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Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning

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Data science and Management 2024年第3期7卷 189-196页

作者： Abhishek Bajpai Harshita Verma Anita Yadav Department of Computer Science Rajkiya Engineering CollegeKannauj209732India Department of Computer Science and Engineering School of EngineeringHarcourt Butler Technical UniversityKanpur208001India

The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and *** address the limitations imposed by inadequate resources,energy,and network scalability,this type of network relies heavily on data aggregation and clustering *** various conventional studies have aimed to enhance the lifespan of a network through robust systems,they do not always provide optimal efficiency for real-time *** paper presents an approach based on state-of-the-art machine-learning *** this study,we employed a novel approach that combines an extended version of principal component analysis(PCA)and a reinforcement learning algorithm to achieve efficient clustering and data *** primary objectives of this study are to enhance the service life of a network,reduce energy usage,and improve data aggregation *** evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop *** proposed approach(PQL)was compared to previous studies that utilized adaptive Q-learning(AQL)and regional energy-aware clustering(REAC).Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.

关键词： Wireless sensor network Principal component analysis(PCA) Reinforcement learning Data aggregation

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A Review of Intelligent Configuration and Its Security for Complex Networks

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Chinese Journal of Electronics 2024年第4期33卷 920-947页

作者： Yue ZHAO Bin YANG Fei TENG Xianhua NIU Ning HU Bo TIAN Science and Technology on Communication Security Laboratory School of Computer and Information Engineering Chuzhou University School of Computing and Artificial Intelligence Southwest Jiaotong University School of Computer and Software Engineering Xihua University Cyberspace Institute of Advanced Technology Guangzhou University

Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

关键词： Complex networks Intelligent configuration Configuration security Software defined network Lifecycle management

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Parsing Objects at a Finer Granularity: A Survey

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Machine Intelligence Research 2024年第3期21卷 431-451页

作者： Yifan Zhao Jia Li Yonghong Tian School of Computer Science Peking UniversityBeijing100871China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing100191China

Fine-grained visual parsing, including fine-grained part segmentation and fine-grained object recognition, has attracted considerable critical attention due to its importance in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms, while the inherent relations between these tasks are neglected. Moreover, given most of the research remains fragmented, we conduct an in-depth study of the advanced work from a new perspective of learning the part relationship. In this perspective, we first consolidate recent research and benchmark syntheses with new taxonomies. Based on this consolidation, we revisit the universal challenges in fine-grained part segmentation and recognition tasks and propose new solutions by part relationship learning for these important challenges. Furthermore, we conclude several promising lines of research in fine-grained visual parsing for future research.

关键词： Finer granularity visual parsing part segmentation fine-grained object recognition part relationship

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Bi-directional Projection Framework for Fast Single Image Super Resolution

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Recent Patents on engineering 2024年第9期18卷 152-167页

作者： Zhou, Ying Zheng, Zhichao Sun, Quansen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer and Electronic Information/School of Artificial Intelligence Nanjing Normal University Nanjing China

Background: Collaborative Representation (CR) has been widely used in Single Image Super Resolution (SISR) with the assumption that Low-resolution (LR) and high-resolution (HR) features can be linearly represented by neighborhoods and share consistent CR coefficients. Numerous patents and journal papers have been published. However, this CR consistency does not hold in the reconstruction phase, which leads to degraded performance. Methods: To fulfill this gap, we propose a novel bi-directional projection model (BDPM) to establish a bi-directional mapping between LR and HR features without any consistency constraint. The multiple projection matrices are offline computed to reduce reconstruction time greatly. We further develop several strategies to extract features and group neighborhoods such that local structures can be preserved better. Results: Compared to the learning-based methods, BDPM is about 2 to 10 times faster and compared to the reconstruction-based methods, it is about 500 to 2,000 times faster. Conclusion: The empirical studies verify the effectiveness of BDPM and extensive experimental results demonstrate that BDPM achieves better SISR performance than many state-of-the-arts. © 2024 Bentham science Publishers.

关键词： Mapping

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Retrieval-and-alignment based large-scale indoor point cloud semantic segmentation

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science China(Information sciences) 2024年第4期67卷 164-180页

作者： Zongyi XU Xiaoshui HUANG Bo YUAN Yangfu WANG Qianni ZHANG Weisheng LI Xinbo GAO School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Institute for Brain and Intelligence Guangyang Bay Laboratory Shanghai Artificial Intelligence Laboratory School of Electronic Engineering and Computer Science Queen Mary University of London

Current methods for point cloud semantic segmentation depend on the extraction of descriptive features. However, unlike images, point clouds are irregular and often lack texture information, making it demanding to extract discriminative features. In addition, noise, outliers, and uneven point distribution are commonly present in point clouds, which further complicates the segmentation task. To address these problems, a novel architecture is proposed for direct and accurate large-scale point cloud segmentation based on point cloud retrieval and alignment. The proposed approach involves using a feature-based point cloud retrieval method for searching for reference point clouds with annotations from a dataset. In the following segmentation stage, an overlap-based point cloud registration method has been developed to align the target and reference point clouds. For accurate and robust alignment, an overlap region estimation module is trained to locate the optimal overlap region between two pieces of point clouds in a coarse-to-fine manner. In the detected overlap region, the global and local features of the points are extracted and combined for featuremetric registration to obtain accurate transformation parameters between the target and reference point clouds. After alignment, the annotated segmentation of the reference is transferred to the target point clouds to obtain accurate segmentation results. Extensive experiments are conducted to show that the developed method outperforms the state-of-the-art approaches in terms of both accuracy and robustness against noise and outliers.

关键词： point cloud semantic segmentation large-scale indoor point clouds point cloud alignment overlap estimation label transfer

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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The Minimum Moving Spanning Tree Problem

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Journal of Graph Algorithms and Applications 2023年第1期27卷 1-18页

作者： Akitaya, Hugo A. Biniaz, Ahmad Bose, Prosenjit De Carufel, Jean-Lou Maheshwari, Anil da Silveira, Luís Fernando Schultz Xavier Smid, Michiel Department of Computer Science University of Massachusetts Lowell United States School of Computer Science University of Windsor Canada School of Computer Science Carleton University Canada School of Electrical Engineering and Computer Science University of Ottawa Canada

We investigate the problem of finding a spanning tree of a set of n moving points in Rdim that minimizes the maximum total weight (under any convex distance function) or the maximum bottleneck throughout the motion. The output is a single tree, i.e., it does not change combinatorially during the movement of the points. We call these trees a minimum moving spanning tree, and a minimum bottleneck moving spanning tree, respectively. We show that, although finding the minimum bottleneck moving spanning tree can be done in O(n2) time when dim is a constant, it is NP-hard to compute the minimum moving spanning tree even for dim = 2. We provide a simple O(n2)-time 2-approximation and a O(n log n)-time (2 + Ε)-approximation for the latter problem, for any constant dim and any constant Ε > 0. © 2023, Brown University. All rights reserved.

关键词： Trees (mathematics)

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Revealing the Trends in the Academic Landscape of the Health Care System Using Contextual Topic Modeling

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Data Intelligence 2023年第4期5卷 923-946页

作者： Muhammad Inaam ul haq Qianmu Li School of computer science and Engineering Nanjing University of Science and Technology

The health care system encompasses the participation of individuals,groups,agencies,and resources that offer services to address the requirements of the person,community,and population in terms of *** to the rising debates on the healthcare systems in relation to diseases,treatments,interventions,medication,and clinical practice guidelines,the world is currently discussing the healthcare industry,technology perspectives,and healthcare *** gain a comprehensive understanding of the healthcare systems research paradigm,we offered a novel contextual topic modeling approach that links up the CombinedTM model with our healthcare Bert to discover the contextual topics in the domain of *** research work discovered 60 contextual topics among them fteen topics are the hottest which include smart medical monitoring systems,causes,and effects of stress and anxiety,and healthcare cost estimation and twelve topics are the ***,thirty-three topics are showing in-significant *** further investigated various clusters and correlations among the topics exploring inter-topic distance maps which add depth to the understanding of the research structure of this scientific *** current study enhances the prior topic modeling methodologies that examine the healthcare literature from a particular disciplinary *** further extends the existing topic modeling approaches that do not incorporate contextual information in the topic discovery process adding contextual information by creating sentence embedding vectors through transformers-based *** also utilized corpus tuning,the mean pooling technique,and the hugging face *** method gives a higher coherence score as compared to the state-of-the-art models(LSA,LDA,and Ber Topic).

关键词： Contextual topic modeling health care Bert content analysis health care system

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Resilient TCP Variant Enabling Smooth Network Updates for Software-Defined Data Center Networks

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Tsinghua science and Technology 2024年第5期29卷 1615-1632页

作者： Abdul Basit Dogar Sami Ullah Yiran Zhang Hisham Alasmary Muhammad Waqas Sheng Chen Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China and also with Department of Informatics and SystemsUniversity of Management and TechnologyLahore 54660Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir 18050Pakistan School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha 61421Saudi Arabia Department of Computer Engineering Faculty of Information TechnologyUniversity of BahrainSakheer 32038Bahrainand also with School of EngineeringEdith Cowan UniversityPerth WA 6027Australia School of Electronics and Computer Science University of SouthamptonSouthamptonSO171BJUK

Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data *** TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient to network updates that provoke flow *** this paper,we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates,because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance *** look into the causes and propose a network update-friendly TCP(NUFTCP),which is an extension of the DCTCP variant,as a *** are used to assess the proposed *** findings reveal that NUFTCP can more effectively manage the problems of out-of-order packets and packet drops triggered in network updates,and it outperforms DCTCP considerably.

关键词： software defined data center networks network updates DCTCP out-of-order packets packet drop SDN

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Sum rate optimizing for multi-IRS-assisted UAV downlink transmission system using deep reinforcement learning

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The Journal of China Universities of Posts and Telecommunications 2024年第5期31卷 23-33页

作者： Long Yuan He Xiaoli Ye Yang Zhang Bo School of Computer Science and Engineering Sichuan University of Science and EngineeringZigong 643002China School of Resources Sichuan Agricultural UniversityYa’an 625014China

By leveraging the high maneuverability of the unmanned aerial vehicle(UAV) and the expansive coverage of the intelligent reflecting surface(IRS), a multi-IRS-assisted UAV communication system aimed at maximizing the sum data rate of all users was investigated in this paper. This is achieved through the joint optimization of the trajectory and transmit beamforming of the UAV, as well as the passive phase shift of the IRS. Nevertheless, the initial problem exhibits a high degree of non-convexity, posing challenges for conventional mathematical optimization techniques in delivering solutions that are both quick and efficient while maintaining low complexity. To address this issue, a novel scheme called the deep reinforcement learning(DRL)-based enhanced cooperative reflection network(DCRN) was proposed. This scheme effectively identifies optimal strategies, with the long short-term memory(LSTM) network improving algorithm convergence by extracting hidden state information. Simulation results demonstrate that the proposed scheme outperforms the baseline scheme, manifesting substantial enhancements in sum rate and superior performance.

关键词： intelligent reflecting surface(IRS) unmanned aerial vehicle(UAV)communication deep reinforcement learning(DRL) trajectory optimization

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