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20th International Conference on Service-Oriented Computing (ICSOC)

作者： Tao, Jiachun Pan, Zhicheng Fang, Junhua Chao, Pingfu Zhao, Pengpeng Xu, Jiajie Soochow Univ Dept Comp Sci & Technol Suzhou Peoples R China

ISBN: (纸本)9783031209833;9783031209840

As a fundamental operation in various LBS (Location Based Service) applications, the trajectory similarity search has long been a performance bottleneck in applications like (e.g., traffic optimization and contact tracing). When handling streaming trajectory data, the variable workload and stateful compute requirement are two crucial challenges that further complicate the problem. Distributed microservice, a mainstream industrial software design architecture, is the preferred way to address such issues. However, the trajectory instance will inevitably be split under the parallel framework. Therefore, how to distribute trajectory data among the parallel processing tasks in a real-time and lightweight manner is the crux. In this paper, we propose a Microservice-based real-time processing framework for streaming trajectory similarity search, called Misty, which effectively reduces the update cost of the secondary index and supports high scalability. Moreover, on top of Misty, we can build resilient and stateful cloud-native applications. Misty is composed of the assembler, index, coordinator, and executor. Specifically, the assembler and the index module ensure retrieval performance, while the coordinator and executor module enable the system with elastic scaling. Extensive experimental studies on real-world data demonstrate higher query throughput and lower latency over traditional approaches.

关键词： real-time data processing Trajectory similarity Microservice Distributed processing Streaming spatio-temporal data

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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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Terabit sampling system with photonic time-stretch analog-to-digital converter 7

Terabit sampling system with photonic time-stretch analog-to...

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Conference on real-time Measurements, Rogue Phenomena, and Single-Shot Applications VII at SPIE LASE Conference

作者： Caselle, M. Bielawski, S. Manzhura, O. Chilingaryan, S. Dritschler, T. Ebersoldt, A. Kopmann, A. Nasse, M. J. Patil, M. M. Brundermann, E. Roussel, E. Szwaj, C. Muller, A-S Karlsruhe Inst Technol Hermann von Helmholtz Pl 1 Eggenstein Leopoldshafen Germany Univ Lille Ctr Etud Rech & Applicat CERLA CNRS UMR 852PhLAMPhys Lasers Atomes & Mol F-59000 Lille France

ISBN: (纸本)9781510648449;9781510648432

The detection of rapid dynamics in diverse physical systems is traditionally very difficult and strongly dominated by several noise contributions. Laser mode-locking, electron bunches in accelerators and optical-triggered phases in materials are events that carry important information about the system from which they emerge. To understand the underlying dynamics of complex systems often large numbers of single-shot measurements must be acquired continuously over a long time with extremely high temporal resolution. Ultrafast real-time instruments allow the acquisition of large data sets, even for rare events, in a relatively short time period. The real-time measurement of fast single-shot events with large record lengths is one of the most challenging problems in the fields of instrumentation and measurement. In this contribution, the novel ultra-fast and continuous data sampling system THERESA using photonic time-stretch is presented and its performance is discussed. The proposed data acquisition system is based on the latest ZYNQ Radio Frequency System on Chip (ZYNQ-RFSoC) family from Xilinx, which combines an array of fast (GS/s) multi-channel Analog-to-Digital Converters (ADCs) with a Field Programmable Gate Array (FPGA) and a multi-core ARM processor in a single heterogeneous programmable device. The stretched pulse is sampled in parallel by 16 wideband sampling channels operating in time-interleaving mode. The sampled data is transferred by a 100Gb Ethernet data link to the data Acquisition (DAQ) compute node for further analysis. The combination of both, the photonic time-stretch and the fast sampling system, is capable of sampling short pulses with femtosecond time resolution. Applications of the new system, hardware implementation and the commissioning of the first system for the electron bunch diagnostics are presented.

关键词： Photonic time-stretch femtosecond time resolution mode-locked lasers characterization electron bunches in accelerators single shot measurement ZYNQ-RFSoC time-interleaving ADC 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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Decision-Making Model for Securing IoT Devices in Smart Industries

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第6期17卷 4270-4278页

作者： Rathee, Geetanjali Garg, Sahil Kaddoum, Georges Choi, Bong Jun Jaypee Univ Informat Technol Dept Comp Sci & Engn Waknaghat 173234 India Univ Quebec Elect Engn Dept Ecole Technol Super Montreal PQ H3C 1K3 Canada Soongsil Univ Sch Comp Sci & Engn Seoul 06978 South Korea

The industrial Internet-of-Things (IIoT) is a powerful Internet of Things (IoT) application that enables industrial growth by ensuring transparent communication among the various entities of a company such as the manufacturing locations, design hubs, and packaging units. However, current industrial architectures are unable to efficiently deal with advanced security issues that come with this communication due to the distributed and expandable nature of IIoT networks. Furthermore, from a security perspective, malicious devices with the objective of modifying data from within the premises of the network pose a high risk for the IIoT. Therefore, introducing intelligent decision-making models to the IIoT can enhance our ability to examine any collected data in a more structured, efficient, and secure manner. In this article, we provide a decision-making model for securing IIoT data. The proposed model, based on the Technique for Order Preference by Similarity to the Ideal Solution, can provide secure information transmission and recording/storage using various communicating parameters. The degree of trust of the IoT devices is analyzed using these parameters. Simple additive weighting is integrated into the proposed model to remove inefficient and ill-structured parameters. The proposed model is validated using various spectrum sensing and security parameters against a baseline method for the IIoT. Simulation results show that the proposed model is approximately 85% more efficient in identifying malicious nodes and denial-of-service threats compared to the baseline method.

关键词： Decision making Security Monitoring Surface acoustic waves Industries Configuration security decision-making efficient computing industrial Internet-of-Things (IIoT) intelligent forensics real-time data processing simple additive weighting (SAW) Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS)

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Modeling synthetic power distribution network and datasets with industrial validation

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JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2023年 31卷

作者： Ali, M. Prakash, K. Macana, C. Raza, M. Q. Bashir, A. K. Pota, H. Univ New South Wales Sch Engn & Informat Technol Canberra Australia Univ Queensland Sch Elect Engn Power & Energy Syst St Lucia Australia Manchester Metropolitan Univ Dept Comp & Math Manchester England

Creating synthetic networks and datasets for power distribution network is challenging due to continuous expansion of networks, integration of new low carbon technologies and large penetration of renewable energy resources in network. In this paper, a practical approach for generating synthetic distribution networks and datasets using public databases and data synthesis algorithms is proposed. A synthetic power distribution network is developed by leveraging the open-data from local government databases, OpenStreetMaps and mapping engines such as Google Street View. New data synthesis algorithms are proposed to obtain the missing network datasets. The proposed algorithms include a topology for designing power lines, a method for computing the lengths of power lines, a hub-line algorithm for determining the number of consumers connected to a single transformer, a virtual layer approach based on FromNode and ToNode for establishing electrical connectivity, and a technique for ingesting raw data into industrial data platforms. The practical feasibility of the proposed solutions is demonstrated by an illustrative case study of the Colac region in Australia. Synthetic network and datasets are created for the distribution feeder, and then evaluated in industry servers. The results are compared using a three-step validation procedure: comparing the synthetic and actual network datasets using geo-based visualizations, by including feedback from industry experts familiar with the analysis, and by testing the generic applicability of the proposed techniques to other regions. The paper compares network elements that include 4714 power lines, 48 distribution transformers, 4155 energy consumers, 609 electrical nodes, and 1 substation. The comparison results demonstrate the efficacy of developed networks and datasets as they show resemblance to real network and datasets while providing the geographical validation of distribution network models.

关键词： data analytics Industrial validation Power distribution networks real-time data processing Synthetic networks Validation case study

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Review: Fundamentals, limitations and pitfalls on the development and application of precision nutrition techniques for precision livestock farming

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ANIMAL 2023年第Sup2期17卷 100763页

作者： Pomar, Candido Remus, Aline Agr & Agri Food Canada Sherbrooke Res & Dev Ctr 2000 Coll St Sherbrooke PQ J1M 0C8 Canada

Precision livestock farming (PLF) concerns the management of livestock using the principles and technologies of process engineering. Precision nutrition (PN) is part of the PLF approach and involves the use of feeding techniques that allow the proper amount of feed with the suitable composition to be supplied in a timely manner to individual animals or groups of animals. Automatic data collection, data processing, and control actions are required activities for PN applications. Despite the benefits that PN offers to producers, few systems have been successfully implemented so far. Besides the economical and logistical challenges, there are conceptual limitations and pitfalls that threaten the widespread adoption of PN. Developers have to avoid the temptation of looking for the application of available sensors and instead concentrate on identifying the most appropriate and relevant information needed for the optimal functioning of PN applications. Efficient PN applications are obtained by controlling the nutrient requirement variations occurring between animals and over time. The utilization of feedback control algorithms for the automatic determination of optimal nutrient supply is not recommended. Mathematical models are the preferred data processing method for PN, but these models have to be designed to operate in real time using up-to-date information. These models are therefore structurally different than traditional nutrition or growth models. Combining knowledge- and data-driven models using machine learning and deep learning algorithms will enhance our ability to use real-time farm data, thus opening up new opportunities for PN. To facilitate the implementation of PN in farms, different experts and stakeholders should be involved in the development of the fully integrated and automatic PLF system. Precision livestock farming and PN should not be seen as just being a question of technology, but a successful marriage between knowledge and technology. (c) 2023 P

关键词： Automatic data collection Control actions Nutrition Precision feeding real-time data processing

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A convenient platform for real-time non-contact thermal measurement and processing

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BULLETIN OF THE KOREAN CHEMICAL SOCIETY 2022年第6期43卷 854-858页

作者： Choi, Hyosung Soeriawidjaja, Banyu Firdaus Lee, Sang Hak Kwak, Minseok Pukyong Natl Univ Dept Chem Busan 48513 South Korea Pukyong Natl Univ Dept Ind Convergence Bion Engn 4 0 Busan 48513 South Korea Pusan Natl Univ Dept Chem Busan 46241 South Korea

Temperature is one of crucial and integral parts of chemical experiments. Since the properties or shape of the material may change depending on the temperature, the device that measures it plays a vital role in an experiment. Such devices commonly used in experiments to this day are unable to perform temperature measurement and data processing simultaneously. Here, we present a low-price, non-contact convenient platform capable of solving both problems at the same time, with automatically set different measurement time intervals for each experiment, and shows processed data anywhere real-time on the Internet. This device can operate within temperature range of -20 and 80 degrees C with a respectable error range of +/- 2.5 degrees C.

关键词： Arduino real-time data processing temperature measurement thermal sensor

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Wide-angle visible video diagnostics for JT-60SA utilizing EDICAM

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FUSION ENGINEERING AND DESIGN 2020年 153卷 111505-111505页

作者： Szepesi, Tamas Davis, Sam Hajnal, Nandor Kamiya, Kensaku Kocsis, Gabor Kovacsik, Akos Oyama, Naoyuki Sozzi, Carlo Zoletnik, Sandor Wigner RCP Budapest Hungary Fus Energy Garching Germany QST Naka Ibaraki Japan BME Inst Nucl Tech Budapest Hungary CNR Ist Fis Plasma Milan Italy

A multi-purpose, wide-angle, visible overview video diagnostic, based on the EDICAM camera, was designed for JT-60SA superconducting tokamak, in order to fulfill both machine protection and scientific observation purposes. The detector is located inside the 'port plug' (a ca. 3 m long re-entrant tube), relatively close to the plasma boundary;the EDICAM can tolerate the magnetic field at this location as well as the radiation levels in the early phases of the experiment. A rail-and-carriage system is used to move the detector between the opening of the port plug and the measurement location. A 4-point docking system ensures the precise and robust positioning of the optics, firmly holding the detector also in the event of disruptions or earthquakes. The camera is running under atmospheric condition, separated by a sapphire window from the plasma vacuum. The vacuum window is protected by a water-cooled pin-hole from mechanical impacts and plasma heat;the pin-hole is also the first element of the detector's optical system. The optics consists of two lens groups and a prism;the housing is made of stainless steel, in order to reduce eddy currents and thus avoid large forces acting on the system in a disruption.

关键词： JT-60SA EDICAM Video diagnostics real-time data processing Machine protection

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Lifelong Machine Learning and root cause analysis for large-scale cancer patient data

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JOURNAL OF BIG data 2019年第1期6卷 1页

作者： Pal, Gautam Hong, Xianbin Wang, Zhuo Wu, Hongyi Li, Gangmin Atkinson, Katie Dept Comp Sci Liverpool Merseyside England Xian Jiaotong Liverpool Univ Res Inst Big Data Analyt Suzhou Peoples R China

IntroductionThis paper presents a lifelong learning framework which constantly adapts with changing data patterns over time through incremental learning approach. In many big data systems, iterative re-training high dimensional data from scratch is computationally infeasible since constant data stream ingestion on top of a historical data pool increases the training time exponentially. Therefore, the need arises on how to retain past learning and fast update the model incrementally based on the new data. Also, the current machine learning approaches do the model prediction without providing a comprehensive root cause analysis. To resolve these limitations, our framework lays foundations on an ensemble process between stream data with historical batch data for an incremental lifelong learning (LML) *** descriptionA cancer patient's pathological tests like blood, DNA, urine or tissue analysis provide a unique signature based on the DNA combinations. Our analysis allows personalized and targeted medications and achieves a therapeutic response. Model is evaluated through data from The National Cancer Institute's Genomic data Commons unified data repository. The aim is to prescribe personalized medicine based on the thousands of genotype and phenotype parameters for each *** and evaluationThe model uses a dimension reduction method to reduce training time at an online sliding window setting. We identify the Gleason score as a determining factor for cancer possibility and substantiate our claim through Lilliefors and Kolmogorov-Smirnov test. We present clustering and Random Decision Forest results. The model's prediction accuracy is compared with standard machine learning algorithms for numeric and categorical *** propose an ensemble framework of stream and batch data for incremental lifelong learning. The framework successively applies first streaming clustering technique and then Random Decision Forest Regressor/Classifier to isolat

关键词： Lifelong learning real-time data processing Lambda Architecture Streaming k-means Random Decision Forest Dimension reduction

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