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Analysis of Big Data Research Hotspots Based on Keyword Co-occurrence

Analysis of Big Data Research Hotspots Based on Keyword Co-o...

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IEEE international conference on Data Science in Cyberspace (DSC)

作者： Niuniu Zhang Guohe Feng National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China College of Economics And Management South China Normal University Guangzhou China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In the era of mobile Internet and cloud computing, the rise of big data has profoundly impacted diverse industries and sectors, drawing significant scholarly attention. the objective of this paper is to delineate an overview of the international research landscape of big data, identifying key research hotspots and emerging trends. To gain a comprehensive overview of the current state of research in the field of big data, we employed a range of analytical techniques, including co-word analysis, clustering analysis, social network analysis, and dynamic strategy coordinate mapping. Our review encompassed articles published between 2017 and 2019, spanning a three-year period. the data were sourced from the SCI, SSCI, SPCI, and CPCI-SSH databases. the literature was subjected to a sysytematic analysis using Bibexcel, SPSS, and Gephi software, with the objective of uncovering distinct patterns and trends. the findings reveal 11 thematic clusters within international big data research. these are text mining, data fusion, distributed computing, Industry 4.0, data privacy, business intelligence, precision medicine, bioinformatics, and data mining techniques such as classification and clustering. It is noteworthy that Industry 4.0, data privacy, and text mining emerged as significant research hotspots. Furthermore, the fields of artificial intelligence, natural language processing, smart grids, energy big data, and wireless sensor networks are regarded as emerging areas of interest. It is anticipated that the convergence of bioinformatics and other pivotal areas of big data research, will serve as the impetus for future research hotspots, necessitating sustained scholarly investigation and discourse.

关键词： Text mining Data privacy Wireless sensor networks Social networking (online) Big Data Market research Software Fourth Industrial Revolution Smart grids Bioinformatics

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Multi-Task Lycium barbarum Recognition and Pedicel Localization Network Based on Improved YOLOv8

Multi-Task Lycium barbarum Recognition and Pedicel Localizat...

引用

international conference on Image, Vision and computing (ICIVC)

作者： Fen Li Dongxiao Li Le Huang Ming Zhang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou China Jinhua Institute of Zhejiang University Jinhua China

ISBN: (数字)9798350385991

ISBN: (纸本)9798350386004

Given the unique indeterminate inflorescence trait of Lycium barbarum, traditional indiscriminate harvesting techniques like vibration, comb-brush, or airflow methods, fail to selectively harvest mature fruits, leading to an unsatisfactory picking rate and considerable damage. While a vision system might provide assistance for precise picking, the intricate task of harvesting Lycium barbarum remains challenging due to aspects like their diminutive size, compact distribution, and the presence of various complex occlusions. the three-stage method proposed in this paper facilitates precise object recognition and semantic segmentation of Lycium barbarum and provides individually targeted segmentation of each stem on branches with mature fruits, achieved by organically arranging three operational components based on improved YOLOv8. Moreover, the proposed method has been validated with the LyciumSeg dataset, yielding respective mean average precision (mAP) scores of $\mathbf{9 7. 8 \%}$ for Lycium barbarum recognition and $\mathbf{9 9. 5 \%}$ for pedicel segmentation.

关键词： Vibrations Performance evaluation Semantic segmentation Lighting Network architecture Multitasking Robustness Object recognition Optimization Testing

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Design of A distributed Intrusion Detection System for Streaming Data in IoT Environments

Design of A Distributed Intrusion Detection System for Strea...

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international conference on Optimization and Applications (ICOA)

作者： Souad Atbib Chaimae Saadi Habiba Chaoui Advanced Systems Engineering Laboratory (ISA) National School of Applied Sciences Kenitra Morocco Laboratory of Systems Analysis Information Processing and Industrial Management (LASTIMI) EST Salé University Mohamed V Salé Morocco

Nowadays, IoT devices produce a large amount of streaming data that requires real-time analysis to ensure its security. Traditional security solutions are no longer sufficient to counter recent IoT attacks and to analyze the huge amount of data. In recent years, intrusion detection systems have been the subject of much research to evaluate their performance in detecting intrusions in IoT, especially since they are based on Artificial Intelligence and allow real-time intrusion detection. In this paper, we present the architecture of an intrusion detection system for streaming data analysis. We implemented our IDS in a Fog computing architecture and developed using Apache Spark. We applied several machine-learning algorithms to the NF-ToN-IoT-v2 dataset, and the results were promising.

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An Edge Handover Approach Based on Cloud Synergy for Emerging Mega-constellation

An Edge Handover Approach Based on Cloud Synergy for Emergin...

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IEEE international conference on Smart Cloud (SmartCloud)

作者： Xiangtong Wang Yibing Liu Yuqi Wang Songchen Han Menglong Yang Wei Li Sichuan University Chengdu China National Innovation Institute of Defense Technology Beijing China China Academy of Space Technology Beijing China

ISBN: (数字)9798350389500

ISBN: (纸本)9798350389517

Cloud computing is one of the most used distributed systems for data processing and data storage. With the continuous increase in the demands of the data processed by cloud computing, addressing this need through emerging Low Earth Orbit (LEO) mega-constellation has received widespread attention due to its global low-latency access. In this paper, We propose to realize cloud services through LEO mega-constellation networks and present the cloud synergy framework of satellite-cloud integrated networks (SCIN). We design a simple but effective real-time agent handover strategy under satellite-cloud integrated networks that is based on dynamic programming (DP). For the assurance of real-time, we also modify it to the greedy approach which can obtain a competitive result at a lower cost. the simulation test on starlink-550 mega-constellation shows that the proposed handover approaches outperform the traditional approaches in terms of blocking ratio.

关键词： Cloud computing Satellites Heuristic algorithms Low earth orbit satellites Memory Handover Programming

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On the Performance of Hidden Markov Model Spectrum Opportunity Forecast on Limited Observed Channel Activity 9

On the Performance of Hidden Markov Model Spectrum Opportuni...

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9th international Symposium on computing and networking (CANDAR)

作者： Bezerra, Rodrigo F. Bordim, Jacir L. Lamar, Marcus, V Caetano, Marcos F. Univ Brasilia Dept Comp Sci Brasilia DF Brazil

ISBN: (纸本)9781665442466

the increasing demands for wireless channels to accommodate the surge of internet of things devices and the associated services exacerbated the need for flexible channel allocation strategies. Opportunistic spectrum sharing is expected to provide a more reasonable use of the limited radio frequencies available by allowing the coexistence of licensed users and unlicensed users in the same frequency. this arrangement is called opportunistic channel allocation, where unlicensed users explore the channel when the licensed user is not transmitting. the challenge in opportunistic spectrum allocation is to find transmission opportunities. Accurate opportunity detection mechanisms to avoid interference and improve spectrum usage are highly desirable. Hidden Markov Model training and predicting procedures are proposed in this work to balance the number of training sequences to limit the influence of outliers and provide opportunity forecast even when the training process is executed over a limited number of observed sequences. Our findings show that higher accuracy can be obtained even when the HMM is trained with a reduced number of transmission sequences. the results show that, compared to similar works, the proposed alternatives reduce collision rates while improving the overall number of seized transmission opportunities. the proposed HMM training procedures are able to identify over 90% of channel opportunities with PU load ranging from 20% to 80% of the channel capacity. Also, the collision rates, that is, when both PU and SU would be transmitting concurrently on the channel, was less than 10% for PU load in 30-90% of the channel capacity. Furthermore, the proposed HMM training procedures reduced the collision rate by 45.1% and improved the number of seized opportunities by 4.9%.

关键词： Transmission opportunity forecast Opportunistic spectrum access Hidden Markov Model Poisson distributed Traffic

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Predictive Salary Modelling: Leveraging Data Science Skills and Machine Learning for Accurate Forecasting

Predictive Salary Modelling: Leveraging Data Science Skills ...

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international conference on Communication and Electronics Systems (ICCES)

作者： Mohammed Ayman Haseeb Rishi Viswanathan Kaushik Iyer Anish Raj Hota Banu Priya Prathaban Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

In approaching predictive salary modeling, this article identified important job positions and skills in the data science business environment. they show the need for a model that would allow employers to calculate the required starting salary while considering sectors, Python known languages, SQL, and ML. It also analyzes linear regression models, Random Forests, and neural network point of view about salary prediction. Analyzing the findings, it is proved that skill-based salary models should be further developed to meet industry requirements, advise students, and support employers. We have applied various models as follows with random forest performing the best giving out an R2 score of 0.971. the ideas for building faultless, precision, and skill-based salary estimation models are presented to the readers at the end of the article.

关键词： Analytical models Accuracy Linear regression Neural networks Predictive models Data science Prediction algorithms Data models Remuneration Random forests

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Fog Enabled distributed Training Architecture for Federated Learning 9th

Fog Enabled Distributed Training Architecture for Federated ...

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9th international conference on Big Data Analytics (BDA)

作者： Kumar, Aditya Srirama, Satish Narayana Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Telangana India

ISBN: (纸本)9783030936198;9783030936204

the amount of data being produced at every epoch of second is increasing every moment. Various sensors, cameras and smart gadgets produce continuous data throughout its installation. Processing and analyzing raw data at a cloud server faces several challenges such as bandwidth, congestion, latency, privacy and security. Fog computing brings computational resources closer to IoT that addresses some of these issues. these IoT devices have low computational capability, which is insufficient to train machine learning. Mining hidden patterns and inferential rules from continuously growing data is crucial for various applications. Due to growing privacy concerns, privacy preserving machine learning is another aspect that needs to be inculcated. In this paper, we have proposed a fog enabled distributed training architecture for machine learning tasks using resources constrained devices. the proposed architecture trains machine learning model on rapidly changing data using online learning. the network is inlined with privacy preserving federated learning training. Further, the learning capability of architecture is tested on a real world IIoT use case. We trained a neural network model for human position detection in IIoT setup on rapidly changing data.

关键词： Internet of things Decentralized learning Fog computing

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Proceedings of EduHPC 2021: Workshop on Education for High-Performance computing, Held in conjunction with SC 2021: the international conference for High Performance computing, networking, Storage and Analysis

Proceedings of EduHPC 2021: Workshop on Education for High-P...

引用

9th IEEE/ACM Workshop on Education for High-Performance computing, EduHPC 2021

ISBN: (纸本)9781665411165

the proceedings contain 7 papers. the topics discussed include: experience and practice teaching an undergraduate course on diverse heterogeneous architectures;the hour of cyberinfrastructure (hour of ci): early findings from pilot study to build cyber literacy for GIScience;educating HPC users in the use of advanced computing technology;visualizing parallel dynamic programming using the thread safe graphics library;automating testing of visual observed concurrency;lightning talks of EDuHPC 2021;and peachy parallel assignments (EDuHPC 2021).

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Survey on Security Protocols for IoT

Survey on Security Protocols for IoT

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international conference for Convergence of Technology (I2CT)

作者： Anamika Rakshe Nilima Dongre Ramrao Adik Institute of Technology D Y Patil Vidyapeeth Navi Mumbai India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

the Internet of things (IoT) represents a network of distinct objects with embedded software for exchanging transient states and data, essential for activating actuators. To interact with a cloud conducting big data analytics and supporting business decisions, edge networking devices and protocols are integral. Key sectors like industrial, agricultural, healthcare, and smart cities heavily rely on IoT, necessitating robust security measures. IoT protocols, functioning on IP protocols, address security across different layers, confronting challenges such as inconsistent manufacturing practices leaving hardware and data vulnerable. It is very significant to ensure data privacy, trust management, authentication and authorization in a heterogeneous environment. Addressing the vulnerabilities at edge and cloud service providers' sites are critical aspects to be addressed, which extensively details security breaches and the protective mechanisms offered by IoT protocols.

关键词： Surveys Cloud computing Protocols Smart cities Computer architecture Internet of things Security

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A distributed TSN Time Synchronization Algorithm with Increased Tolerance for Failure Scenarios

A Distributed TSN Time Synchronization Algorithm with Increa...

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international conference on Computer and Communications (ICCC)

作者： Yifei Peng Xinlin Wu Xiaodong Tu Tigang Jiang Du Xu Jun Xie School of Information and Communication Engineering University of Electronic Science & Technology of China Chengdu China

the integration of Industrial Internet of things (IIoT) and Time-Sensitive networking (TSN) enables efficient and reliable real-time communication and control. Currently, one of the current research hotspots in the field of TSN is the improvement of reliability in the use of IEEE 802.1AS(IEEE Standard for Local and metropolitan area networks - Timing and Synchronization for Time-Sensitive Applications). On one hand, IEEE 802.1AS does not provide specific steps for constructing redundant clock trees, and on the other hand, it cannot detect error time values caused by the failure propagation of devices on the clock tree. To address these issues, this paper proposes a distributed time synchronization strategy based on IEEE 802.1AS(DAS). DAS utilizes a broadcast-based message exchange, and each endpoint device calculates its own synchronized time using fault-tolerant time synchronization methods. Furthermore, the time information is no longer embedded in data packets, avoiding the impact of erroneous time values on the time synchronization of other devices. Extensive simulations and on-board verifications are conducted on the DAS algorithm in this paper. the results show that the DAS algorithm can tolerate more failure scenarios while maintaining similar time synchronization accuracy compared to IEEE 802.1AS. Although the DAS algorithm consumes more bandwidth, this overhead is acceptable in small-scale networks. As far as we know, our work is the first in the industry to simultaneously achieve compatibility with existing 802.1AS and distributed TSN time synchronization, and to physically implement it in switch.

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