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Adaptive and Low-Cost Traffic Engineering: A Traffic Matrix clustering Perspective

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2025年第2期43卷 510-523页

作者： Liu, Yi Geng, Nan Xu, Mingwei Yang, Yuan Dong, Enhuan Liu, Chenyi Gan, Qiaoyin Li, Qing Wu, Jianping Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Inst Network Sci & Cyberspace Beijing 100084 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

Traffic engineering (TE) has attracted extensive attention over the years. Operators expect to design a TE scheme that accommodates traffic dynamics well and achieves good TE performance with little overhead. Some approaches like oblivious routing compute an optimal static routing based on a large traffic matrix (TM) range, which usually leads to much performance loss. Many approaches compute routing solutions based on one or a few representative TMs obtained from observed historical TMs. However, they may suffer from performance degradation for unexpected TMs and usually induce much overhead of system operating. In this paper, we propose ALTE, an adaptive and low-cost TE scheme based on TM classification. We develop a novel clustering algorithm to properly group a set of historical TMs into several clusters and compute a candidate routing solution for each TM cluster. A machine learning classifier is trained to infer the proper candidate routing solution online based on the features extracted from some easily measured statistics. We implement a system prototype of ALTE and do extensive simulations and experiments using both real and synthetic traffic traces. The results show that ALTE achieves near-optimal performance for dynamic traffic and introduces little overhead of routing updates.

关键词： Routing Real-time systems Feature extraction Computational modeling clustering algorithms Quality of service Optimization models Classification algorithms Transforms Topology Traffic engineering traffic matrix classification machine learning

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A Deep Learning Approach for Throughput Enhanced clustering and Spectrally Efficient Resource Allocation in Ultra-Dense Networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2025年第1期22卷 582-591页

作者： Katwal, Saksham Sharma, Nidhi Kumar, Krishan Natl Inst Technol Hamirpur Dept Elect & Commun Engn Hamirpur 177005 India

The primary obstacle for the wireless industry is meeting the growing demand for cellular services, which necessitates the deployment of numerous femto base stations (FBSs) in ultra-dense networks. Effective resource distribution among densely and randomly distributed FBSs in ultra-dense is difficult, mainly because of intensified interference problems. The K-means clustering is improved by employing the Davies Bouldin index, which separates the clusters to prevent overlapping and mitigate interference. The elbow approach is utilized to determine the optimal number of clusters. Afterward, attention is directed toward addressing efficient resource allocation through a distributive methodology. The proposed approach makes use of a replay buffer-based multi-agent framework and uses the generative adversarial networks deep distributional Q-network (GAN-DDQN) to efficiently model and learn state-action value distributions for intelligent resource allocation. To further improve control over the training error, the distributions are estimated by approximating a whole quantile function. The numerical results validate the effectiveness of both the proposed clustering method and the GAN-DDQN-based resource allocation scheme in optimizing throughput, fairness, energy efficiency, and spectrum efficiency, all while maintaining the QoS for all users.

关键词： Resource management Interference Throughput clustering algorithms Quality of service Generative adversarial networks Elbow Ultra-dense networks Computational complexity Systems architecture throughput clustering distributed deep neural network resource allocation

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Navi-Based Distributed Adaptive clustering and Estimation Over Multitask Networks

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 4059-4069页

作者： He, Yilin Hu, Limei Chen, Feng Ren, Xiaoping Duan, Shukai Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Southwest Univ Brain Inspired Comp & Intelligent Control Key Lab Chongqing 400715 Peoples R China Key Lab Luminescence Anal & Mol Sensing Chongqing 400715 Peoples R China

The distributed multitask learning (MTL) are widely studied under the area of wireless sensor networks, Internet of Things, and Internet of Vehicles in recent years. However, when faced with real scenarios, the presence of unknown clustering information and intricate network environment drives the degradation of existing distributed MTL algorithms. To tackle the issue, the navi-based distributed adaptive clustering and estimation algorithm is proposed. This algorithm operates within a hybrid system: one subsystem dedicated to clustering, another to estimation, and the navigation subsystem (N-Sub), which enhance the overall system accuracy by guiding the other subsystems through convex combinations. In addition, two extra schemes are designed to improve the accuracy and convergence speed of the overall system. The mean and mean-square performance of the proposed algorithm is analyzed theoretically. Simulations demonstrate the effectiveness of the proposed algorithm.

关键词： clustering algorithms Estimation Accuracy Wireless sensor networks Convergence Multitasking Filters Navigation Mathematical models Collaboration clustering convex combination distributed estimation multitask learning (MTL) wireless sensor networks (WSNs)

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Superpixel-Based Bipartite Graph clustering Enriched With Spatial Information for Hyperspectral and LiDAR Data

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Cao, Zhe Lu, Yihang Xin, Haonan Wang, Rong Nie, Feiping Sebilo, Mathieu Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Shaanxi Peoples R China Sorbonne Univ Inst Ecol & Environm Sci Paris IEES F-75005 Paris France

The surge in remote sensing (RS) data underscores the need for improved data diversity and processing. While integrating hyperspectral (HS) and light detection and ranging (LiDAR) data enhances analysis and addresses spectral variability, the high dimensionality, noise, and outliers inherent in hyperspectral images present significant challenges. In addition, the precise labeling required for HS makes supervised classification labor-intensive, professional-focused, and time-consuming, further motivating the development of advanced HS clustering algorithms to address these issues. Unsupervised clustering addresses the above issues but still struggles due to the underutilization of auxiliary spatial and structural information, high data dimensionality with redundant hyperspectral bands, and information divergence from heterogeneity among multimodal data. These challenges impede the effective extraction of consistent structures, undermining clustering stability and overall model performance. To address these challenges, we propose a superpixel-based bipartite graph clustering (SBGC) enriched with spatial information for hyperspectral and LiDAR data models. Our proposed method fully utilizes spatial information to construct meaningful bipartite graphs for the efficient processing of multimodal RS data. By adopting a projected clustering paradigm, our approach simultaneously clusters and reduces dimensionality, effectively eliminating redundant bands. In addition, it innovatively stacks multimodal data into tensors, thoroughly exploring the consistent structures in the low-rank space among different modalities. This reduces the heterogeneity-induced information divergence and significantly enhances clustering performance. Extensive experiments on real datasets confirm the method's effectiveness and advanced capabilities.

关键词： Hyperspectral imaging Tensors Laser radar Discrete Fourier transforms Data models Feature extraction Land surface Computational modeling clustering algorithms Bipartite graph Bipartite graphs dimensionality reduction (DR) remote sensing (RS) scalable method spatial information tensor-based clustering unsupervised learning

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Robust Joint Graph Learning for Multi-View clustering

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IEEE TRANSACTIONS ON BIG DATA 2025年第2期11卷 722-734页

作者： He, Yanfang Yusof, Umi Kalsom Univ Sains Malaysia Sch Comp Sci Gelugor 11800 Penang Malaysia

In real-world applications, multi-view datasets often comprise diverse data sources or views, inevitably accompanied by noise. However, most existing graph-based multi-view clustering methods utilize fixed graph similarity matrices to handle noisy multi-view data, necessitating additional clustering steps for obtaining the final clustering. This paper proposes a Robust Joint Graph learning for Multi-view clustering (RJGMC) based on & ell;(1)-norm to address these problems. RJGMC integrates the learning processes of the graph similarity matrix and the unified graph matrix to improve mutual reinforcement between these graph matrices. Simultaneously, employing the & ell;(1)-norm to generate the unified graph matrix enhances the algorithm's robustness. A rank constraint is imposed on the graph Laplacian matrix of the unified graph matrix, where clustering can be divided directly without additional processing. In addition, we also introduce a method for automatically assigning optimal weights to each view. The optimization of this objective function employs an alternating optimization approach. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art techniques regarding clustering performance and robustness.

关键词： clustering algorithms Noise Vectors Robustness Laplace equations Linear programming Feature extraction Unified graph matrix & ell (1)-norm multi-view clustering noise robustness rank constraint

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Globality Meets Locality: An Anchor Graph Collaborative Learning Framework for Fast Multiview Subspace clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Guo, Jipeng Sun, Yanfeng Ma, Xin Gao, Junbin Hu, Yongli Wang, Youqing Yin, Baocai Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Beijing Univ Technol Beijing Key Lab Multimedia & Intelligent Software Beijing 100124 Peoples R China Univ Sydney Business Sch Discipline Business Analyt Camperdown NSW 2006 Australia

Multiview subspace clustering (MSC) maximizes the utilization of complementary description information provided by multiview data and achieves impressive clustering performance. However, most of them are inefficient or even invalid among large-scale scenarios due to expensive computational complexity. Recently, anchor strategy has been developed to address this, which selects a few representative samples as anchor points for representation learning and anchor graph construction. However, most of them only explore single cross-view correlation, i.e., cross-view consistency from the global aspect or cross-view complementarity from the local aspect, which provides insufficient semantic correlation understanding and exploration for complex multiview data. To effectively address this issue, this study proposes a fast multiview subspace clustering (FMSC) with local-global anchor representation collaborative learning. FMSC integrates the discriminative anchor points learning and anchor graph construction with optimal structure into a joint framework. Furthermore, local (view-specific) and global (view-shared) anchor representations are learned collaboratively under two interaction strategies at different levels, providing beneficial guidance from global learning to local learning. Thus, the proposed FMSC can maximize the exploration of the complementarity-consistency among multiview data and capture a more comprehensive semantic correlation. More importantly, an effective algorithm with linear complexity is designed to solve the corresponding optimization problem of FMSC, making it more practical in large-scale clustering tasks. Extensive experimental results confirm the superiority of the proposed FMSC in both clustering performance and computational efficiency.

关键词： Correlation Representation learning Optimization Computational complexity Bipartite graph Semantics Federated learning Computational efficiency clustering algorithms Tensors Collaborative interaction fast multiview subspace clustering (FMSC) global-local anchor representation optimal bipartite graph learning

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年第1期12卷 435-446页

作者： Sun, Weijun Jiang, Zhikun Chen, Yonghao Li, Jiaqing Zhou, Chengbin Han, Na Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Minist Educ Key Lab Intelligent Detect & Internet Things Mfg Guangzhou 510006 Peoples R China Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Peoples R China Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China Guangdong HongKong Macao Joint Lab Smart Discrete Guangzhou 510006 Peoples R China 111 Ctr Intelligent Batch Mfg Based IoT Technol Sch Comp Sci Guangzhou 510006 Peoples R China Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou 510665 Peoples R China

The graph-based multiview clustering has gained significant attention due to its effectiveness in representing complex relationships among multiview data for enhanced clustering. Among the previous graph-based methods, the multiview graph learning (or graph fusion) technique has rapidly emerged as a promising direction, which, however, still suffers from two critical limitations. First, most of previous methods adopt a single-level of graph fusion, which lack the ability to go from single-level graph fusion to multilevel (deep) graph fusion. Second, they generally focus on constructing an optimal unified graph but cannot fully investigate the correlations among multiple views. Therefore, it is difficult to establish a comprehensive and obvious graph structure. In light of this, this article presents a new multiview graph learning method called deep similarity graph fusion (DSGF) for the multiview clustering task, where three pathways are simultaneously leveraged to fuse multilevel similarity into a unified graph. Particularly, multilevel graph fusion is utilized to obtain a view-specific similarity graph for each view and then fuse these single-view graphs (via three levels of graph fusion) into a robust graph, which takes advantage of deeper consensus information between various similarity graphs and improves the quality of the learned graph for the final spectral clustering process. Extensive experiments are conducted on six real-world multiview datasets, which demonstrate the highly competitive clustering performance of DSGF in comparison with state-of-the- art methods.

关键词： Kernel Linear programming clustering methods Sun Fuses clustering algorithms Automation Semisupervised learning Optimization Manufacturing Deep graph fusion multiview clustering similarity graph

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Research on Density Peak clustering Color-Separation Algorithm Based on SOM Neural Network

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IEEE ACCESS 2025年 13卷 16811-16823页

作者： Ru, Xin Wang, Yanhao Chen, Ran Zeng, Zhifa Peng, Laihu Tang, Wei Shi, Weimin Wang, Hui Zhejiang Sci Tech Univ Sch Mech Engn Hangzhou 310018 Peoples R China Shanxi Univ Sch Comp & Informat Technol Taiyuan 237016 Peoples R China Zhejiang Hengqiang Technol Co Ltd Hangzhou 311100 Peoples R China Zhejiang Sci Tech Univ Longgang Inst Wenzhou 325802 Peoples R China

Pattern images input to The knitting CAD system have a large number of different colors, and it is necessary to reduce the number of colors in the image by merging similar colors through color-separation algorithms. However, current pattern images usually have the problem of image degradation, which seriously affects the accuracy of color-separation algorithms. In addition, the traditional color-separation algorithm needs to rely on the manual setting of clustering parameters, which is very time-consuming and laborious. To solve these problems, In this paper, we propose a density peak clustering color-separation algorithm based on self-organizing mapping (SOM) neural network, which firstly uses enhanced super-resolution generative adversarial network (Real-ESRGAN) blind super-resolution reconstruction network to clarify the degraded image and obtain a high-resolution image with clear boundaries;secondly, we carry out the initial clustering through the SOM neural network to simplify the image information;and then we use an improved density peak clustering (DPC) algorithm to calculate clustering centers under the conditions of conforming to the perception of human eyes, and carry out secondary clustering on the image;finally, carry out image post-processing through the variegated spots merging algorithm based on the connected component analysis. The experimental results show that the algorithm proposed in this study can effectively deal with degraded pattern images, and the clustering effectiveness evaluation indices perform well.

关键词： Image color analysis clustering algorithms Neural networks Image reconstruction Superresolution Degradation Image segmentation Classification algorithms Computational efficiency NIST Color-separation algorithm density peak clustering (DPC) knitting CAD self-organizing mapping (SOM) neural network blind super-resolution reconstruction network

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Improved PBFT Consensus Mechanism Based on Voting Sort clustering Partition With Group Signature for IoT

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第2期26卷 2239-2251页

作者： Dong, Shi Su, Huadong Hou, Ruizhe Shankar, Achyut Zhoukou Normal Univ Sch Comp Sci & Technol Zhoukou 466001 Peoples R China Wuhan Text Univ Sch Comp Sci & Artificial Intelligence Wuhan 430200 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China Univ Warwick Dept Cyber Syst Engn WMG Coventry CV7 4AL England Graphic Era Deemed Univ Dept Comp Sci & Engn Dehra Dun 248002 India Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 Punjab India Lovely Profess Univ Sch Comp Sci Engn Phagwara 144411 Punjab India Chitkara Univ Ctr Res Impact & Outcome Chandigarh 160002 Punjab India

The consensus mechanism is crucial to blockchain performance, making it essential to design a mechanism that aligns with the characteristics of the Internet of Things (IoT). This paper focuses on the application of PBFT consensus mechanism in the Internet of things. However, it is found that the current PBFT consensus mechanisms need to address some problems, such as communication overhead, bandwidth occupation and privacy protection. In this paper, we propose a voting sorting clustering mechanism based on group signatures to enhance the PBFT consensus mechanism (IPBFT), ensuring privacy protection between Internet of Things nodes. Finally, the communication efficiency is improved. The voting sorting clustering method reduces the communication probability with the nodes, and decreases the communication overhead and bandwidth occupation. Experimental results show that compared with other mechanisms, the proposed mechanism increases throughput, reduces communication overhead and bandwidth occupation, and alleviates privacy protection problems.

关键词： Internet of Things Consensus protocol Throughput Fault tolerant systems Fault tolerance Delays Consensus algorithm Scalability Protection clustering algorithms Consensus mechanism consortium blockchain PBFT IoT clustering mechanism group signature

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Memristive clustering: A Novel Sustainable Parameter Selection Based on Memristive Circuit Model

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2025年第1期10卷 18-27页

作者： Qiao, Kaikai Ma, Ben Wang, Lidan Duan, Shukai Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Chongqing Key Lab Brain Inspired Comp & Intelligen Chongqing 400715 Peoples R China

In recent years, memristors have attracted much attention in the fields of nonvolatile memory, logic operation and neuromorphic computing. As a new type of two-terminal passive electronic component similar to sandwich structure, its main resistance mechanism is the formation and fracture of metal or oxygen vacancy conductive filaments. Traditional clustering algorithms own strong sensitivity to different parameter selection, including partition clustering algorithm and density clustering algorithm. In view of the non-volatile characteristics of memristor and the In-memory computing characteristics of memristive circuit, this paper designs a new memristive clustering paradigm, and further verifies the feasibility and effectiveness of the proposed analog circuit to improve the performance of clustering parameters by exploring the data mining and image segmentation problems of these two types of clustering algorithms.

关键词： clustering algorithms Partitioning algorithms Ions Resistance Programming Voltage Memristors Bilevel programming clustering method data mining in-memory computing optimization

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