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High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting

ALGORITHMS 2025年第4期18卷 182-182页

作者： Myllis, Georgios Tsimpiris, Alkiviadis Aggelopoulos, Stamatios Vrana, Vasiliki G. Int Hellen Univ Dept Comp Informat & Telecommun Engn Serres 62124 Greece Int Hellen Univ Dept Agr Thessaloniki 57001 Greece Int Hellen Univ Dept Business Adm Serres 62124 Greece

This paper explores the application of parallel algorithms and high-performance computing (HPC) in the processing and forecasting of large-scale water demand data. Building upon prior work, which identified the need for more robust and scalable forecasting models, this study integrates parallel computing frameworks such as Apache Spark for distributed data processing, Message Passing Interface (MPI) for fine-grained parallel execution, and CUDA-enabled GPUs for deep learning acceleration. These advancements significantly improve model training and deployment speed, enabling near-real-time data processing. Apache Spark's in-memory computing and distributed data handling optimize data preprocessing and model execution, while MPI provides enhanced control over custom parallel algorithms, ensuring high performance in complex simulations. By leveraging these techniques, urban water utilities can implement scalable, efficient, and reliable forecasting solutions critical for sustainable water resource management in increasingly complex environments. Additionally, expanding these models to larger datasets and diverse regional contexts will be essential for validating their robustness and applicability in different urban settings. Addressing these challenges will help bridge the gap between theoretical advancements and practical implementation, ensuring that HPC-driven forecasting models provide actionable insights for real-world water management decision-making.

关键词： High-Performance Computing (HPC) parallel algorithms water demand forecasting Apache Spark Message Passing Interface (MPI) machine learning time-series analysis real-time data processing urban water management computational efficiency deep learning models data imputation scalability anomaly detection distributed computing frameworks

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An Intelligent IoT Framework for real-time Energy-Efficient Smart Building Management 5th

An Intelligent IoT Framework for Real-Time Energy-Efficient ...

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5th 5th Olympus - International Conference on Supply Chain

作者： Dimara, Asimina Papaioannou, Alexios Papaioannou, Christophoros Krinidis, Stelios Anagnostopoulos, Christos-Nikolaos Democritus Univ Thrace DUTh Management Sci & Technol Dept Kavala Greece Univ Aegean UAEGEAN Dept Cultural Technol & Commun Intelligent Syst Lab Mitilini Greece Int Hellen Univ IHU Management Sci & Technol Dept Kavala Greece

ISBN: (纸本)9783031693434;9783031693441

The increasing demand for sustainable smart building management systems has motivated the development of advanced solutions, among which is the Internet of Things (IoT). This paper introduces a comprehensive Intelligent IoT framework that has been methodically designed for energy-efficient smart building management in real-time. The approach displays the complex details of the architecture, highlighting significant components such as technologically advanced sensors and real-time data processing systems. The framework's implementation instructions, which describe technologies, programming languages, and compatibility issues, offer a step-by-step guidance with a focus on seamless integration. Implementation-related challenges, such as technical complexities and interoperability problems, are addressed with innovative workarounds and useful solutions, adding important insights to the area. The framework's effectiveness is demonstrated by the way real-time data is employed to inform decision-making and promote flexibility for sustainable building management. Additionally supporting the field of smart building technology, this research provides to the stakeholders who are interested in enhancing built environments' energy efficiency with practical insights. To sum up, the Intelligent IoT Framework delivers a comprehensive solution for the dynamic field of real-time energy-efficient smart building management by serving as a prototype for the synergy between creative methodology and practical implementation.

关键词： Smart Building Management Internet of Things (IoT) real-time data processing Smart Building Management Energy Efficiency

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Leveraging the Industrial Internet of Things (IIoT) for real-time CO2 Monitoring, Measurement and Visualization: Technologies, Applications and Future Directions 1st

Leveraging the Industrial Internet of Things (IIoT) for Real...

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1st Global Internet of Things and Edge Computing Summit

作者： Christensen, Mads S-F. Aarhus Univ BTECH 7400 Herning Aarhus Denmark

ISBN: (纸本)9783031785719;9783031785726

Global CO2 emissions reduction requires industries to manage and understand their CO2 emission levels in real-time. This paper examines the Industrial Internet of Things (IIoT) for real-time monitoring, measurement, and visualization of reducing CO2 emissions in industrial and environmental domains. Methodology: The methodology consists of a literature reviewbased on peer-reviewed publications and use cases to explore the current state and practical implications. Furthermore, a technical analysis of IIoT systems, CO2 sensors, and data processing techniques is also identified. Results: IIoT systems can support CO2 emission monitoring and accuracy optimization in industrial domains by combining CO2 sensors, wireless communication, and data fusion techniques. In addition, machine learning and artificial intelligence can be used to reduce anomalies in CO2 sensor readings and predictive maintenance of systems. Challenges: Challenges include interoperability, data security and system scalability. To resolve these issues standardized communication protocols, data security methods and implementation barriers should be improved. Future Directions: To enhance data processing and security features, future work should focus on integrating edge computing, artificial intelligence, machine learning, and blockchain techniques. In addition, data visualizations and cost-effective solutions should also be in focus, to provide more adoptable IIoT systems in industrial domains. Conclusion: As IIoT systems and CO2 sensor technologies evolve, IIoT systems can contribute significantly to increasing global air quality and CO2 emission control in industry, agricultural, and urban areas.

关键词： Industrial Internet of Things (IIoT) CO2 Monitoring real-time data processing Carbon Footprint Monitoring Air Quality Security and Privacy in IIoT Emission Management Predictive Maintenance Edge Computing Machine Learning Blockchain Technology Smart Buildings Smart Cities Environmental Sustainability

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Architecture for Intensive Care data processing and Visualization in real-time

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Procedia Computer Science 2021年 184卷 923-928页

作者： Ricardo Cruz Tiago Guimarães Hugo Peixoto Manuel Filipe Santos Centro Algoritmi University of Minho Braga 4710 Portugal

Clinical data is growing every day. Ergo, to treat, store and publish such data is an emergent task. Furthermore, analysing data in real-time using streaming and processing technologies and methods, in order to obtain quality data, prepared to support decision making is of extreme value. Big data emerged with the introduction of real-time processing, thus revolutionizing traditional technologies and techniques through the ability to deal with the volume, speed and variety of data. Countless studies have been proposed in the healthcare domain in search of solutions that allow the flow of data in real-time. However, the work presented hereby is distinguished by allowing the collection, processing, storage and analysis of Intensive Care Units (ICU) data, both collected in real-time from bedside monitors but also stored in a historical repository. The architecture proposed makes use of current technologies, like Nextgen Connector as message supplier and integrator, Elasticsearch as a search index, Kibana for viewing stored data and Grafana for real-time streaming. This article is part of the ICDS4IM project - Intelligent Clinical Decision Support in Intensive Care Medicine to support the experimentation of data processing techniques and technologies, based in HL7 format and collected in real-time so that it can be made available through Health Information Systems across the healthcare institutions.

关键词： real-time data processing real-time data Visualization Decision Support Systems Big data

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Cloud-Based AI Robotics for Precision Pruning of Fruit Trees to Improve Sustainable Farming 3

Cloud-Based AI Robotics for Precision Pruning of Fruit Trees...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Ilambirai, R.C. Barkathulla, A. Mahalakshmi, G. Babu, J. Jagan Prabu, K. Srinivasan, C. SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The crucial agricultural practice of precision pruning fruit trees greatly impacts fruit production yield and quality. Traditional pruning techniques take a lot of time and money because they need skilled workers and are labor-intensive. It suggests a novel cloud-based AI robotics system intended to precisely prune fruit trees to address these issues. This system uses cutting-edge robotics fitted with Convolutional Neural Networks (CNNs) to carry out precise and effective tasks related to pruning. Cloud computing integration makes it possible to process and make real-time decisions, improving the system's flexibility in responding to changing tree conditions and pruning needs. The system's primary parts are robotic arms for accurate cuts, various sensors for analyzing tree structures, and CNN models for maximizing pruning techniques. Early field trials show noticeable gains in operational efficiency and pruning accuracy compared to conventional techniques. This study demonstrates how cloud-integrated AI robotics can transform agricultural practices and improve fruit farming's sustainability and productivity. © 2025 IEEE.

关键词： Agricultural Automation Fruit Trees real-time data processing Sensor Integration Sustainable Farming

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real-time processing of IoT Events with Historic data using Apache Kafka and Apache Spark with Dashing framework

Real-time Processing of IoT Events with Historic data using ...

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IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology

作者： Godson Michael D'silva Azharuddin Khan Mr. Gaurav Siddhesh Bari Information Technology Department St. John College of Engineering and Technology Palghar India Information Technology DepartmentSt. John College of Engineering and Technology Palghar India Joshi Information Technology DepartmentSt. John College of Engineering and Technology Palghar India

ISBN: (纸本)9781509037056

IoT (Internet of Things) is a concept that broadens the idea of connecting multiple devices to each other over the Internet and enabling communication between these devices. Traditionally, the packets are sent over the network for communication only if both, the sender as well as the receiver, are online. This forces the sender and the receiver to be online 24×7;which is not achievable in each and every environment the devices communicates in. Considering the humongous data generated in the communication, it is necessary to store and process this data so that data insights can be identified to improve the organizational benefits. This generated data can be in two forms, real-time as well as existing or historical data. When this data is obtained in realtime and it is processed, even traditional big data technologies do not perform up to the mark. Hence to process this real-time data, streaming of this data is required;which is not a feature of traditional big data technologies. To achieve these objectives, the proposed architecture uses open source technologies such as Apache Kafka, for online and offline consumption of messages, and Apache Spark, to stream, process and provide a structure to the real-time and existing data. A framework known as Dashing is used to present the processed data in a more attractive and readable manner.

关键词： IoT real-time data processing Apache Kafka Apache Spark Dashing Azure Hdinsight ISOCHRONOUS CYCLOTRONS real time data processing Receivers Transmitters big data Communication Research real-time process

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Enhancing Intelligent Transport Systems Through Decentralized Security Frameworks in Vehicle-to-Everything Networks

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WORLD ELECTRIC VEHICLE JOURNAL 2025年第1期16卷

作者： Tariq, Usman Ahanger, Tariq Ahamed Prince Sattam Bin Abdulaziz Univ Coll Business Adm Management Informat Syst Dept Al Kharj 16278 Saudi Arabia

Vehicle Ad hoc Networks (VANETs) play an essential role in intelligent transportation systems (ITSs) by improving road safety and traffic management through robust decentralized communication between vehicles and infrastructure. Yet, decentralization introduces security vulnerabilities, including spoofing, tampering, and denial-of-service attacks, which can compromise the reliability and safety of vehicular communications. Traditional centralized security mechanisms are often inadequate in providing the real-time response and scalability required by such dispersed networks. This research promotes a shift toward distributed and real-time technologies, including blockchain and secure multi-party computation, to enhance communication integrity and privacy, ultimately strengthening system resilience by eliminating single points of failure. A core aspect of this study is the novel D-CASBR framework, which integrates three essential components. First, it employs hybrid machine learning methods, such as ElasticNet and Gradient Boosting, to facilitate real-time anomaly detection, identifying unusual activities as they occur. Second, it utilizes a consortium blockchain to provide secure and transparent information exchange among authorized participants. Third, it implements a fog-enabled reputation system that uses distributed fog computing to effectively manage trust within the network. This comprehensive approach addresses latency issues found in conventional systems while significantly improving the reliability and efficacy of threat detection, achieving 95 percent anomaly detection accuracy with minimal false positives. The result is a substantial advancement in securing vehicular networks.

关键词： vehicular ad hoc networks (VANETs) decentralized intrusion detection systems (D-IDSs) interconnectivity privacy preserving hybrid machine learning fog computing minority class imbalance real-time data processing

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Fog Computing Challenges and Opportunities in IoT Networks: A Review

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Procedia Computer Science 2025年 259卷 1749-1764页

作者： Zanyar Rzgar Ahmed Shavan Askar Diana Hayder Hussein Media Ali Ibrahim Erbil Technical Administrative Institute Erbil Polytechnic University Erbil Iraq Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq

This review explores the crucial role of fog computing in addressing the increasing demands of the Internet of Things (IoT) and cloud environments, focusing on its ability to reduce latency, manage large data volumes, and optimize bandwidth by bringing computational resources closer to data sources. The paper highlights three key contributions: first, it discusses significant advancements in fog computing infrastructure, such as the development of scalable fog nodes and improved software solutions, which enhance deployment and management capabilities; second, it examines how fog computing is being integrated with related technologies like IoT, 5G, and blockchain to create efficient, secure, and decentralized systems; and third, it addresses the technical challenges associated with scalability, interoperability, and privacy, emphasizing the need for stronger security mechanisms, resource management algorithms, and standardized protocols. The review also looks ahead to future research directions, particularly the potential of artificial intelligence (AI) to optimize fog computing systems and the role of 5G networks in expanding their capabilities. Overall, the article provides a comprehensive overview of the state of fog computing, detailing its current advancements, ongoing challenges, and the opportunities it holds for transforming connected and data-driven systems in the future.

关键词： Fog Computing Internet of Things (IoT) Edge Computing Low-Latency Services Cloud Integration real-time data processing

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PyBIRALES: A Radar data processing Backend for the real-time Detection of Space Debris

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JOURNAL OF ASTRONOMICAL INSTRUMENTATION 2020年第1期9卷

作者： Cutajar, D. Magro, A. Borg, J. Adami, K. Z. Bianchi, G. Pupillo, G. Mattana, A. Naldi, G. Bortolotti, C. Perini, F. Lama, L. Schiaffino, M. Roma, M. Maccaferri, A. Di Lizia, P. Massari, M. Losacco, M. Univ Malta Inst Space Sci & Astron ISSA Msida Malta Univ Oxford Dept Phys Oxford OX1 3RH England Ist Radioastron Inst Nazl Astrofis Via P Gobetti 101 I-40129 Bologna Italy Politecn Milan Dept Aerosp Sci & Technol Via G La Masa 34 I-20156 Milan Italy

The growing population of artificial satellites in near-Earth orbit has made the monitoring of orbital debris objects ever more important. Orbital debris objects pose a threat to these satellites as their orbit cannot be changed in order to avoid a collision. In recent years, the European Space Agency (ESA)'s Space Surveillance and Tracking (SST) programme has been assisting national institutions in the upgrading of their space debris detection and monitoring capabilities. One of the latest such systems within this programme is the BIRALES space surveillance system based in Italy. The receiving antenna is a radio telescope that is made up of 32 receivers which are placed on eight parabolic cylindrical reflectors of the North-South arm of the Istituto Nazionale di Astrofisica (INAF)'s Northern Cross. This work introduces a new software backend which was developed for this novel space debris sensor. The system was designed to be a fast, highly configurable software backend for the radio telescope's acquisition and processing system and whose monitoring and control can be realized by a simple front-end web-based application. The real-time detection of Resident Space Object (RSO) is an important prerequisite for such a system as it gives the operator an immediate feedback loop on any detections whilst keeping the storage requirements at a minimum given that there is no need to save the raw data. The detection of high-velocity objects is achieved by means of a specially developed data processing pipeline that uses the received raw antenna voltages to generate a number of beams, collectively known as a multipixel, that cover the Field of View (FoV) of the instrument. The trajectory of the detected objects is determined by considering the illumination sequence within this multipixel. The initial results on known objects represent the first steps in extending the growing network of European SST systems.

关键词： Space situational awareness radar remote sensing real-time data processing

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Comparative Analysis of Hadoop and Spark Performance for real-time Big data Smart Platforms Utilizing IoT Technology in Electrical Facilities

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2024年第7期19卷 4595-4606页

作者： Gabdullin, Maratbek T. Suinullayev, Yerulan Kabi, Yelikbay Kang, Jeong Won Mukasheva, Assel Kazakh British Tech Univ Sch Mat Sci & Green Technol Alma Ata Kazakhstan Almaty Univ Power Engn & Telecommun Alma Ata Kazakhstan Korea Natl Univ Transportat Transportat Syst Engn Uiwang Si 16106 Gyeonggi Do South Korea Kazakh British Tech Univ Sch Informat Technol & Engn Alma Ata Kazakhstan

As the adoption of IoT technology in power systems accelerates and the need for improved methods to handle large volumes of data emerges, real-time big data smart platforms must address the growing data processing demands in IoT integrated power systems. Therefore, in this study, we assess the performance of Hadoop and Spark for iterative computing and real-time data processing applications. Our evaluation is based on metrics such as execution time, resource utilization, and scalability, particularly with increasing data volume. The comparison aims to provide guidance to researchers, practitioners and entrepreneurs on platform selection depending on their specific requirements. The study identified the strengths and weaknesses of both platforms and provided valuable insights into optimizing the performance of big data applications. Text documents and charts for Word Count and PageRank tasks were used for comparison, and performance testing was performed on datasets of different sizes. The results showed that Spark outperforms Hadoop in most applications, especially in iterative computation and real-time data processing, due to its use of in-memory computation. However, Hadoop is best suited for batch processing operations that require multiple steps. It can perform these operations in parallel across multiple cluster nodes, enabling fast processing of large amounts of data. This comprehensive performance comparison of Hadoop and Spark in iterative computing and real-time data processing applications provides valuable information for researchers, practitioners, and enterprises on the trade-offs and benefits of using these big data platforms.

关键词： IoT real-time data processing Hadoop Spark Performance evaluation

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