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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Vedhika Srinidhi, V.S. Sanjana, S. Rozario, Neo Joseph Dewan, Sia Gouthaman, P. School of Computing College of Engineering & Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing Faculty of Engineering & Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331518097

Big Data analytics is a rapidly evolving field that offers profound insights into various sectors by enabling the analysis of massive and diverse datasets. This research paper focuses on the application of Big Data techniques to social media and airline networks, leveraging tools such as sentiment analysis, clustering, and data visualization to extract actionable information. The study analyses Twitter data to uncover public sentiment trends and classifies topics using advanced machine learning techniques. Furthermore, the research explores the use of clustering algorithms within the Hadoop ecosystem to process large-scale datasets efficiently. Insights from airline customer feedback and network planning data demonstrate how Big Data analytics can optimize operational efficiency and enhance customer satisfaction. Key findings include the identification of distinct sentiment patterns in social media data, the efficiency of novel clustering techniques in handling high-volume datasets, and the utility of advanced visualization methods to convey complex data. These results have significant implications for industries ranging from social media platforms to the airline sector, providing valuable tools for enhancing decision-making processes and improving user experiences across diverse domains. The study highlights the potential of Big Data analytics to drive innovation and operational excellence in dynamic environments. © 2024 IEEE.

关键词： Airline networks Big Data analytics Clustering algorithms Data visualization Sentiment analysis Social media

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Detecting a suitable path method for assisted localization techniques in transmission of data over wireless sensor networks

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International Journal of Information technology (Singapore) 2025年第4期17卷 2235-2242页

作者： Navaneethan, S. Arun, A. Department of Networking & Communications School of Computing SRM Institute of Science and Technology SRM Nagar Tamilnadu KattanKulathur 603203 India Department of Networking & Communications School of Computing Faculty of Engineering & Technology SRM Institute of Science and Technology Tamilnadu KattanKulathur 603203 India

In many Wireless Sensor Networks (WSNs) applications, the relevant sensor node’s location information is essential in determining where the event or situation occurs. Therefore, localization is one of the critical challenges in WSNs. Mobile Anchor Node Assisted Localization (MANAL) is one of the promising solutions for the localization of statically deployed sensors. The main problem in MANAL is that the path planning of the Mobile Anchor Node (MAN) should be done in a way that the localization error in the network will be minimal and that all unknown nodes in the network are covered. In this paper, a new path planning approach called Nested Hexagons Curves (NH-exCurves) is proposed for MANAL. NH-exCurves guarantees that it will receive messages from at least three non-collinear anchors to locate all unknown nodes in the network. The proposed model is compared to six different path planning schemes using Accuracy Priority Trilateration (APT) under different evaluation criteria. In these comparisons, first of all, localization errors of the models are compared using some statistical concepts. Secondly, the variation of the localization error according to parameters such as resolution (R) and the standard deviation of noise (σ) are observed. The simulation results present the superiority of the proposed work over existing path planning models used in this study. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Accuracy priority trilateration (APT) Localization Mobile anchor node assisted localization (MANAL) Static path planning Wireless sensor networks (WSNs)

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Real-Time Weapon Detection System for Enhanced Security Monitoring 4

Real-Time Weapon Detection System for Enhanced Security Moni...

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4th International Conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Kuppili, Rohan Agarwal, Khusboo Bhosale, Tanmay Gouthaman, P. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331529635

Innovative technology for weapon detection during security monitoring is necessary today due to the rising threats of violence and terror. The paper discusses a detailed design of a weapon detection system intended for application in enhancing security surveillance specifically using a deep learning approach. The system employs YOLOv3 (You Only Look Once, Version 3) mood and adaption aimed at live footage weapon detection. To do this on broader scale, this system incorporates big data tools Hadoop, Docker, Spark and Kafka for reasons of real time processing and effectiveness in high volume data situations. This model is applied on the live footage surveillance of public and private places to determine if the model can detect weapons, to warn relevant authorities in the aim of decreasing response time and averting security breach occurrences. The experiments conducted confirm that the model is capable of performing weapon detection tasks in an indoor environment with changes in illumination conditions with few false alarms. © 2024 IEEE.

关键词： Big data

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Machine learning empowered UAV-based beamforming design in ISAC systems

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Science China(Information Sciences) 2025年第5期68卷 285-286页

作者： Xiangyang DUAN Xiaoqi ZHANG Shuqiang XIA Zhongbin WANG Yihua MA Weijie YUAN State Key Laboratory of Mobile Network and Mobile Multimedia Technology Wireless and Computing Research InstituteZTE Corporation School of Electrical and Data Engineering University of Technology Sydney School of Automation and Intelligent Manufacturing Southern University of Science and Technology

Integrated sensing and communication (ISAC) has been proposed as an enabling technology for the realization of the next-generation wireless system,which focuses on performing wireless communication and sensing *** the various potential ISAC-based applications,unmanned aerial vehicle (UAV)-based ISAC plays a significant part in unlocking the potential of future next-generation wireless communication,facilitating low-latency data transmission in high-mobility *** by recent advancements,a variety of effective techniques have been investigated to optimize beamforming design in ISAC *** instance,the authors in [1] introduced an extended Kalman filtering (EKF)-based method tailored for millimeter wave (mmWave) ISAC ***,Ref.[2]proposed an extended interacting multiple model (IMM)-EKF framework designed for vehicular networks with intricate roadway *** these advancements,the aforementioned methods typically employ a separate scheme for channel prediction and beam alignment,which introduces additional signaling overhead in real-world ***,there is a demand for an end-to-end beamforming design approach specifically for UAV-based ISAC systems.

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Relative humidity prediction across the Indian subcontinent using Kumaraswamy distribution based non-Gaussian model

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Environmental Science and Pollution Research 2024年第59期31卷 66780-66795页

作者： Shad, Mohammad Sharma, Yogeshver Dutt Narula, Pankaj Department of Mathematics and Scientific Computing National Institute of Technology Himachal Pradesh Hamirpur177005 India School of Mathematics Thapar Institute of Engineering & Technology Punjab Patiala147004 India

Relative humidity (RH) significantly influences various aspects of human life, including agriculture, weather prediction, indoor air quality, and energy consumption. Its intricate non-linear behavior poses a significant challenge for accurate estimation. In the context of Indian climate change, precise forecasting of RH is vital for agriculture and weather prediction. This study proposes a new non-Gaussian approach, the Kumaraswamy seasonal autoregressive moving average (KSARMA) model, for RH prediction across India’s five climatic zones (hot-dry, warm-humid, moderate, cold, and composite). Analyzing monthly RH data from 1981 to 2021, model selection was based on diagnostic analysis and Akaike’s information criterion (AIC), with parameter estimation utilizing conditional maximum likelihood estimators (CMLE). The KSARMA model demonstrated superior predictive accuracy compared to other models (e.g., multilayer perceptron, SARIMA, Holt-Winter, and βSARMA) across all climatic zones, except the hot-dry zone, as supported by error metrics including RMSE (0.04–0.12), MAE (0.03–0.1), SMAPE (0.04–0.2), and MASE (0.5–.08). © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Indoor air pollution

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CALTM:A Context-Aware Long-Term Time-Series Forecasting Model

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Computer Modeling in engineering & Sciences 2024年第4期139卷 873-891页

作者： Canghong Jin Jiapeng Chen Shuyu Wu Hao Wu Shuoping Wang Jing Ying School of Computer and Computing Science Hangzhou City UniversityHangzhou310015China School of Computer Science and Engineering Macao University of Science and TechnologyMacao999078China College of Computer Science and Technology Zhejiang UniversityHangzhou310058China

Time series data plays a crucial role in intelligent transportation *** flow forecasting represents a precise estimation of future traffic flow within a specific region and time *** approaches,including sequence periodic,regression,and deep learning models,have shown promising results in short-term series ***,forecasting scenarios specifically focused on holiday traffic flow present unique challenges,such as distinct traffic patterns during vacations and the increased demand for long-term ***,the effectiveness of existing methods diminishes in such ***,we propose a novel longterm forecasting model based on scene matching and embedding fusion representation to forecast long-term holiday traffic *** model comprises three components:the similar scene matching module,responsible for extracting Similar Scene Features;the long-short term representation fusion module,which integrates scenario embeddings;and a simple fully connected layer at the head for making the final *** results on real datasets demonstrate that our model outperforms other methods,particularly in medium and long-term forecasting scenarios.

关键词： Traffic volume forecasting scene matching multi module fusion

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Financial Fraud Detection for Credit Card Transactions Using Apache Spark 5

Financial Fraud Detection for Credit Card Transactions Using...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Jha, Pratyush Srivastava, Srijan Gandhi, Tanishq Gouthaman, P. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331530013

The financial sector's rapid adoption of digital transformation has made identifying fraud a progressively complex challenge, since modern fraudulent activities present significant risks to institutions globally. This paper presents a novel methodology for detecting financial fraud that integrates big data analytics and machine learning techniques within a distributed environment. With an extensive number of actual financial transactions, the system uses Apache Spark for real-time data processing and machine learning algorithms to accurately classify fraudulent transactions. This method effectively addresses two major issues: efficiently processing large data sets and managing the severe class imbalance between fraudulent and non-fraudulent transactions. Collectively, these solutions provide a scalable and resilient framework for improving fraud detection in the digital era. © 2024 IEEE.

关键词： Nearest neighbor search

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OFDM-structure based waveform designs for integrated sensing and communication

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Science China(Information Sciences) 2025年第5期68卷 267-284页

作者： Jiaqi LIU Ping YANG Ke JIANG Jiarong ZHAO Wei XIANG Jianping WEI Ruixiang DUAN Saviour ZAMMIT Tony Q.S.QUEK National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China School of Computing Engineering and Mathematical Sciences La Trobe University Meituan Academy of Robotics Department of Communications and Computer Engineering University of Malta Information Systems Technology and Design Pillar Singapore University of Technology and Design

The future sixth-generation(6G) paradigm aims to seamlessly integrate communication and environmental sensing capabilities into a single radio signal, promising improved efficiency and cost-effectiveness through simultaneous data communications and environmental perception. At the core of this evolution, orthogonal frequency division multiplexing(OFDM) and its advanced waveforms emerge as pivotal for integrated sensing and communications(ISAC). This study introduces a concise and unified ISAC waveform design framework based on orthogonal multicarriers. This framework supports versatile applications of OFDM and its derivative waveforms within a generalized ISAC system, marking a significant leap in integrating communication and sensing capabilities. A distinguishing feature of this framework is its adaptability,allowing users to intelligently select modulation strategies based on their specific environmental needs. This adaptability optimizes performance across diverse scenarios. Central to our innovations is the proposal of discrete Fourier transformspread OFDM with index modulation(DFT-S-OFDM-IM). This framework is paired with newly proposed signal processing methods for single-input single-output and multiple-input multiple-output(MIMO) systems. Extensive evaluations highlight DFT-S-OFDM-IM's superiority, including dramatically reduced peak-to-average power ratios(PAPRs), competitive communication performance, and exceptional sensing capabilities, striking an elegant balance between communication capacity and environmental sensing precision.

关键词： DFT-S-OFDM-IM index modulation integrated sensing and communication OFDM

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A Novel Proactive AI-Based Agents Framework for an IoE-Based Smart Things Monitoring System with Applications for Smart Vehicles

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Computers, Materials & Continua 2025年第2期82卷 1839-1855页

作者： Meng-Hua Yen Nilamadhab Mishra Win-Jet Luo Chu-En Lin Department of Electronic Engineering National Chin-Yi University of TechnologyTaichung411030Taiwan School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India Department of Refrigeration Air-Conditioning and Energy EngineeringNational Chin-Yi University of TechnologyTaichung411030Taiwan

The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one *** featured in the IoE include embedding,networking,and sensing *** achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked *** IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel *** Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this *** STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor *** STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle *** information is utilized to locate the vehicle in an accident or *** findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have ***,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and *** our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV *** framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.

关键词： Artificial intelligence(AI) proactive AI-based learning agents(PLA) internet of everything(IoE) smart things monitoring system(STMS) cloud processing system driving monitoring assistance system(MAS) smart vehicles

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Fully Distributed Nash Equilibrium Seeking for High-Order Players With Actuator Limitations

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1434-1444页

作者： Maojiao Ye Qing-Long Han Lei Ding Shengyuan Xu the School of Automation Nanjing University of Science and TechnologyNanjing 210094China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the Institute of Advanced Technology Nanjing University of Posts and TelecommunicationsNanjing 210023China

This paper explores the problem of distributed Nash equilibrium seeking in games, where players have limited knowledge on other players' actions. In particular, the involved players are considered to be high-order integrators with their control inputs constrained within a pre-specified region. A linear transformation for players' dynamics is firstly utilized to facilitate the design of bounded control inputs incorporating multiple saturation functions. By introducing consensus protocols with adaptive and time-varying gains, the unknown actions for players are distributively estimated. Then, a fully distributed Nash equilibrium seeking strategy is exploited, showcasing its remarkable properties: (1) ensuring the boundedness of control inputs;(2) avoiding any global information/parameters;and (3) allowing the graph to be directed. Based on Lyapunov stability analysis, it is theoretically proved that the proposed distributed control strategy can lead all the players' actions to the Nash equilibrium. Finally, an illustrative example is given to validate effectiveness of the proposed method.

关键词： Actuator limitation directed networks games Nash equilibrium

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