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A Blockchain PoW Consensus Mechanism Based on Edge computing in the IoVs 8th

A Blockchain PoW Consensus Mechanism Based on Edge Computing...

引用

8th international Congress on Edge computing, EDGE 2024, Held as Part of the Services conference Federation, SCF 2024

作者： Xu, Liya Ge, Mingzhu Zhang, Caicai Shi, Jiaoli Dong, Xiwei Li, Hongbo Jiujiang University Jiangxi Jiujiang332005 China Taizhou Vocational College of Science and Technology Zhejiang Taizhou318020 China Zhejiang Polytechnic University of Mechanical and Electrical Engineering Zhejiang Hangzhou310000 China Daqing Normal University Heilongjiang Daqing163712 China

ISBN: (纸本)9783031770685

the explosive increase of vehicles and the emergence of novel businesses such as driverless driving have presented fresh challenges to the data security of the IoVs (Internet of Vehicles). To ensure data security, a trusted decentralized distributed storage approach for managing and storing information proves to be a feasible solution. the blockchain enables distributed nodes to jointly maintain a consistent and tamper-proof ledger, facilitating trusted storage and transmission of information among distributed nodes. However, the consensus mechanism of the blockchain demands that miners in the system possess strong computing power for mining computations. Unfortunately, the performance of nodes in the IoVs is limited and fails to meet these requirements, significantly restricting the application of the blockchain in the IoVs. this paper proposes a blockchain consensus mechanism based on edge computing, with roadside node RSU serving as the edge server to undertake the task of miners for mining, in order to address the issues of insufficient computing power and unstable topology of vehicle nodes. Simulation experiments indicate that the consensus mechanism proposed in this paper has lower total costs, including total energy consumption and delay, compared to the PoW consensus mechanism that uses vehicle nodes as miners. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Miners

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Questionable Fairness in Federated Learning 8

Questionable Fairness in Federated Learning

引用

8th international conference on Data Science and Machine Learning Applications, CDMA 2025

作者： Alharbi, Raed Nguyen, Truc Yousefzadeh, Nooshin Aljohani, Ahmed National Renewable Energy Laboratory Computatioual Science Center GoldenCO United States University of Florida Computer and Information Science and Engineering Department GainesvilleFL United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia University of North Texas Computer Science and Engineering Denton United States

ISBN: (纸本)9798331539696

Federated learning (FL) has emerged as a powerful framework for training deep learning models across numerous distributed clients, where a central server distributes and aggregates model updates without accessing clients' raw data, ensuring privacy. However, this decentralized approach introduces significant challenges regarding fairness. Existing research shows that non-i.i.d. data distributions among clients can lead to biased global models, particularly affecting decisions based on sensitive attributes. this paper aims to investigate these fairness issues rigorously by analyzing how disparities in client data distribution impact model fairness. We conduct experiments using various models and datasets to evaluate and mitigate biases. We also propose novel data partitioning strategies to reflect real-world client data distributions accurately. Our finding shows that FL is highly vulnerable to the fairness problem due to the Non-IID property. the experiment's code can be found here: https://***/raed19/Questionable-Fairness-in-Federated-Learning. © 2025 IEEE.

关键词： Federated learning

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Towards Smarter Maritime Security: Apriori Algorithm Optimization in Serverless Environments 16

Towards Smarter Maritime Security: Apriori Algorithm Optimiz...

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16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Shahin, Mahtab Rahimpour, Saeed Ghasempouri, Tara Bauk, Sanja Fahringer, thomas Draheim, Dirk Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Distributed and Parallel Systems Group University of Innsbruck Tirol Austria Information Systems Group Tallinn University of Technology Tallinn Estonia

this paper presents an optimized implementation of the Apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% reduction in execution time and a 22% decrease in memory consumption compared to traditional distributed Apriori methods. the study leverages high-dimensional fuel datasets, spanning from 2020 to 2050, to evaluate scalability and efficiency in processing energy-related data. By employing advanced synchronization and deferred partitioning strategies, communication overhead is significantly reduced, improving performance while effectively balancing computational loads across distributed nodes. Security measures, including AES-256 encryption and role-based access control (RBAC), are incorporated to safeguard data confidentiality and ensure compliance with regulatory frameworks. the proposed solution scales efficiently for datasets up to 1 million records, demonstrating applicability across domains such as transportation and logistics. Future work will explore adaptive partitioning techniques, hybrid cloud architectures, and AI-driven predictive analytics to further enhance scalability and operational efficiency in serverless multi-cloud systems. © 2025 Elsevier B.V.. All rights reserved.

关键词： Apriori Algorithm Big Data Cloud-Native Architectures Data Mining distributed computing Maritime Serverless computing

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Enhancing Electric Vehicle Charging Systems with distributed Edge Intelligence 16

Enhancing Electric Vehicle Charging Systems with Distributed...

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16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Rahmani, Rahim Stojcevska, Biljana Department of Computer and Systems Sciences Stockholm University Sweden American University of Europe Skopje North Macedonia

the rapid proliferation of electric vehicles (EVs) and the corresponding expansion of charging systems, driven by global regulatory mandates, present significant challenges to the electric grid. these systems also generate massive volumes of heterogeneous, distributed data, necessitating advanced edge-cloud computing solutions. Deploying distributed Edge Intelligence (DEI) offers a transformative approach by contextualizing raw data, minimizing dependency on centralized resources, enhancing task performance through experience-based learning, predicting outcomes under uncertainties, optimizing data routing, and enabling self-organization of networked devices. Traditional centralized machine learning approaches are often unsuitable due to high communication costs, low reliability, legal constraints, and scalability limitations. In this context, Federated Learning (FL) emerges as a promising solution, enabling distributed machine learning models to be trained locally on decentralized data while preserving privacy and reducing communication overhead. this paper provides a comprehensive analysis of the key challenges associated with EV charging systems, including scalability, security, and privacy concerns. It also explores the integration of blockchain technology with FL at distributed edges to enhance system efficiency, reliability, and privacy. By addressing these challenges, the study outlines a framework for sustainable EV adoption and efficient charging infrastructure deployment in the context of evolving technological and regulatory landscapes. © 2025 the Author(s).

关键词： Blockchain Charging Systems distributed Edge Intelligence Electric Vechicle smart contract

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Dynamic TSN Reconfiguration for Time-Triggered Organic computing 16

Dynamic TSN Reconfiguration for Time-Triggered Organic Compu...

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16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Qosja, Mario Raj, Utkarsh Meckel, Simon Obermaisser, Roman University of Siegen Adolf-Reichwein-Straße 2a Siegen 57076 Germany

In response to the increasing complexity of distributed embedded systems, self-organizing systems have been developed and studied extensively. One such system, Organic computing (OC), draws inspiration from biological organisms to enhance the adaptability and robustness. However, this approach poses challenges related to determinism and dependability, as Organic computing approaches lack deterministic communication mechanisms, making them unsuitable for safety-critical applications. To address these limitations, we introduce Time-Triggered Organic computing (TTOC), integrating time-triggered communication concepts to ensure predictable task execution and message exchange. In this architecture, task scheduling and message communication follows a predefined table schedule generated by a list scheduler. A key step toward real-world implementation of TTOC is simulating message communication using a time-triggered protocol such as Time-Sensitive networking (TSN). To achieve this, we leverage the INET framework of OMNeT++, which provides a discrete-event network simulation. Our approach allows for dynamic reconfiguration of TSN based on the communication schedules generated by TTOC. We evaluate the proposed method through a co-simulation of TTOC and TSN in OMNeT++, demonstrating its effectiveness in dynamic reconfiguration. this work contributes toward integrating self-organizing principles with time-deterministic networking, bridging the gap between adaptability and reliability in embedded systems. © 2025 Elsevier B.V.. All rights reserved.

关键词： Organic computing Time-Sensitive networking Time-Triggered TSN

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Enhancing Explainability of Artificial Intelligence for threat Detection in SDN-based Multicast Systems 16

Enhancing Explainability of Artificial Intelligence for Thre...

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16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Prasad, Preety Tahir, Mohammad Isoaho, Jouni Department of Computing University of Turku Turku 20014 Finland

the increasing adoption of Artificial Intelligence (AI) based Software-Defined networking (SDN) in multicast systems has improved network management and traffic efficiency. However, for network administrators, understanding AI outcomes and explanations for how conclusions are reached in threat detection and mitigation is essential for strengthening their overall security framework. Additionally, centralized control planes in SDN introduce new security challenges, which can complicate the detection and mitigation of various network threats. In this regard, this paper presents a novel framework that integrates Explainable AI (XAI) with SDN to detect and mitigate threats in real-time. the proposed framework leverages a hybrid machine learning model, using Convolutional Neural Networks (CNN) for analyzing network traffic features and Long Short-Term Memory (LSTM) networks for identifying patterns and anomalies. To enhance transparency and explanation for the threat detection process, the framework incorporates both LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations). LIME provides local explanations by generating perturbed data instances and training a surrogate model to identify the most influential features in a specific prediction. this allows the network administrators to understand how different network features contribute to the classification decision. SHAP, on the other hand, quantifies the contribution of each feature to the overall model decision by computing Shapley values, offering a global perspective of feature importance. this approach offers a more effective and transparent solution for SDN systems in a multicast environment, improving threat detection and security. © 2025 Elsevier B.V.. All rights reserved.

关键词： Convolutional Neural Networks (CNN) Explainable AI (XAI) Intrusion Detection Systems LIME (Local Interpretable Model-Agnostic Explanations) Long Short-Term Memory (LSTM) Multicast Traffic Network Security SHAP (SHapley Additive exPlanations) Software-Defined networking threat Detection

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distributed Applications and Interoperable Systems: 13th IFIP WG 6.1 international conference, DAIS 2013, Held as Part of the 8th international Federated conference on distributed computing Techniques, DisCoTec 2013, Florence, Italy, June 3-5, 2013. Proceedings 1

引用

丛书名： Lecture Notes in Computer Science 7891

2013年

作者： Matthew Brook, Craig Sharp, Graham Morgan (auth.), Jim Dowling, François Taïani (eds.)

ISBN: (数字)9783642385414

ISBN: (纸本)9783642385407;9783642385414

this book constitutes the refereed proceedings of the 13th IFIP WG 6.1 international conference on distributed Applications and Interoperable Systems, DAIS 2013, held in Florence, Italy, in June 2013, as part of the 8th international Federated conference on distributed computing Techniques, DisCoTec 2013. the 12 revised full papers and 9 short papers presented were carefully reviewed and selected from 42 submissions. the papers present state-of-the-art research results and case studies in the area of distributed applications and interoperable systems focussing on cloud computing, replicated storage, and peer-to-peer computing.

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Research on the Application of "Cloud + Edge"Meteorological Blockchain distributed Storage and Network Technology

Research on the Application of "Cloud + Edge"Meteorological ...

引用

IEEE Information Technology and Mechatronics Engineering conference (ITOEC)

作者： Changliang Zhang Cong Guo Yinhai Xie Sichuan Meteorological Observation and Data Centre Chengdu China Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province Chengdu China National Meteorological Information Center Beijing China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

this paper studies a blockchain-based solution for the distributed storage and networking of meteorological data, aiming to address issues in traditional meteorological data storage and transmission, such as single points of failure, poor scalability, inadequate security, and difficulties in data sharing. First, by analyzing the characteristics of meteorological data and the limitations of existing technologies, the feasibility and advantages of applying blockchain and distributed storage technology in the field of meteorological data are explored. the author designs an overall architecture consisting of a meteorological blockchain data collection layer, blockchain network layer, distributed storage layer, and application service layer. the functions and implementation methods of core components, such as the data collection module, blockchain module, distributed storage module, and data access module, are elaborated in detail. A blockchain-based distributed storage and networking system for meteorological data is constructed. through experiments and result analysis, the proposed solution is shown to improve data processing efficiency by over 30%, while ensuring data security and access speed, greatly enhancing the meteorological digital infrastructure's support capability for forecasting and disaster prevention and mitigation services.

关键词： Sharding Prevention and mitigation Data security Scalability Disasters Memory Blockchains Peer-to-peer computing Maintenance Data communication

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Proceedings - 2012 8th international conference on computing and networking Technology (INC, ICCIS and ICMIC), ICCNT 2012

Proceedings - 2012 8th International Conference on Computing...

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2012 8th international conference on computing and networking Technology (INC, ICCIS and ICMIC), ICCNT 2012

ISBN: (纸本)9788994364179

the proceedings contain 87 papers. the topics discussed include: performance research and analysis of ForCES TML based DP-WRR algorithm;solving sudoku in reconfigurable hardware;ideal and perfect multi-secret sharing schemes whose access structure are Q3;applications of cloud computing in education;new buffer management schemes for P2P streaming systems;a dynamic cryptographic access control scheme in cloud storage service;mobile cloud computing: the impact of users' motivation on actual usage;exploiting the concept of traffic shaping to improve distributed systems under node dynamics;graph-theory model based e-commerce website design and realize;a framework for resource recommendations for learners using social bookmarking;smartphone cloud storage anti virus detection and prevention using robust algorithm;and toward extracting malware features for classification using static and dynamic analysis.

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602315

the proceedings contain 158 papers. the special focus in this conference is on Life System Modeling and Simulation and Intelligent computing for Sustainable Energy and Environment. the topics include: A Method for Evaluating the Operating Health of Wind Turbines Under Wake Effect;toward Next-Generation Buildings: A Review of Renewable Energy Integration and Intelligent Building Energy Management Technologies;improving the Construction of a Multi-modal Underwater Image Recognition System for Anomaly Detection in Nuclear Power Plant Cooling Source Areas;A Novel Anti-noise Fault Diagnosis Method for Nuclear Energy Steam Turbine Based on VCformer;Short-Term Photovoltaic Power Forecasting Using Multi-timescale Information Based FFTformer Model;wind Power Forecasting Model Based on Feature Processing and Deep Hybrid Kernel Extreme Learning Machine;optimal User-Side Energy Arbitrage Strategy in Electricity Market with Accurate Battery Model Using Benders Decomposition;a Novel distributed Economic Dispatch Method for Smart Grid with Time-Varying Feedback Gain;benefits, Barriers, and Human Factors of V2X Implementation – A Brief Review;a General Pattern of Permanent Magnet Arrangement for Design Optimization of a Reluctance Magnetic Gear with Enhanced Torque Density for Electric Vehicles;wind Power Scheduling Deviation Compensation Based on Dual-Frequency Pulse Control;a Comprehensive Simulation and Validation of Single Particle Aging Mechanism Model for Lithium-Ion Batteries;semi-supervised State of Health Estimation for Lithium-Ion Batteries via Dynamical Self-encoding;a Reduce-Order Model to Understand the Multiscale Coupling of Battery Electrochemical-thermal Performance;RUL Prediction for Lithium-Ion Batteries Based on Sequence Decomposition and Transformer Networks;An Estimation Method of State of Health of Sodium-Ion Batteries Based on TCN.

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