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20th international conference on intelligent computing (ICIC)

作者： Li, Chenting Li, Dan Zhou, Yuqian Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9789819756056;9789819756063

The demand for blockchain-based data storage and interaction is increasing substantially. Cross-chain technology has alleviated the isolation between different chains in currency transactions. However, in fields like the Internet of Things, cross-chain data sharing security has gained significant attention. The main challenge in data sharing is ensuring data integrity, consistency, and privacy. Therefore, auditing data integrity across chains is crucial. Current research primarily focuses on the cross-chain interaction of individual data, neglecting data source auditing and resulting in an incomplete shared data auditing scheme. Additionally, on-chain blockchain transactions have limited storage space and transaction processing speed. Identifying duplicate data and rejecting duplicate transaction uploads is essential in this context. Furthermore, in most blockchain applications, efficient handling of dynamic data is crucial due to the frequent updates in the dataset. Consequently, to check the correctness of data sources, ensure the integrity and consistency of cross-chain data, and achieve data dynamism and privacy, we design a dual-audit scheme for cross-chain shared data. The designed scheme utilizes the core idea of governing the chain by chain for decentralized audit, the message-locked encryption for data privacy and duplicate data identification, an improved Merkle tree for efficient batch addition of dynamic data, smart contracts for dual-audit algorithm, identity-based public key cryptography and the homomorphic tag algorithm for reducing on-chain computation overhead. Security analysis proves the scheme to be secure in the threat model. Experimental results demonstrate that our dual-audit scheme achieves high efficiency in terms of time overhead and communication costs.

关键词： Dual auditing data integrity verification Cross-chain Privacy protection Dynamic update

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Advancing Sustainability Impact Assessment: A Comprehensive Tool for Low Emissions Zone Management 8

Advancing Sustainability Impact Assessment: A Comprehensive ...

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8th international conference on smart and Sustainable Technologies, SpliTech 2023

作者： Fernández, Eduardo Illueca Kühne, Noel Gomariz Mulero, Nuria Bernabé Jara, Antonio J. University of Murcia Department of Informatics and Systems Spain Libelium Murcia Research and Development Department Spain

ISBN: (纸本)9789532901283

Low emissions zones (LEZs) have emerged as a key strategy in addressing urban air pollution and reducing greenhouse gas emissions. To effectively manage and enforce LEZs, optimization of mobility, intelligent transport systems, multi-modal mobility, and mobility as a service must be considered as objectives to be reached. In the same direction, public authorities have included in their strategic plans to make their city "more sustainable and smarter. In this sense, a comprehensive toot that integrates advanced technologies and data-driven approaches is essential. To overcome this limitation, Libelium proposes in this work a LEZ management software for smart decission making. It integrates various components such as real-time monitoring, data analytics, air quality modeling, and KPIs computation. Our solutions utilize a combination of sensors, cameras, and communication networks to collect real-time data on vehicle emissions, traffic patterns, and compliance levels within the designated low emissions zones. This paper shows promising results in this context validated in five European cities - Paris, Helsinki, Amsterdam, Stavanger, and Tallinn - obtaining a CO2eq emissions reduction up to 86 % in the best scenarios. In addition, the dispersion models can achieve a high resolution at a street level. For this reason, the tool for low emissions zone management presents a comprehensive and integrated approach to monitor, enforce, and evaluate LEZs effectively. The tool enables authorities to effectively combat urban air pollution and reduce emissions by leveraging real-time monitoring, data analytics, and sustainability impact assessment. Implementing such a tool has the potential to create cleaner and healthier urban environments, enhance transportation sustainability, and improve the overall quality of life for citizens. © 2023 University of Split, FESB.

关键词： data analytics

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Step-Wise Working Status for Next-Generation smart Cities: Integrating Fog and Edge computing Solutions

Step-Wise Working Status for Next-Generation Smart Cities: I...

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2025 international conference on Visual analytics and data Visualization, ICVADV 2025

作者： Beevi, L Sherin Nehru, V. Joe Prathap, P.M. Dani, W. Vinil R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Avadi Chennai India St. Peter's College of Engineering and Technology Department of Eee Avadi India

ISBN: (纸本)9798331521394

In the fast changing environment of smart cities, combining fog and edge computing with powerful IoT devices like Hikvision cameras is revolutionising urban administration and public safety. Hikvision, a video surveillance systems pioneer, offers a variety of IP and analogue cameras, thermal imaging, and intelligent software platforms used for traffic management, public safety, environmental monitoring, and infrastructure security. These technologies, which combine high-definition video quality, advanced image processing, and cognitive analytics, enable real-time monitoring, event detection, and quick responses. Traffic cameras collect real-time data for vehicle counts and congestion management, while environmental sensors monitor air, water, and noise levels to aid in health warnings and pollution control. smart meters and wearable health gadgets monitor energy, water, and health indicators to optimise resource management. Parking sensors and waste management sensors boost parking and rubbish collection efficiency. The integration of fog and edge computing processes data locally, lowering latency and bandwidth utilisation, while fog nodes aggregate data for thorough analysis. Cloud storage enables long-term data analytics, which improves decision-making for city planners and emergency responders. Hikvision's software, which includes HikCentral and iVMS-4200, combines video surveillance, access control, and intelligent traffic management to create a comprehensive urban security framework. These systems are crucial to public safety because they provide real-time monitoring, facial recognition, and motion detection. For city planners, such innovations provide essential insights for urban growth and sustainability programs. The integration of Hikvision video surveillance with fog and edge computing is a game-changing approach to smart city management, improving urban safety and efficiency while improving inhabitants' quality of life. © 2025 IEEE.

关键词： Water pollution control

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A Semantic Analysis-Based Method for smart Contract Vulnerability 8

A Semantic Analysis-Based Method for Smart Contract Vulnerab...

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8th IEEE international conference on Big data Security on Cloud, 8th IEEE international conference on High Performance and smart computing, and 8th IEEE international conference on intelligent data and Security, BigdataSecurity/HPSC/IDS 2022

作者： Yan, Xingrun Wang, Shuo Gai, Keke Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China

ISBN: (纸本)9781665480697

smart contracts are distributed, self-executing programs on blockchain network and they have the potential to impact many industries. A large number of developers are optimistic about the development prospects of smart contracts. However, due to different levels of familiarity of developers with technology, the existence of serious vulnerabilities in code can result in huge property losses. The serious consequences of reentrancy vulnerabilities have attracted widespread attention from researchers, and researchers have proposed many approaches to detect them. Many experiments show that these approaches can only effectively detect certain types of vulnerabilities. Similar vulnerabilities still exist in some smart contracts. In this paper, we propose a semantic analysis-based detection method for reentrancy vulnerabilities. The method utilizes machine learning techniques to efficiently detect the vulnerabilities of the given code. And then, our method provides semantic analysis-based effective correction feedback of the code. Compared with the existing vulnerability detection tools Slither and Mythril, our method improves the work efficiency. Moreover, compared with the current Mythril method with the best vulnerability detection accuracy, our method improves the accuracy by 1.01%. © 2022 IEEE.

关键词： smart contract

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Sustainability in Development of Grant Applications 2nd

Sustainability in Development of Grant Applications

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2nd international conference on Ubiquitous computing and intelligent Information Systems (ICUIS)

作者： Encheva, Sylvia Western Norway Univ Appl Sci Inndalsveien 28Post Box 7030 N-5020 Bergen Norway

ISBN: (纸本)9789811925412;9789811925405

The aim of this work is to present a novel method for transitioning from competition to collaboration phase in the process of preparing applications for delivering an external support for research projects. Concept similarity is suggested to be employed to facilitate a search for semantic similarities among differently formulated grant applications and thereafter offer possibilities for incorporating a collaborative work to interested parties. During the entire process of similarity search and eventual suggestions for collaboration among competing teams, there will be no disclosure of proposals' contexts or ideas they are built upon. The presented approach may very and it will be used in other types of project applications cases, where both public and private organizations are involved.

关键词： Collaboration data analytics External grants

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Accelerating the Deployment of observant Energy Efficient Lighting System for smart Cities

Accelerating the Deployment of observant Energy Efficient Li...

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2022 international conference on data analytics for Business and Industry, ICDABI 2022

作者： Mishra, Vinay Kumar Singh, Mayank Tiwari, Raj Gaurang Kumar, Ajay Teerthanker Mahaveer University College of Computing Sciences and Information Technology Moradabad India Teerthanker Mahaveer University Uttar Pradesh Moradabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665490580

It is no secret that street lighting costs a city a lot of money in terms of energy. An intelligent street lighting system can reduce municipal street lighting expenses by 50 percent to 70 percent. As the name implies, intelligent street lighting systems automatically modify their light output based on usage and occupancy, such as pedestrians, cyclists, cars and trucks being the primary users of the road. In order to reduce energy consumption, an intelligent street light management recommends installing a wireless-based system that can remotely track and control the real energy usage of the street lights. Using an IR transmitter and IR receiver combination, we present a technique for energy conservation. When the sensor detects movement, it sends the data to the microcontroller, which then turns the Light ON/OFF as needed. To reduce energy wasting and to save lives from accidents, this research's major goal is to prevent energy wastage. © 2022 IEEE.

关键词： Street lighting

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smart Children Management Using data analytics, Machine Learning and IoT 1st

Smart Children Management Using Data Analytics, Machine Lear...

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1st international conference on Artificial Intelligence for smart Community, AISC 2020

作者： Faruk, Md Jobair Hossain Adnan, Muhamad Hariz Muhamad College of Computing and Software Engineering Kennesaw State University Marietta United States Computing Department Faculty of Art Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia

ISBN: (纸本)9789811621826

One of the biggest challenges faced by parents is managing the difficult or defiant behavior of children of the twenty-first century. Technology can help to provide support in managing children’s activities and helping parents to be in control. Recent advances in IoT have sparked the interest of researchers to develop a smart management system for children. This paper presents a review of the smart management of children using the internet of things (IoT), machine learning, and data analytics. The objective is to identify potential approaches regarding smart management for children based on IoT for future researchers. The reviews were conducted from secondary sources, where conference proceedings and journals have been analyzed from Scopus and other databases. The finding indicates that IoT-based smart system has robust accuracy and performance. Thus, exploiting IoT for the development of a smart management system for children will provide significant benefits to the future generation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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IoT System Architecture for a smart City

IoT System Architecture for a Smart City

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Information, Control, and Communication Technologies (ICCT), international conference on

作者： Priyanka Nahar Avdhesh Ghuraiya Ilia Mikhailovich Voronkov Alexander Alexandrovich Kharlamov Department of Intelligent Information System and Technologies Moscow Institute of Physics and Technology Dolgoprudny Russia

ISBN: (数字)9798331517564

ISBN: (纸本)9798331517571

Industry 5.0 has transformed manufacturing with smart factories as a key component. This report reviews the latest research on smart factories and industrial data management. Focusing on automation, AI, machine learning, big data and virtualization Technologies such as IoT and crowd sensing enable continuous data collection for real-time decision making and human-machine collaboration. We present a smart factory architecture that combines edge computing for local processing and cloud analytics with various communication technologies. To increase safety, efficiency, and connectivity in modern factories

关键词： Cloud computing Machine learning Computer architecture Real-time systems Production facilities Communications technology Fifth Industrial Revolution Virtualization smart manufacturing Edge computing

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Prediction of Diabetes at early stages using ANN & Outlier Exposure

Prediction of Diabetes at early stages using ANN & Outlier E...

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2022 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2022

作者： Jagini, Ajay Gayatri, Nandam Nagaraju, Sangepu Rao, Vardhineni Saketh Rao, Kanjarla Sathish Sumanth, Boorla KITS Department of CSE Telangana Warangal India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

In the present scenario of medical field, Diabetes is a critical issue which do require a lot of attention and if predicted in early stages it can be cured easily. From past few decades many machine learning algorithms are being used for detection at early stages. In this research our principal attention is on prediction of diabetes for pregnant woman that can also be termed as Gestational Diabetes. This may also lead to affecting of Type 2 Diabetes. Several researchers worked hard to predict the results accurately. ANN has considerable edge over other classification algorithms. Machine Learning, Artificial Intelligence, data analytics, and other latest domains are being engaged in prediction of several categories of diseases in the health sector. ANNs (Artificial Neural Network) made a massive impact in medical department further egresses for various data driven applications in domain of health care. It starts with disease diagnosis, image processing of infected organs and finally predicting the disease accurately. Our primary focus is finding out the effect of parameters over dataset to forecast if a specific person is suffering from diabetes or not. PIMA dataset has been considered with 20 neurons for hidden layers and acquired accuracy of 86.2% when there are no outliers in dataset. This research depicts the accuracy &effectiveness of ANN model when compared to other classification algorithms. © 2022 IEEE.

关键词： data analytics

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A Robust Fault Detection and Location Prediction Module Using Support Vector Machine and Gaussian Process Regression for AC Microgrid

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第1期58卷 930-939页

作者： Srivastava, Adhishree Parida, S. K. Birla Inst Technol Dept Elect & Elect Engn Patna 800014 Bihar India Indian Inst Technol Elect Engn Dept Patna 801106 Bihar India

Better reliability of power supply is assured with the inculcation of distributed generators (DGs) in a distribution network. smart sensors and latest grid communication protocols have played a crucial role in the development of intelligent microgrids (MGs). Conventional protection schemes do not provide reliable performance when implemented in MGs. This article proposes an approach which requires root mean square value of one cycle three -phase voltage and current measurements during fault. These data are treated as inputs for developing a fault isolation and locator module. This module is supposed to be available at central protection system, and is designed using machine learning (ML) based techniques viz. Gaussian process regression for fault location prediction and support vector machine for fault identification. Effectiveness of the proposed methodology is evaluated by considering practical grid scenarios with load variation and different DG penetration level. Furthermore, the robustness of the proposed model is assessed by performing sensitivity analysis with consideration of variation in line parameters and load as well as effect of DG correlation. A 7-bus meshed ac MG test system consisting of three DGs and two grid sources is modeled in SIMULINK platform, and is used to demonstrate the proposed module. data analytics tools of MATLAB 2020a has been explored to develop an ML-based fault isolation and location module for MGs. The proposed scheme has also been validated with real-time MG data obtained from OPAL Real time (OPAL-RT) real-time simulator OP-4510. The accuracy in predicted results proves that the proposed scheme is pertinent for real-time practical applications.

关键词： data models Support vector machines Microgrids Voltage measurement Training Fault location Circuit faults Distributed generator (DG) fault location Gaussian process regression machine learning (ML) microgrid (MG) relay support vector machine (SVM)

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