Dept. Software Engineering Benghazi Libya College of Computer Technology Dept. Software Engineering Benghazi Libya Darna University Dept. Computer Science Algoba Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

The prediction of solar storms from real-time satellites data is an essential to protect various aviation, power, and communication infrastructures. For this reason, current research interest is focusing on creating systems that can predict solar fare accurately. This paper combers Gramian Angular Fields (GAF) images and Markov Transition Field (MTF) images that have been generated by converting pre-flare Geostationary Operational Environmental Satellite (GOES) data from 2010 to 2016. After that, the deep learning neural network for the solar flare production platform accepts both MTF and GAF images as input to generate the predictions. Furthermore, this paper;investigated using MTF and GAF images spritely as input to the solar flare production platform. After that, the results of MTF and GAF images were compared using various prediction verification measures. © 2022 IEEE.

关键词： Forecasting

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Initial Study of Oil Palm Crop Condition Detection Through Drone Imagery: A YOLO Approach

Initial Study of Oil Palm Crop Condition Detection Through D...

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International Conference on Informatics, Multimedia, Cyber and Information system (ICIMCIS)

作者： Winata Nugraha Ermatita Deris Stiawan Faculty of Computer Science Doctoral Program Faculty of Engineering Universitas Sriwijaya Palembang Indonesia Department of Information System Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

Oil palm plantations in Indonesia still have many challenges, especially in terms of monitoring and mapping technology. One of the important aspects in the monitoring stage of oil palm plantations is monitoring the productivity of oil palm plants. To identify the oil palm plants, many previous studies have utilized image analysis to solve the problem. The problem is that poor image conditions can affect the architecture model to be built.[1] So in this research we do tune mapping on the image to produce a quality image. Each image data that has been improved will be feature extracted using ResNet and VGG to produce the best model. Furthermore, the classification model development process is carried out using CNN architecture as the model architecture. We also provide innovation by using object detection technology. Which uses artificial neural networks. Here we train the model using aerial video datasets of oil palm plants obtained from drones. Furthermore, to evaluate the performance of the above technique, it is proposed to use the mapping model, precision and accuracy evaluation obtained in the test phase[2]

关键词： YOLO Productivity Technological innovation Oils Multimedia systems Plantations Feature extraction Informatics Monitoring Drones

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Modified Kelvin-Voigt fractional derivative model for viscoelasticity measurement in optical coherence elastography

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Chinese Optics Letters 2025年第1期23卷 93-101页

作者： Chenming Yang Zhongliang Li Nan Nan Teng Liu Yaoli Luo Xiangzhao Wang Advanced Light Source and System R&D Center Department of Advanced Optical and Microelectronic EquipmentShanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China

Optical coherence elastography(OCE)can quantitatively obtain the viscoelasticity of tissues using rheological models and is widely applied to the clinical diagnosis of ***,commonly used rheological models in OCE do not account for the distinctive dependence of high-frequency storage and loss moduli on frequency in tissues,which results in the rheological models failing to accurately measure the viscoelastic properties of *** this paper,a modified Kelvin-Voigt fractional derivative model is presented based on the power-law behavior of soft tissues and the dependence of high-frequency complex shear modulus on frequency in living *** the rheometer and OCE tests,the modified model can provide the prediction of the power-law relationship between the low-frequency shear viscosity and frequency;compared with the Kelvin-Voigt and Kelvin-Voigt fractional derivative models,the modified model has a higher goodness-of-fit(accuracy>96%)for the high-frequency storage moduli of gelatin ***,the proposed model can reduce the root mean square error of fit by approximately 83%for the high-frequency(1–128 kHz)storage modulus of the polydimethylsiloxane phantoms obtained from publicly available ***,the modified model accurately predicts the mechanical properties of biomimetic materials over a wide frequency range,with the potential to more accurately reflect pathological changes in tissues.

关键词： optical coherence tomography optical coherence elastography viscoelasticity rheological model

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Performance Analysis of Higher Order Tensor Storages for Highly Sparse Multidimensional Data 5

Performance Analysis of Higher Order Tensor Storages for Hig...

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5th International Conference on Electrical Information and Communication Technology, EICT 2021

作者： Safayet Hossain, Md. Azharul Hasan, K.M. Tsuji, Tatsuo Department of Computer Science and Engineering Khulna Bangladesh University of Fukui Faculty of System Design Engineering Fukui Japan

ISBN: (纸本)9781665409063

To compute multidimensional data, tensor algebra is a powerful tool and it has many applications in machine learning and data analytics. This multidimensional data which is represented with higher-order tensors for multi-Aspect data analysis is very sparse for an actual dataset. The degree of data sparsity increases with the increase of dimensionality. This is because the volume of the tensor increases very fast with the increase of dimensionality but the actual data to the tensor does not increase at such a rate for high-performance scientific computing. Storing and applying operations on this highly sparse multidimensional data is a challenge for data scientists. Therefore, an efficient storage scheme for such sparse tensor is very important. There are many storage schemes in the literature for storing sparse tensors. In this paper, we analyze and compare different tensor storage formats using synthetic and actual datasets. We found that no single storage scheme is efficient for all the operations or applications of tensors. We calculated the memory required for per-nonzero elements among these tensor formats and compared them for different parameters. © 2021 IEEE.

关键词： Tensors

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Character Recognition Techniques using Machine Learning: A Comprehensive Study

Character Recognition Techniques using Machine Learning: A C...

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Applied Intelligence and Sustainable Computing (ICAISC), International Conference on

作者： Tarun Kumar Cherry Khosla Kartikey Vashistha Computer Science and Engineering Lovely Professional University Phagwara India Power System Engineer Independent Researcher

Optical Character Recognition (OCR) technology is essential in converting printed or handwritten documents into machine-readable text. This paper provides an overview of machine learning based OCR technology, including its history, principles, and applications. The paper discusses the challenges and limitations of machine learning based OCR and presents various OCR models based on different feature extraction and classification methods. Specifically, it covers feature extraction methods such as Euclidean distance, HOG, Fourier descriptor, and directional chain code, which are used to improve the accuracy of classification models such as CNN, KNN, ANN, and SVM. The paper also discusses recent advancements in OCR for different languages and how OCR can be integrated with machine learning and AI to understand ancient scripts and convert them to modern languages. The paper concludes with a discussion on the future of multi-lingual OCR and integration with AI and machine learning models, which have the potential to revolutionize the field of OCR. In summary, this paper provides a comprehensive overview of OCR technology, its advancements in various languages and potential for future development.

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Interoperability and Standardization in the Internet of Things

Interoperability and Standardization in the Internet of Thin...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： Ravi Kumar Saidala Haseeb V V G. Padmapriya Jayabalan S V. Kalai Priya Anwar Ahamed Shaikh Department of Computer Science Engineering Alliance University Bangalore Karnataka India Department of Computer Science NAM College Kallikkandy Kerala Tamil Nadu India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Business System Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering RMK college of Engineering and Technology Puduvoyal India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

This research highlights the importance of standards and interoperability in the IoT. An essential component of IoT ecosystems is interoperability, which refers to the ability for various systems and devices to converse and share data. Formats for data, methods of communication, and safeguards are all outlined by standards. Widespread adoption and scalability are made possible through interoperability and standards, which allow devices from many manufacturers to communicate with one another. This research investigates the alliances, regulatory authorities, and commercial associations that cooperate in the creation and advancement of open standards that can be embraced by other entities. The main objective of the research is to showcase the construction of an Internet of Things device capable of communicating with various embedded peripheral protocols utilizing Plug and Play (PnP) technology. Moreover, the research examines the impact of regulatory frameworks on the IoT ecosystem and their role in fostering interoperability. An IoT ecosystem necessitates a standardized and open environment to provide optimal performance in terms of connectivity, data exchange, and innovation. This research contributes to the progress of harmonizing and achieving interoperability among World Wide Web Things.

关键词： Technological innovation Protocols Scalability Ecosystems Internet of Things Web sites Interoperability

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Mathematical modeling of mixed convective MHD Falkner-Skan squeezed Sutterby multiphase flow with non-Fourier heat flux theory and porosity

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Applied Mathematics and Mechanics(English Edition) 2023年第11期44卷 2005-2018页

作者： Shuguang LI M.I.KHAN F.ALI S.S.ABDULLAEV S.SAADAOUI HABIBULLAH School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005Shandong ProvinceChina Department of Mechanics and Engineering Science Peking UniversityBeijing 100871China Department of Mechanical Engineering Lebanese American UniversityBeirut 11020Lebanon Department of Mathematical Sciences Federal Urdu University of ArtsSciences&TechnologyGulshan-e-IqbalKarachi 75300Pakistan Faculty of Chemical Engineering New Uzbekistan UniversityTashkent 100070Uzbekistan Department of Science and Innovation Tashkent State Pedagogical University named after NizamiTashkent 100070Uzbekistan Department of Physics Faculty of Science and ArtsMohayel AserKing Khalid UniversityAbha 61421Saudi Arabia State Key Laboratory of Turbulence and Complex System Collaborative Innovative Center for Advanced Aero-EnginesPeking UniversityBeijing 100871China

In a wide variety of mechanical and industrial applications,e.g.,space cooling,nuclear reactor cooling,medicinal utilizations(magnetic drug targeting),energy generation,and heat conduction in tissues,the heat transfer phenomenon is ***’s law of heat conduction has been used as the foundation for predicting the heat transfer behavior in a variety of real-world *** model’s production of a parabolic energy expression,which means that an initial disturbance would immediately affect the system under investigation,is one of its main ***,numerous researchers worked on such problem to resolve this *** last,this problem was resolved by Cattaneo by adding relaxation time for heat flux in Fourier’s law,which was defined as the time required to establish steady heat conduction once a temperature gradient is *** offered a material invariant version of Cattaneo’s model by taking into account the upper-connected derivative of the Oldroyd ***,both models are combinedly known as the Cattaneo-Christov(CC)*** this attempt,the mixed convective MHD Falkner-Skan Sutterby nanofluid flow is addressed towards a wedge surface in the presence of the variable external magnetic *** CC model is incorporated instead of Fourier’s law for the examination of heat transfer features in the energy expression.A two-phase nanofluid model is utilized for the implementation of *** nonlinear system of equations is tackled through the bvp4c technique in the MATLAB software *** influence of pertinent flow parameters is discussed and displayed through different *** and important results are summarized in the conclusion ***,in both cases of wall-through flow(i.e.,suction and injection effects),the porosity parameters increase the flow speed,and decrease the heat transport and the influence of drag forces.

关键词： sporosity Cattaneo-Christov(CC)heat flux model Falkner-Skan Sutterby nanofluid mixed convection stagnation point

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Cloud-Based IoT Solutions for Smart Grids: Advancing Smart Technologies in Energy Management 3

Cloud-Based IoT Solutions for Smart Grids: Advancing Smart T...

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3rd IEEE International Conference on Electrical Power and Energy systems, ICEPES 2024

作者： Antonius Alijoyo, Franciskus Vidhate, Deepak A. Kaur, Chamandeep Nivedan, V. Rani, V. Kanpur Balakumar, A. School of Business and Information Technology STMIK LIKMI Bandung Indonesia Dr. Vithalrao Vikhe Patil College of Engineering Department of Information Technology Maharashtra Ahmednagar India Jazan University Department of Computer Science Jazan Saudi Arabia Sri Ramakrishna College of Arts & Science Department of Electronics and Communication System Coimbatore India Hindustan Institute of Technology and Science Chennai India K.Ramakrishnan College of Engineering Department of ECE Trichy India

ISBN: (纸本)9798350390728

The incorporation of Internet of Things (IoT) cloud computing solutions with smart grids signifies a significant advancement in energy management. Traditional methods, though beneficial, often lack scalability and real-time capabilities, limiting their effectiveness in managing the complexity of modern energy systems. In this paper, a novel approach cloud-based IoT technologies to address these shortcomings and enhance smart grid functionalities. Existing energy management methods heavily rely on localized monitoring and control systems, constraining scalability and real-time responsiveness. Additionally, these methods struggle to handle the immense data generated by IoT devices dispersed throughout the grid. The proposed approach tackles these challenges by utilizing cloud computing to store, process, and analyze IoT sensor data in real-time. The novelty of this method lies in seamlessly integrating cloud-based platforms with IoT devices across the smart grid. Through utilising cloud resources, the limitations of traditional approaches are overcome, enabling scalable, real-time oversight and management of grid assets. The methodology involves deploying IoT sensors across the grid to compile information about energy usage, generation, and distribution, which is then transmitted to cloud servers for analysis and decision-making. Through simulations and real-world experiments, the effectiveness of this approach in optimizing grid performance, improving reliability, and facilitating demand-response programs is demonstrated. The results exhibit significant enhancements in grid efficiency and resilience, showcasing the potential of cloud-based IoT solutions to transform energy management in smart grids. Overall, this proposed method presents a promising pathway towards establishing more efficient, sustainable, and resilient energy infrastructure for the future. © 2024 IEEE.

关键词： Cloud platforms

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