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LoS Coverage Analysis for UAV-Based THz Communication Networks: Toward 3-D Visualization of Wireless Networks

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第6期60卷 8726-8743页

作者： Dabiri, Mohammad Taghi Hasna, Mazen Althunibat, Saud Qaraqe, Khalid Qatar Univ Dept Elect Engn Doha 2713 Qatar Al Hussein Bin Talal Univ Dept Commun Engn Maan 71111 Jordan Texas A&M Univ Qatar Dept Electr & Comp Engn Doha Qatar

Terahertz (THz) links require a line-of-sight (LoS) connection, which is hard to be obtained in most scenarios. For THz communications, analyses based on LoS probability are not accurate, and a real LoS model should be considered to determine the LoS status of the link in a real 3-D environment. Considering unmanned aerial vehicle (UAV)-based THz networks, LoS coverage is analyzed in this work, where nodes are accurately determined to be in LoS or not. Specifically, by modeling an environment with 3-D blocks, our target is to locate a set of UAVs equipped with directional THz links to provide LoS connectivity for the distributed users among the 3-D obstacles. To this end, we first characterize and model the environment with 3-D blocks. Then, we propose a user-friendly algorithm based on 3-D spatial vectors, which determines the LoS status of nodes in the target area. In addition, using 3-D modeling, several metaheuristic algorithms are proposed for UAVs' positioning under 3-D blocks in order to maximize the LoS coverage percentage. In the second part of this article, for a UAV-based THz communication network, a geometrical-analysis-based algorithm is proposed, which jointly clusters the distributed nodes and locates the set of UAVs to maximize average network capacity while ensuring the LoS state of distributed nodes among 3-D obstacles. Moreover, we also propose a suboptimal hybrid k-means-geometrical-based algorithm with a low computational complexity that can be used for networks where the topology continuously changes, and thus, users' clustering and UAVs' positioning need to be regularly updated. Finally, by providing various 3-D simulations, we evaluate the effect of various system parameters, such as the number and heights of UAVs, as well as the density and height of 3-D obstacles on the LoS coverage.

关键词： Three-dimensional displays Solid modeling Terahertz communications Autonomous aerial vehicles Buildings clustering algorithms Analytical models clustering geometrical analysis line-of-sight (LoS) coverage terahertz (THz) communication unmanned aerial vehicle (UAV) positioning 3-D visualization

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A Novel Mean-Shift Algorithm for Data clustering

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IEEE ACCESS 2022年 10卷 14575-14585页

作者： Cariou, Claude Le Moan, Steven Chehdi, Kacem IETR CNRS UMR 6164 Univ Rennes 1 F-22300 Lannion France Norwegian Univ Sci & Technol Dept Comp Sci N-2815 Gjovik Norway

We propose a novel Mean-Shift method for data clustering, called Robust Mean-Shift (RMS). A new update equation for point iterates is proposed, mixing the ones of the standard Mean-Shift (MS) and the Blurring Mean-Shift (BMS). Despite its simplicity, the proposed method has not been studied so far. RMS can be set up in both a kernel-based and a nearest-neighbor (NN)-based fashion. Since the update rule of RMS is closer to BMS, the convergence of point iterates is conjectured based on the Chen's BMS convergence theorem. Experimental results on synthetic and real datasets show that RMS in several cases outperforms MS and BMS in the clustering task. In addition, RMS exhibits larger attraction basins than MS and BMS for identical parametrization;consequently, its kernel variant requires a lower aperture of the kernel function, and its NN variant a lower number of nearest neighbors compared to MS or BMS, to achieve optimal clustering results. In addition, the NN version of RMS does not need to specify a convergence threshold to stop the iterations, contrarily to the NN-BMS algorithm.

关键词： Convergence Kernel Bandwidth clustering algorithms Task analysis Standards Sensitivity Data mining data clustering mean-shift kernel-based method nearest neighbors

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Mining Stable Communities in Temporal Networks by Density-Based clustering

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IEEE TRANSACTIONS ON BIG DATA 2022年第3期8卷 671-684页

作者： Qin, Hongchao Li, Rong-Hua Wang, Guoren Huang, Xin Yuan, Ye Yu, Jeffrey Xu Northeastern Univ Dept Comp Sci Shenyang 110004 Liaoning Peoples R China Beijing Inst Technol Dept Comp Sci Beijing 100081 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China

Community detection is a fundamental task in graph data mining. Most existing studies in contact networks, collaboration networks, and social networks do not utilize the temporal information associated with edges for community detection. In this article, we study a problem of finding stable communities in a temporal network, where each edge is associated with a timestamp. Our goal is to identify the communities in a temporal network that are stable over time. To efficiently find the stable communities, we develop a new community detection algorithm based on the density-based graph clustering framework. We also propose several carefully-designed pruning techniques to significantly speed up the proposed algorithm. We conduct extensive experiments on four real-life temporal networks to evaluate our algorithm. The results demonstrate the effectiveness and efficiency of the proposed algorithm.

关键词： clustering algorithms Big Data Collaboration Image edge detection Detection algorithms Computer science Data mining Community detection temporal networks stable community structural graph clustering density-based clustering

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On Hierarchical Multi-UAV Dubins Traveling Salesman Problem Paths in a Complex Obstacle Environment

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第1期54卷 123-135页

作者： Fu, Jinyu Sun, Guanghui Liu, Jianxing Yao, Weiran Wu, Ligang Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China

This article aims to solve a hierarchical multi-UAV Dubins traveling salesman problem (HMDTSP). Optimal hierarchical coverage and multi-UAV collaboration are achieved by the proposed approaches in a 3-D complex obstacle environment. A multi-UAV multilayer projection clustering (MMPC) algorithm is presented to reduce the cumulative distance from multilayer targets to corresponding cluster centers. A straight-line flight judgment (SFJ) was developed to reduce the calculation of obstacle avoidance. An improved adaptive window probabilistic roadmap (AWPRM) algorithm is addressed to plan obstacle-avoidance paths. The AWPRM improves the feasibility of finding the optimal sequence based on the proposed SFJ compared with a traditional probabilistic roadmap. To solve the solution to TSP with obstacles constraints, the proposed sequencing-bundling-bridging (SBB) framework combines the bundling ant colony system (BACS) and homotopic AWPRM. An obstacle-avoidance optimal curved path is constructed with a turning radius constraint based on the Dubins method and followed up by solving the TSP sequence. The results of simulation experiments indicated that the proposed strategies can provide a set of feasible solutions for HMDTSPs in a complex obstacle environment.

关键词： Task analysis Turning Heuristic algorithms Collision avoidance clustering algorithms Nonhomogeneous media Vehicle dynamics Bundling ant colony system (BACS) hierarchical multi-UAV Dubins traveling salesman problem (HMDTSP) homotopic adaptive window probabilistic roadmap (AWPRM) obstacle avoidance

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Survey on Clustered Routing Protocols Adaptivity for Fire Incidents: Architecture Challenges, Data Losing, and Recommended Solutions

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IEEE ACCESS 2024年 12卷 113518-113552页

作者： Abbas, Ola Khudhair Abdullah, Fairuz Radzi, Nurul Asyikin Mohamed Salman, Aymen Dawood Abdulkadir, Said Jadid Univ Tenaga Nas Inst Power Engn Kajang 43000 Selangor Malaysia Iraqi Minist Educ Gen Directorate Educ Misan Governorate Misan 10045 Iraq Univ Technol Iraq Dept Comp Engn Baghdad 10066 Iraq Univ Teknol PETRONAS Dept Comp & Informat Sci Seri Iskandar 32610 Malaysia Univ Teknol PETRONAS Ctr Res Data Sci Seri Iskandar 32610 Malaysia

Wireless Sensor Networks (WSNs) significantly impact reliable data communications in environmental monitoring, health care, and transportation applications. Low-powered and small sensor nodes compose these networks, configured to perform specific tasks like detection, management, prediction, and monitoring. clustering in WSNs is a reliable method of effectively transferring data within the network through routing. Thus, developing an effective routing algorithm to optimize network functionality is a big concern. Cluster Heads (CHs), selected through a range of metrics, are responsible for data aggregation and transmission to the Base Station (BS). Many clustered routing algorithms have been developed to address various issues like energy efficiency, network lifetime, and hotspot problems. However, several challenges still need to be addressed, including network segmentation, isolated node issues, and data routing failures. This survey offers a comprehensive review of various routing protocols and their different performance metrics. It evaluates the architectural challenges that caused network segmentations and data routing failures in the case of unexpected head node failures during high-stress events, particularly indoor fires. Recommended solutions to the mentioned issues are also explored. A new taxonomy for the CH-selection methods based on the technique used is proposed, along with a comparative review of other CH-selection surveys. Additionally, this survey scrutinizes the challenges and constraints inherent in current fault tolerance routing algorithms, evaluating their efficacy in fire-related incidents. The survey concluded that protocol designers must focus on the development and implementation of fire-adaptive routing protocols, incorporating optimal fault tolerance routing algorithms that adapt seamlessly to unforeseen environmental changes, including fire incidents. Such adaptability is pivotal in preserving network topology and preventing data loss.

关键词： Routing protocols Wireless sensor networks Surveys clustering algorithms Scalability Hierarchical systems Fault tolerance Environmental monitoring Wireless sensor network routing protocols hierarchical clustering network segmentation data routing failure architecture challenges fault tolerance

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TSTFNN: Performance Enhancement for Fuzzy Neural Network in Performance Monitoring of Industrial Flotation Processes

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第3期20卷 4919-4929页

作者： Xie, Shiwen Xie, Yongfang Huang, Tingwen Cent South Univ Sch Automat Changsha 410083 Peoples R China Texas A&M Univ Qatar Doha 23874 Qatar

Numerous studies on learning algorithms have been done in the neural networks community, however concerning to training strategy is limited. In this article, to enhance the performance of fuzzy neural network (FNN) in industrial process identification, an FNN based on two-stage training (TSTFNN) is proposed. First, to initialize TSTFNN, we propose a supervised clustering method. It determines the initial centers and number of neurons for TSTFNN through clustering part of samples based on their labels. Then, the gradient-based algorithm is employed to further learning the parameters of TSTFNN. To improve its generalization performance, we developed a two-stage training scheme. In the first stage, a standard 10-fold cross-validation is executed to learn parameters. The samples whose prediction errors exceed a preset threshold are utilized to train the network in the second stage. According to Lyapunov criterion, we show the convergence of TSTFNN if the learning rate is selected in an appropriate range. The effectiveness of TSTFNN is validated by simulations on two benchmark problems. The proposed TSTFNN performs better and outperforms the state-of-the-art. Compared with supervised long short-term memory, root mean square error is decreased by 17%. Then, we apply it to the industrial rougher flotation process for soft sensor modeling. The experimental results show that TSTFNN achieves a satisfactory prediction performance.

关键词： Fuzzy neural networks Training Convergence Neurons clustering algorithms Heuristic algorithms Input variables Fuzzy neural network (FNN) industrial flotation process performance monitoring soft sensor two-stage training

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An Integrated Modeling Strategy for Wind Power Forecasting Based on Dynamic Meteorological Visualization

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IEEE ACCESS 2024年 12卷 69423-69433页

作者： Zhang, Junnan Fu, Hua Liaoning Tech Univ Fac Elect & Control Engn Huludao 125105 Peoples R China

The escalating incorporation of wind energy into power grids imposes constraints on the effective operation and control of power systems. Effective short-term wind power forecasting technology is essential for safe power supply and stable operation of the power grid. Thus, a UMAP-IVMD-ILSTM model for wind power forecasting is proposed based on the uniform manifold approximation and projection (UMAP) integrated improved variational mode decomposition (IVMD) and multi-level improved long and short-term memory (LSTM) networks. First, the UMAP approach was utilized to reduce the dimension of related meteorological indicators, and k-means clustering was applied to classify the weather categories after dimension reduction. Subsequently, through the improved variational mode decomposition, the wind power of the adjacent day is decomposed. Moreover, long and short-term memory networks were optimized using the sparrow search algorithm (SSA) as an improved LSTM (ILSTM), and a wind power forecasting model with multi-level LSTM was established for each mode, and the forecasting results of each mode were integrated. Through actual wind farm operation data, it was proven that the method proposed in this study is effective in processing high-dimensional numerical weather prediction (NWP) data. The model can better preserve the information of the original data and reduce the complexity of the original data sequence based on visualization. Compared with the comparative models, the proposed model reduces the RMSE and MAE by up to 18.2% and 21.6% respectively, and has the least running time, which can effectively improve the accuracy and efficiency of wind power prediction.

关键词： Wind power generation Forecasting Predictive models Long short term memory clustering algorithms Wind forecasting Adaptation models Nonlinear systems Weather forecasting Wind power forecasting dynamic weather identification nonlinear dimension reduction long and short-term memory network variational mode decomposition

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Multi-View and Multi-Order Structured Graph Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第10期35卷 14437-14448页

作者： Wang, Rong Wang, Penglei Wu, Danyang Sun, Zhensheng Nie, Feiping Li, Xuelong Northwestern Polytech Univ Sch Artificial Intelligence OPt & Elect IOPEN Minist Ind & Informat Technol Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence OPt & Elect IOPEN Sch Comp Sci Minist Ind & Informat Technol Xian 710072 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Rocket Force Univ Engn Zhijian Lab Xian 710025 Peoples R China

Recently, graph-based multi-view clustering (GMC) has attracted extensive attention from researchers, in which multi-view clustering based on structured graph learning (SGL) can be considered as one of the most interesting branches, achieving promising performance. However, most of the existing SGL methods suffer from sparse graphs lacking useful information, which normally appears in practice. To alleviate this problem, we propose a novel multi-view and multi-order SGL (M(2)SGL) model which introduces multiple different orders (multi-order) graphs into the SGL procedure reasonably. To be more specific, M(2)SGL designs a two-layer weighted-learning mechanism, in which the first layer truncatedly selects part of views in different orders to retain the most useful information, and the second layer assigns smooth weights into retained multi-order graphs to fuse them attentively. Moreover, an iterative optimization algorithm is derived to solve the optimization problem involved in M(2)SGL, and the corresponding theoretical analyses are provided. In experiments, extensive empirical results demonstrate that the proposed M(2)SGL model achieves the state-of-the-art performance in several benchmarks.

关键词： clustering methods clustering algorithms Sun Fuses Representation learning Periodic structures Optics Multi-order graph multi-view clustering structured graph learning (SGL) unsupervised learning

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GCF-MLD: Integrated Approach for Money Laundering Detection Using Machine Learning and Graph Network Analysis

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IEEE ACCESS 2024年 12卷 183961-183972页

作者： Irshad, Faizan Alkhalifah, Tamim Alturise, Fahad Khan, Yaser Daanial Univ Management & Technol Sch Syst & Technol Dept Comp Sci Lahore 54770 Pakistan Qassim Univ Coll Comp Dept Comp Engn Buraydah 52571 Saudi Arabia Qassim Univ Coll Comp Dept Cybersecur Buraydah 52571 Saudi Arabia

Money laundering is a critical issue for financial institutions especially in developing countries and detecting such suspicious activities is a challenging task. The rule-based money laundering detection systems currently being used by the financial institutions rely on pre-defined rules that are often not able to keep up with rapidly changing tactics of money launderers. In this regard, machine learning based approaches have gained attention for detecting suspicious transactions related to money laundering. In this study, we propose a novel approach "Graph-clustering Fusion for Money Laundering Detection (GCF-MLD)" combining graph network analysis with clustering methodology to identify suspicious accounts in real-world banking dataset. The results exhibit that the proposed model can be effectively used by the financial institutions to replace current rule-based techniques to significantly improve the efficiency and effectiveness of money laundering detection systems.

关键词： Machine learning Terrorism Machine learning algorithms Banking Monitoring clustering algorithms Support vector machines Drugs Risk management Government governmental factors machine learning financial services money laundering clustering methods

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Programmable Olfactory Computing

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IEEE MICRO 2024年第4期44卷 88-96页

作者： Bleier, Nathaniel Wezelis, Abigail Varshney, Lav Kumar, Rakesh Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Univ Illinois Urbana IL 61801 USA

Although smell is arguably the most visceral of senses, olfactory computing has been barely explored in the mainstream. We argue that this is a good time to explore olfactory computing as driver applications are emerging, sensors are dramatically better, and nontraditional form factors that would be required to support olfactory computing have widespread acceptance. Through a comprehensive review of literature, we identify the key algorithms needed to support a wide variety of olfactory computing tasks. We profiled these algorithms on existing hardware and identified several characteristics, including the preponderance of fixed-point computation, linear operations, and real arithmetic;a variety of data-memory requirements;and opportunities for data-level parallelism. We propose Ahromaa, a heterogeneous architecture for olfactory computing that targets power- and energy-constrained olfactory computing workloads, and evaluate it against the baseline architectures of a microcontroller unit (MCU), coarse-grained reconfigurable array, and an MCU with packed single instruction, multiple data.

关键词： Task analysis Location awareness Forestry Forensics Estimation Drugs clustering algorithms

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