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A Blockchain-based and Privacy-Protected Method for Sharing of BIM Big data 7

A Blockchain-based and Privacy-Protected Method for Sharing ...

引用

7th international conference on Cloud computing and Big data analytics, ICCCBDA 2022

作者： Wang, Hongwei Hao, Xiaohan Yin, Lian Gong, Peisong Xiong, Feng Ren, Wei Wuhan Municipal Road and Bridge Co. Ltd Wuhan430050 China School of Software University of Technology Sydney UltimoNSW2007 Australia School of Computer Science China University of Geosciences Wuhan430074 China School of Economics and Management China University of Geosciences Wuhan430074 China School of Business Administration Zhongnan University of Economics and Law Wuhan430073 China Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan430074 China

ISBN: (纸本)9781665487115

Currently, cloud computing and big data are largely applied in smart construction, or so-called industrial Internet of Things 4.0. Building Information Model (BIM) performs as an elemental information and data unit, which is extensively employed in entire construction processes (lifespan) and inter-Acted with various entities including investors, employers, design institutes, construction companies, and monitors. They thus can achieve quantitative computing, measurement, and managements by sharing BIM data. The accumulation of BIM data will finally approach to a big data for construction intelligence. However, sharing BIM big data may be subtle in terms of privacy leakage, since BIM data may enclose the critical infrastructure information, homeland security data, structure parameters, and design intellectual property. It thus is of urgent importance to protect privacy in the BIM data sharing. In this paper, we propose a blockchain based method to protect data privacy in BIM data sharing, which can be applied for guaranteeing the information security in next generation industrial Internet of Things for smart construction. © 2022 IEEE.

关键词： data privacy Cloud computing Computational modeling Information security Big data data models Blockchains

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Study of the Structural Renewal of "Snow Bud" in Shutao Company Based on intelligent Systems and Big data analytics

Study of the Structural Renewal of "Snow Bud" in Shutao Comp...

引用

international conference on Cyber Security Intelligence and analytics (CSIA)

作者： Xu, Yiming Gu, Siyang Li, Pinwu Sichuan Agr Univ Chengdu 611130 Sichuan Peoples R China

ISBN: (纸本)9783030152352;9783030152345

The improved "Snow Bud" packaging mainly includes the medium packing and the external packaging, with the cuboid as the main structure, and the paper as the main material. The inner packaging uses the tin foil paper bag. Because of the thin thickness of the carton used and the problems in colors, patterns, character design, structures and other aspects, the purpose of the improvement is to use the new structures to enhance the practical functions of the packaging and to explore the application of the new materials in the tea packaging.

关键词： Shutao "Snow Buds" Packaging structure Packaging materials

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Optimal Hyperparameter Tuning using Meta-Learning for Big Traffic datasets

Optimal Hyperparameter Tuning using Meta-Learning for Big Tr...

引用

IEEE international conference on Big data and smart computing (BigComp)

作者： Bui, Khac-Hoai Nam Yi, Hongsuk Korea Inst Sci & Technol Informat Ctr Supercomp Applicat Daejeon South Korea

ISBN: (纸本)9781728160344

Big traffic data is an emergent issue of intelligent Transportation System (ITS) since a large of data can be produced every day. Consequently, traffic flow prediction becomes the main component for developing ITS in terms of providing accurate and timely traffic information. In this study, we take the traffic forecasting problem into account using Deep learning (DL) models. Specifically, traffic data are from many devices on the roads in which training by DL models have to face with expensive problems (e.g., time-consuming and human expertise). Therefore, we focus on hyperparameter tuning for training big traffic datasets using meta-learning to improve the automatic learning process and reduce time-consuming tasks. Regarding the experiment, we take data from the Vehicle Detection System (VDS) as the case study for evaluating our approach. Specifically, data have collected from 21 sensors which are located in an urban area. The experiments show promising results of our proposed approach for training multiple traffic datasets.

关键词： intelligent transportation system big traffic datasets meta-learning hyperparameter optimization traffic prediction vehicle detection systems

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Predictive analytics for Human Resource Management: Enhancing Talent Acquisition and Retention Using data analytics

Predictive Analytics for Human Resource Management: Enhancin...

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2024 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2024

作者： Radhasri, D. Easwara Moorthy, R. Vinothini, K. Karthikeyan, S. Aswini, L. Dr.SNS Rajalakshmi College of Arts and Science Department of Commerce Tamilnadu India

ISBN: (纸本)9798331543617

HRM depends on the output of predictive analytics by which the input achieves accurate data on employee hiring and retention. For enhancing the decision-making in the HR domain, this research proposes a novel multi-modal logistic regression, XGBoost, and deep learning-based designated hybrid prediction model. Due to the integration of several more common and uncommon HR datasets and richer feature engineering techniques, the mentioned model eliminates the limitations of previous studies. These enhance the chances of applicant selection and consequently the chances of the applicants' retention. The proposed hybrid model achieved 91.4% accuracy for applicant selection and 89.7% for employee retention;the accuracy of the models developed in the years 2021, 2022 & 2023, was 82.3%, 84.9%, and 87.4% respectively. Its implementation using IBM Watson and Kafka will afford HR managers practical real-time information from the model. Lastly, the benefits of applying the model to enhance the HR procedures are highlighted and recommendations for further work to increase the model's applicability in other areas are made. © 2024 IEEE.

关键词： data accuracy

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smart interface development for sensor data analytics in internet of robotic things

Smart interface development for sensor data analytics in int...

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2019 international conference on intelligent computing and Control Systems, ICCS 2019

作者： Mahakalkar, Namrata Pethe, Rahul Kimmatkar, Kiran M. Durga Tiwari, Honey Priyadarshini Institute of Engineering and Technology Computer Science Engineering Nagpur Maharashtra India Priyadarshini J. L. College of Engineering Electrical Engineering Nagpur Maharashtra India Priyadarshini Institute of Engineering and Technology Electronics and Communication Engineering Nagpur Maharashtra India Narsimha Reddy Engineering College Department of Electronics and Communication Secunderabad Telangana State India

ISBN: (纸本)9781538681138

In recent years, the number of transducers connected to a common entity has increased significantly. In order to develop collaborative networks of robotic elements, the data from each sensor is shared through the network. As the number of sensors increases the data handled by the network also increases significantly. This causes serious network congestion issues even for a small network. In this paper, a framework for smart sensor interface is presented to optimize the data sharing and diagnostics operations. Different functions, operating conditions and architecture for cloud connectivity of sensor interface is presented. The implementation scheme and verification experiment are presented to show the application of the proposed design in multiple related sensors. The partial experiment results show that the proposed scheme can provide valuable insight to detect any deviation from normal behavior. Based on this information smart sensor and remote application can be used to take appropriate action. The partial results show that the proposed scheme can be employed for sensor data analytics in collaborative robotic networks. © 2019 IEEE.

关键词： data analytics

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Enabling smart Homes Through Health Informatics and Internet of Things for Enhanced Living Environments 8th

Enabling Smart Homes Through Health Informatics and Internet...

引用

World conference on Information Systems and Technologies (WorldCIST)

作者： Marques, Goncalo Pitarma, Rui Univ Beira Interior Inst Telecomunicacoes P-6201001 Covilha Portugal Polytech Inst Guarda P-6300559 Guarda Portugal Univ Beira Interior CISE Electromechatron Syst Res Ctr P-6201001 Covilha Portugal

ISBN: (纸本)9783030456962;9783030456979

As people spend most of their time inside buildings, indoor environment quality must be monitored in real-time for enhanced living environments and occupational health. Indoor environmental quality assessment is based on the satisfaction of the thermal, sound, light and air quality conditions. The indoor quality patterns can be directly used to promote health and wellbeing. With the proliferation of the Internet of Things related technologies, smart homes must incorporate monitoring solutions for data acquisition, transmission, and microsensors for several real-time monitoring activities. This paper presents a low-cost and scalable multi-sensor smart home solution based on Internet of Things for enhanced indoor quality considering acoustic, thermal and luminous comfort. The proposed system incorporates three sensor modules for data collection and use Wi-Fi communication technology for Internet access. The system has been developed using open-source and mobile computing technologies for real-time data visualization and analytics. The acquisition modules incorporate light intensity and colour temperature, particulate matter, formaldehyde, relative humidity, ambient temperature and sound sensor capabilities. The results have successfully validated the scalability, reliability and easy installation of the proposed system.

关键词： Ambient assisted living Enhanced living environments Health informatics Indoor environmental quality Internet of Things smart home

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Three-phase imbalance control method for distribution grid based on SVG and phase switch technology 1

引用

1st international conference on Big data analytics for Cyber-Physical System in smart City, BDCPS 2019

作者： Wang, Yangtian College of Electrical Engineering and New Energy China Three Gorges University YichangHubei443002 China

ISBN: (数字)9789811525681

ISBN: (纸本)9789811525674

With the increasing diversification of unbalanced load in distribution grid in smart city, the three-phase imbalance becomes more and more serious, which has a great impact on the safety of power grid operation and the quality of electric power. To realize the load adjustment in power distribution and transformation for the goal of power loss reduction, this paper firstly systematically analyzes the power loss of each part in the system, and compares the characteristics and balancing outcome between various adjustment technologies on the analysis basis. Then, this paper further comes up with the comprehensive compensation method by combing Static Var Generator (SVG) technology with phase switching technology. This method can not only effectively improve the power loss of low-voltage power lines, but also can realize the optimal balance adjustment in distribution network. The correctness and plausibility of this method is further verified by simulation. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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intelligent Video analytics for Better Planning of smart Cities using Edge computing 13

Intelligent Video Analytics for Better Planning of Smart Cit...

引用

13th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2022

作者： Shetty, Savita Maheshwari, Raghav Mehta, Rithika Kashyap, Shirsh Vardhan Ramaiah Institute of Technology Visvesvaraya Technological University Bangalore Belagavi India

ISBN: (纸本)9781713857938

Recent years have shown how machine learning and deep learning techniques can be used to radically transform smart cities. However, current cloud-centric processing approaches present latency, privacy, and bandwidth-related challenges. Edge computing is a solution to the above challenges where the video feeds captured can be analyzed right at the source of generation of the feed while deeper insights and analytics can still be performed on the cloud. The edge computing approach to video analytics also enables us to take action to certain situations in real-time which is not possible in cloud-centric approaches. In this paper, we propose certain methods for better planning of smart cities using edge computing. The data points generated for the proposed methods are based on 2 use cases - traffic counting using Nvidia DetectNet and pedestrian face mask detection using MobileNetV2. We also discuss how video frame resizing can be used for enhanced processing on the edge device. © Grenze Scientific Society, 2022.

关键词： Edge computing

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Edge computing and 5G Integration for Real-time analytics in Interoperable smart Grids

Edge Computing and 5G Integration for Real-time Analytics in...

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2023 international conference on Power Energy, Environment and intelligent Control, PEEIC 2023

作者： Devi, Kskn Venkata Ramana Koithyar, Aravinda Lakhanpal, Sorabh Parmar, Ashish Sethi, Vandana Arora Ftaiet, Adnan Allwi Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Lloyd Institute of Management and Technology Plot No.-11 Knowledge Park-II Uttar Pradesh Greater Noida201306 India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350357769

In the rapidly evolving landscape of power and energy systems, the smart Grid (SG) emerges as a dynamic and adaptive solution to cater to the ever-increasing energy demands while ensuring sustainable growth. The core essence of a smart Grid is its ability to provide real-time analytics, facilitating efficient energy management and distribution. With the influx of a myriad of heterogeneous devices and systems in the grid, interoperability emerges as a crucial factor to ensure seamless operations. This paper delves deep into the symbiotic relationship between Edge computing (EC) and the fifth generation (5G) mobile networks, illustrating their potential to revolutionize real-time analytics in interoperable smart Grids. Edge computing, with its decentralized data processing capabilities, caters to the latency-sensitive needs of SG systems. On the other hand, 5G, with its high bandwidth and ultra-reliable low latency communication, offers robust support to EC operations. Their integration promises unprecedented improvements in the efficiency, reliability, and resilience of SGs. Through an exhaustive study, this paper highlights the architectural enhancements, challenges, and potential solutions, casting a vision for the future of intelligent energy systems. © 2023 IEEE.

关键词： 5G Mobile Networks Edge computing Energy Management. Top of Form Real-time analytics smart Grid Interoperability

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Big data analytics Concepts, Technologies Challenges, and Opportunities 5th

Big Data Analytics Concepts, Technologies Challenges, and Op...

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5th international conference on Advanced intelligent Systems and Informatics (AISI)

作者： Shehab, Noha Badawy, Mahmoud Arafat, Hesham Mansoura Univ Fac Engn Comp Engn & Control Syst Dept Mansoura Egypt

ISBN: (纸本)9783030311292;9783030311285

The rapid observed increase in using the Internet led to the presence of huge amounts of data. Traditional data technologies, techniques, and even applications cannot cope with the new data's volume, structure, and types of styles. Big data concepts come to assimilate this non-stop flooding. Big data analysis process used to jewel the useful data and exclude the other one which provides better results with minimum resource utilization, time, and cost. Feature selection principle is a traditional data dimension reduction technique, and big data analytics provided modern technologies and frameworks that feature selection can be integrated with them to provide better performance for the principle itself and help in preprocessing of big data on the other hand. The main objective of this paper is to survey the most recent research challenges for big data analysis and preprocessing processes. The analysis is carried out via acquiring data from resources, storing them, then filtered to pick up the useful ones and dismissing the unwanted ones then extracting information. Before analyzing data, it needs preparation to remove noise, fix incomplete data and put it in a suitable pattern. This is done in the preprocessing step by various models like data reduction, cleaning, normalization, preparation, integration, and transformation.

关键词： Big data analytics Big data preprocessing Feature selection Flink

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