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A Privacy-Preserving Timing Attacks Mitigation in Information-Centric Edge Networks 25

A Privacy-Preserving Timing Attacks Mitigation in Informatio...

引用

25th ieee international conferences on High Performance computing and Communications, 9th international conference on data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Liu, Ningchun Gao, Shuai Liang, Teng Hou, Xindi Yu, Lei Zhang, Hongke Research Institute of Intelligent Networks Zhejiang Lab Hangzhou311121 China School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Peng Cheng Laboratory Department of New Networks Shenzhen518066 China China Telecom Zhejiang CO. Ltd Intelligent Cloud Network Operation Center Hangzhou310009 China

ISBN: (纸本)9798350330014

In-network caching is one of the most important features of Information-Centric Networking (ICN), which reduces data-retrieval latency, especially at edge networks. However, in-network caching can lead to privacy leakage with timing attacks, i.e., an adversary retrieves cached data and identifies it based on the difference of round trip time (rtt) of cached and non-cached data retrieval. Existing mitigation mechanisms either abandon in-network caching or intentionally add latency to cached data retrieval but fails to prove their effectiveness. In this paper, we propose a timing attack mitigation mechanism based on differen-tial privacy exponential mechanism that strictly protects content privacy against timing attacks without significantly increasing the content retrieval delay. Theoretical analysis and evaluation results show that our mechanism protects content privacy effectively at a low cost. © 2023 ieee.

关键词： Timing circuits

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Classification of Information Objects with Fuzzy Parameters in E-Learning systems

Classification of Information Objects with Fuzzy Parameters ...

引用

ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Vadym Mukhin Valerii Zavgorodnii Viacheslav Liskin Sergiy Syrota Vasyl Koval Liudmyla Honchar Department of System Design National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kiev Ukraine Department of Information Systems and Technologies National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kiev Ukraine Department of Applied Mathematics National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kiev Ukraine Department of Information-Computing Systems and Control West Ukrainian National University Ternopil Ukraine Department of Computer Science West Ukrainian National University Ternopil Ukraine

The paper describes the process of the information objects classification in e-learning systems using the mathematical background of fuzzy logic. This approach can be used to improve the process of searching and organizing information in e-learning systems. As the volume of educational materials grows, it becomes more and more difficult to find and structure information. Fuzzy logic helps to optimize the search, and the use of a neural network can correctly classify materials in e-learning systems, which increases the efficiency and accuracy of search queries.

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intelligent Crossroads Testbed: Toward Autonomous Intersection Management systems 10

Intelligent Crossroads Testbed: Toward Autonomous Intersecti...

引用

10th international conference on Control, Decision and Information Technologies (CoDIT)

作者： Chamideh, SeyedeZahra Sanner, Oscar Tarneberg, William Kihl, Maria Lund Univ Dept Elect & Informat Technol Lund Sweden Lund Univ Dept Elect Informat Technol Lund Sweden

ISBN: (纸本)9798350373981;9798350373974

In the rapidly advancing domain of autonomous vehicle technology, the management of intersection traffic emerges as a demanding challenge. This paper describes the development and implementation of a novel testbed designed for the investigation of an autonomous intersection management system (AIMS). The research primarily focuses on the complex coordination required among autonomous vehicles for efficient operation at urban intersections. At the heart of this system lies the Intersection Coordination Unit (ICU), which centralizes traffic management. Robotic vehicles, equipped with On-Board Units (OBU) and advanced sensors, communicate wirelessly with the ICU. This communication facilitates real-time updates on vehicle positions and movements, enhancing environmental perception and aiding in collision prevention. The paper includes a validation section, demonstrating the testbed's capabilities and the quality of data it generates.

关键词： Autonomous vehicles

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Robust Sensor Fusion Algorithms for IoT

Robust Sensor Fusion Algorithms for IoT

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2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Subramanian, Sakthitharan Lokeshwari, Gandrakoti Jagadeesan, S. Leela, S. Pratheep, V.G. Venkatasudhahar, Murugesan School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai603203 India Geethanjali College of Engineering and Technology Department of Cybersecurity Telangana Hyderabad501301 India Department of Computer Science and Engineering Tamil Nadu Erode638052 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Kongu Engineering College Department of Mechatronics Engineering Tamil Nadu Perundurai638060 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mechanical Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798331543617

Sensor fusion is a critical component of the IoT ecosystem, as it combines a variety of data streams from multiple sensors to enhance the accuracy, reliability, and real-time decision-making. This research concentrates on the development of robust sensor fusion algorithms that are capable of effectively managing the heterogeneity, noise, and uncertainty that are inherent in data from Internet of Things (IoT) sensors. Our primary focus is on the ability of these algorithmic methodologies to manage data from multi-modal sensors. We investigate algorithms that are based on deep learning, Bayesian inference, and Kalman filtering. The proposed solutions aim to improve the performance of systems in sectors that are influenced by the Internet of Things (IoT), including healthcare, smart cities, industrial automation, and autonomous cars. This investigation investigates the scalability, energy efficiency, and computational burden of the Internet of Things (IoT) in the context of limited resources. Through simulations and practical applications, we demonstrate the enhancement of data precision, defect tolerance, and overall system dependability by these robust sensor fusion techniques. © 2024 ieee.

关键词： Kalman filters

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A Novel intelligent Recommendation Algorithm based on Multidimensional data Analysis of User Portrait 3

A Novel Intelligent Recommendation Algorithm based on Multid...

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3rd international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2023

作者： Xiong, Min ChangJiang Polytechnic Hubei430000 China

ISBN: (纸本)9798350306989

The collaborative filtering recommendation model needs to adapt to low-quality sparse data, while taking into account high-quality recommendation effects, and fuzzy sets and user portraits based on neural network calculations can solve this problem very well, raising the recommendation quality to a new level. In the designed model, novel intelligent recommendation algorithm based on the multidimensional data analysis of user portrait is studied. In the designed model, the multidimensional data analysis paradigm is first discussed, then, the user portrait analysis model is discussed. The user behavior labels should be properly recombined according to the needs of enterprise management, and the virtual behavior characteristics of ordinary users and leading users should be sorted out. With this additional model, the intelligent recommendation algorithm is implemented with the gradient descent model. In the experiment section, the MovieLens-100K and the MovieLens-1M data sets are used to test the performance, and the accuracy is considered as the standard test. The comparison results show that the proposed algorithm has the better recommendation performance. © 2023 ieee.

关键词： Collaborative filtering

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Extending the Multiphysics Modelling of Electric Machines in a Digital Twin Concept 11

Extending the Multiphysics Modelling of Electric Machines in...

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11th ieee international conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Mendes, Gabriel Ferreira, Angela Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal

ISBN: (纸本)9781665426053

The digital twin is a trending technology that has been applied to different fields. In this work, the use of the digital twin of electric machines in a life cycle perspective is investigated. It presents a literature review of the main concepts and applications of the digital twin in the field. In addition, it discusses the methodological approach to obtain the digital twin of a static electric machine by using its multiphysics model in a reduced order model, to improve the maintenance scheme and estimate the lifetime of its insulation system based on the machine's temperature profile.

关键词： digital twin multiphysics FEM remaining lifetime electric machines

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intelligent Battery Management System for Electric Vehicle with IoT-based Monitoring, AI-Driven Multiple Fault Diagnosis and Energy Optimization 3

Intelligent Battery Management System for Electric Vehicle w...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Arun Kumar, P. Shalini, S. Sneha, R. Vasunthra, P. Suganyadevi, S. M Kumarasamy College of Engineering Department of Ece Karur India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Battery related issues have a significant effect on both the performance and safety of electric vehicle (EV). Overheating Battery Lifespan is meant to be reduced by simply the dangers of overheating, as they can even cause thermal runaway, as well as from over and deep revolution. Besides, EV batteries have poor performance even in extreme temperatures, capacity loss, and internal defects. In order to cope with these problems, a proposed IoT based EV battery monitoring and fault detection system provides the real time analysis to promptly notify the battery health. Built in IoT sensors are integrated in this system which monitors critical parameters constantly i.e. temperature, voltage, current and charge levels. For 95-98% battery parameters monitoring accuracy, the collected data is transferred to a centralized cloud platform for real time cloud processing. The system utilizes advanced edge computing techniques and continuous monitoring capable of detecting a fault with 95-98% probability. Electrochemical processes are analyzed with 90-95% accuracy using predictive analytics and machine learning algorithms to ensure optimal battery performance. Moreover, the system achieves energy efficiency of more than 95% through power management and control mechanisms. In 95% of the cases, intelligent charging algorithms will prevent overcharging. This cutting-edge technology dramatically improves EV battery safety, longevity as well as efficiency, assuring reliability and sustainability. © 2025 ieee.

关键词： Predictive analytics

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Assessing Wildlife Tourism's Role in Conservation Awareness Using Artificial Intelligence and data Science for Sustainable Tourism Development

Assessing Wildlife Tourism's Role in Conservation Awareness ...

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2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Sathe, Suvarna Randhave, Mahesh Sadhale, Manasi Joshi, Aditi Khare, Amit Upadhye, Nilesh Tilak Maharashtra Vidyapeeth Pune India

ISBN: (纸本)9798331543617

The purpose of this exploration paper is to improve the development of sustainable tourism by examining the intersection of wildlife tourism and conservation consciousness through the perspective of artificial intelligence (AI) and data wisdom. Through the use of data analysis and tools driven by artificial intelligence, the project analyzes how wildlife tourism practices might be optimized to strengthen conservation efforts. Through the utilization of artificial intelligence methodology for caller shadowing, wildlife monitoring, and predictive analytics, the investigation investigates how these technologies can serve to mitigate adverse effects on wildlife while simultaneously fostering beneficial conservation concerns. Additionally, it investigates how tailored gestures and interactive instructional aids, powered by artificial intelligence, can help excursionists become more conscious. In addition to this, the paper discusses the difficulties that are related to ethical considerations, the sequestration of data, and the incorporation of unique information. This paper gives insight into how artificial intelligence (AI) and data wisdom can be effectively applied to support sustainable wildlife tourism and advance conservation pretensions. This insight is provided through case studies and tactics that are driven by data (data-driven). The purpose of the findings is to use them to inform policy and functional practices to further promote tourism that is both responsible and meaningful. © 2024 ieee.

关键词： artificial intelligence conservation awareness data science lmpact of tourism on sustainable development wildlife

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Comprehensive Image Analytics for Diversion of Forest Land 4

Comprehensive Image Analytics for Diversion of Forest Land

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4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Kambare, Shweta Rudrabhate, Aabha Divate, Srinath Taktode, Tapasvi Tekam, Anurag Thakare, Yatharth Vishwakarma Institute Of Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798331529635

This research study presents an initiative that seeks to revolutionize the process of tree enumeration and categorization, traditionally carried out manually. It harnesses the power of image analytics, the system aims to streamline the process, reduces manpower requirements, and lowers costs, all while minimizing the environmental impact of development projects. By incorporating techniques such as Binary Image Thresholding and Convolutional Neural Networks, the system categorizes and monitors trees using high-resolution satellite imagery. Analytical tools, including YOLOv8 for object detection and NDVI analysis for vegetation classification, are deployed to optimize land allocation and pathfinding. The proposed system's design is centered around efficiency and environmental consciousness. An interactive user interface is developed ensuring accessibility and ease of use. The integration of Google Maps API enables real-time monitoring of forest changes, supporting effective decision-making for deforestation and reforestation efforts. © 2024 ieee.

关键词： Convolutional neural networks

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Simulator of Microcontroller's Power Consumption

Simulator of Microcontroller's Power Consumption

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ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Orest Kochan Oleksandr Osolinskyi Anatoliy Sachenko Volodymyr Kochan Ihor Romanets Lviv Polytechnic National University Lviv Ukraine Department for Information Computer Systems and Control West Ukrainian National University Ternopil Ukraine

This paper proposed a simulator of power consumption for pulse consumers (in particular, microcontrollers, henceforth MC). It simulates the character of their power consumption. Due to the function of self-correction of error of circuit components, the simulator provides high accuracy. At the same time, the simulator provides a simulation of the MC operation at frequencies that considerably exceed the frequency of the MC clock generator. This eliminates frequency errors when calibrating the measuring channels of devices and systems designed to measure the MC's power consumption. Due to the calibration, these measurement channels can provide traceability of measurements when studying the MC's power consumption when executing instructions, commands, programs, and their fragments. The simulator can be a reference for creating the system of metrological service of the specified above measurement channels.

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