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A Spatial Model and Multiobjective Fuzzy Inference-Based clustering Routing Algorithm for Bridge Monitoring

IEEE SENSORS JOURNAL 2025年第1期25卷 1669-1681页

作者： Yang, Jiguang Huo, Jiuyuan Mu, Cong Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China Natl Cryosphere Desert Data Ctr NCDC Lanzhou 730000 Peoples R China

Wireless sensors have the advantages of distributed and flexible deployment, real-time monitoring, multisensor collaboration, self-organization, and self-adaptation. They can be widely used in bridge structural health monitoring systems (BHMSs). At the same time, wireless sensors are still limited by battery life and communication distance;extending the life cycle of wireless sensor networks (WSNs) employing optimal deployment and effective improvement of routing algorithms is a research hotspot for domestic and foreign engineering and academic circles. In this article, a WSN clustering routing algorithm based on bridge spatial model and multiobjective fuzzy inference (BSMOFC) is proposed to solve the problem that the existing research has not considered the spatial characteristics of bridges and the reasonable choice of cluster head (CH) in the clustering routing algorithm. First, a bridge space model is designed to simulate the 3-D deployment of WSN nodes;second, the CH selection process of the WSN clustering routing algorithm is defined as a multiobjective optimization problem, and the multiobjective non-dominated sorted whale optimization algorithm (NSWOA) is used to solve the set of Pareto solutions. All the solutions are defined as the candidate CHs (C-CHs), and then a fuzzy inference system is used to choose the final CHs (F-CHs). Finally, the simulation platform MATLAB completes the performance verification of the BSMOFC algorithm. The simulation results show that the BSMOFC algorithm can effectively extend the WSN lifetime and provide a new practical method for selecting a Pareto optimal solution.

关键词： Wireless sensor networks clustering algorithms Bridges Routing Optimization Monitoring Inference algorithms Wireless communication Fuzzy logic Sensor phenomena and characterization Bridge space model bridge structural health monitoring system (BHMS) fuzzy inference system multiobjective optimization wireless sensor network (WSN)

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Enhancing clustering Performance With Tensorized High-Order Bipartite Graphs: A Structured Graph Learning Approach

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第3期35卷 2616-2631页

作者： Zhao, Zihua Cao, Zhe Xin, Haonan Wang, Rong Wu, Danyang Wang, Zhen Nie, Feiping 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 Northwest A&F Univ Coll Informat Engn Yangling 712100 Peoples R China Northwest A&F Univ Shaanxi Engn Res Ctr Intelligent Percept & Anal Ag Yangling 712100 Peoples R China

clustering based on structured graph learning involves acquiring a proximity matrix with an explicit clustering structure from the original one. However, the original proximity matrix often lacks some must-links compared to the groundtruth, constraining the upper bound of clustering performance. High-order proximity information can mitigate this limitation, yet traditional high-order proximity matrix-based methods are time-intensive. To tackle this, we propose the Tensorized High-order Bipartite Graphs-based structured proximity matrix learning method (THBG). Firstly, we introduce a high-order bipartite graph proximity matrix with a swift computation method, incorporating high-order information and significantly reducing computational overhead. Secondly, we apply tensor nuclear norm minimization to the tensor composed of high-order bipartite graphs, learning a low-rank tensor representation that effectively harnesses the consistency of high-order information. Concurrently, a structured bipartite graph proximity matrix with an explicit clustering structure is adaptively learned based on the low-rank tensor representation and Laplace rank constraint. Experimental results demonstrate the superiority and great potential of this method. Code available: https://***/r/THBG-D10D.

关键词： Tensors Bipartite graph Noise clustering algorithms Turning Sparse matrices Minimization Matrix decomposition Learning systems Laplace equations clustering structured proximity matrix high-order bipartite graph tensor nuclear norm

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Hierarchical Federated Learning With Distributed clustering and Multichannel ALOHA

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IEEE SENSORS JOURNAL 2025年第7期25卷 12128-12142页

作者： Da Silva, Rodolfo Viturino Nogueira Dos Reis, Ana Flavia Brante, Glauber Souza, Richard Demo Univ Tecnol Fed Parana UTFPR Grad Program Elect & Comp Engn BR-80230901 Curitiba Brazil Inst Nacl Telecomunicacoes INATEL BR-37536001 Santa Rita Do Sapucai Brazil Fed Univ Santa Catarina UFSC Dept Elect & Elect Engn BR-88040900 Florianopolis Brazil

In an age characterized by seamless interconnectivity, the quantity of Internet of Things (IoT) devices has experienced substantial growth in recent years, with projections indicating further expansion. In this context, federated learning (FL) plays an important role in the future of wireless communications, offering numerous advantages over traditional centralized learning approaches, including data privacy preservation, reduced bandwidth usage, improved accuracy, and customization. However, selecting an appropriate wireless protocol and data transmission method for FL is crucial. In this work, we adopt the multichannel ALOHA protocol due to its asynchronous nature and simple implementation compared to other protocols. This article focuses on optimizing multichannel ALOHA communication within a hierarchical FL (HFL) system by creating a device-to-device (D2D) clustering scheme, which enables a single base station (BS) to serve more devices and drastically reduces the achievable error.

关键词： Device-to-device communication Internet of Things Protocols Wireless communication Synchronization Sensors clustering algorithms Wireless sensor networks Federated learning Sensor phenomena and characterization clustering device-to-device (D2D) federated learning (FL) multichannel ALOHA

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Distributed multi-object tracking under limited field of view heterogeneous sensors with density clustering

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SIGNAL PROCESSING 2025年 228卷

作者： Chen, Fei Van Nguyen, Hoa Leong, Alex S. Panicker, Sabita Baker, Robin Ranasinghe, Damith C. Univ Adelaide Sch Comp & Math Sci Adelaide SA 5005 Australia Curtin Univ Sch Elect Engn Comp & Math Sci Bentley WA 6102 Australia Def Sci & Technol Grp Platforms Div Canberra Australia Def Sci & Technol Grp Land & Integrated Force Div Edinburgh SA Australia

We consider the problem of tracking multiple, unknown, and time-varying numbers of objects using a distributed network of heterogeneous sensors. In an effort to derive a formulation for practical settings, we consider limited and unknown sensor field-of-views (FoVs), sensors with limited local computational resources and communication channel capacity. The resulting distributed multi-object tracking algorithm involves solving an NP-hard multidimensional assignment problem either optimally for small-size problems or sub-optimally for general practical problems. For general problems, we propose an efficient distributed multi-object tracking algorithm that performs track-to-track fusion using a clustering-based analysis of the state space transformed into a density space to mitigate the complexity of the assignment problem. The proposed algorithm can more efficiently group local track estimates for fusion than existing approaches. To ensure we achieve globally consistent identities for tracks across a network of nodes as objects move between FoVs, we develop a graph-based algorithm to achieve label consensus and minimise track segmentation. Numerical experiments with synthetic and real-world trajectory datasets demonstrate that our proposed method is significantly more computationally efficient than state-of-the-art solutions, achieving similar tracking accuracy and bandwidth requirements but with improved label consistency.

关键词： Label consistency clustering algorithms Multi-sensor multi-object tracking Distributed multi-object tracking

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EM-Based Algorithm for Unsupervised clustering of Measurements from a Radar Sensor Network

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第1期61卷 787-801页

作者： Yan, Linjie Addabbo, Pia Fiscante, Nicomino Clemente, Carmine Hao, Chengpeng Giunta, Gaetano Orlando, Danilo Chinese Acad Sci Inst Acoust Beijing 100190 Peoples R China Univ Giustino Fortunato I-8210 Benevento Italy Univ Roma Tre Ind Elect & Mech Engn Dept I-00146 Rome Italy Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Scotland Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy

This article deals with the problem of clustering data returned by a radar sensor network that monitors a region where multiple moving targets are present. The network is formed by nodes with limited functionalities that transmit the estimates of target positions (after a detection) to a fusion center without any association between measurements and targets. To solve the problem at hand, we resort to model-based learning algorithms and instead of applying the plain maximum likelihood approach, due to the related computational requirements, we exploit the latent variable model coupled with the expectation-maximization algorithm. The devised estimation procedure returns posterior probabilities that are used to cluster the huge amount of data collected by the fusion center. Remarkably, we also consider challenging scenarios with an unknown number of targets and estimate it by means of the model-order selection rules. The clustering performance of the proposed strategy is compared to that of conventional data-driven methods over synthetic data. The numerical examples point out that the herein proposed solutions can provide reliable clustering performance overcoming the considered competitors.

关键词： Radar Radar tracking clustering algorithms Time measurement Random variables Computational modeling Target tracking Batch algorithms expectation-maximization (EM) measurement clustering multiple moving targets radar sensor network unsupervised learning

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Toward Autonomous Distributed clustering

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第2期9卷 2065-2072页

作者： Yang, Jie Lin, Chin-Teng Univ Technol Sydney Australian AI Inst GrapheneX UTS HAI Ctr Computat Intelligence & Brain Comp Interface LabS Sydney Australia

Compared to traditional centralized clustering, distributed clustering offers the advantage of parallel processing of data from different sites, enhancing the efficiency of clustering while preserving the privacy of the data at each site. However, most existing distributed clustering techniques require manual tuning of several parameters or hyperparameters, which can pose challenges for practical applications. This paper introduces a novel parameter-free distributed clustering framework known as distributed torque clustering (DTC). When dealing with data or subdata distributed across various sites, DTC predominantly executes two steps. The first step is a data reduction at each site using torque clustering, and the second step involves performing global clustering with weighted torque clustering. We compare DTC against six state-of-the-art distributed clustering algorithms and automatic centralized clustering techniques on ten large-scale or medium-scale datasets. The results show that the average rank of DTC is at least three times better than those of the other algorithms across all the datasets. Additionally, DTC can accurately predict the ground-truth number of clusters in nine out of ten datasets, further demonstrating its competitive performance and practical potential.

关键词： clustering algorithms Torque clustering methods Heuristic algorithms Complexity theory Partitioning algorithms Distributed databases clustering distributed clustering parameter-free autonomous unsupervised learning

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A Dictionary-Enhanced clustering Compressive Sensing Routing Protocol for Large-Scale WSNs

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IEEE SENSORS JOURNAL 2025年第4期25卷 7445-7456页

作者： Tong, Junjie Shou, Shenwei Wang, Hui Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China

Designing an efficient energy-saving routing protocol to optimize network lifespan is a pivotal challenge in large-scale wireless sensor networks (WSNs). In this article, a dictionary-enhanced clustering compressive sensing routing (DEC2R) protocol is designed to conserve energy and provide network load balancing. In DEC2R, the optimal number of clusters for each round is accurately calculated based on the analysis of the optimal cluster size. Through learning the sparse dictionary method, a low-coherence sensing matrix is constructed for data transmission and compression. On this basis, the optimal cluster heads (CHs) are selected based on a cost function (including remaining energy and distance). Non-CHs determine whether to join a cluster based on energy and distance, ultimately completing the clustering formation. In each cluster, data nodes multiply the data by measurement coefficients and transmit it to the CH via the shortest path. Between clusters, each CH forwards the data packet to the next CH along the transmission path. In the end, the sink node receives the entire compressed packets. The simulation results demonstrate the effectiveness of DEC2R. Compared with LEACH, PEGASIS, CDG, and EIREC protocols, dictionary-enhanced clustering compressive sensing routing (DEC2R) significantly extends the lifetime of the network and improves energy efficiency.

关键词： Wireless sensor networks Sensors Energy efficiency Energy consumption Routing clustering algorithms Data collection Routing protocols Compressed sensing Costs clustering compressive sensing (CS) dictionary learning intercluster routing wireless sensor networks (WSNs)

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Contrastive Ensemble clustering

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

作者： Chen, Man-Sheng Lin, Jia-Qi Wang, Chang-Dong Huang, Dong Lai, Jian-Huang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Sun Yat Sen Univ Guangdong Key Lab Big Data Anal & Proc Guangzhou 510275 Peoples R China Guangxi Zhuang Autonomous Reg Informat Ctr Guangxi Key Lab Digital Infrastructure Nanning 530201 Peoples R China Sun Yat Sen Univ Sch Math Zhuhai Guangzhou 510275 Peoples R China South China Univ Dept Comp Sci Guangzhou 510640 Peoples R China Sun Yat sen Univ Sch Comp Sci & Engn Guangdong Key Lab Informat Secur Technol Minist Educ Guangzhou 510275 Peoples R China Sun Yat sen Univ Key Lab Machine Intelligence & Adv Comp Minist Educ Guangzhou 510275 Peoples R China

Ensemble clustering aims to combine different base clusterings into a better clustering than that of the individual one. In general, a co-association matrix depicting the pairwise affinity between different data samples is constructed by average fusion or weighted fusion of the connective matrices from multiple base clusterings. Despite the significant success, the existing works fail to capture the global structure information from multiple noisy connective matrices. Meanwhile, the locality property of the resulting representation matrix could not be explicitly preserved. In this article, we propose a novel contrastive ensemble clustering (CEC) method. Specifically, a consensus mapping model is designed for the discovery of the latent representation from the noisy observations with distinct confidences. Furthermore, a contrastive regularizer is dexterously formulated to refine the latent representation while preserving its locality property. Extensive experiments conducted on several benchmark datasets demonstrate the superiority of the proposed CEC method. To the best of our knowledge, it is the first time to explore the potential of latent representation learning and contrastive components for the ensemble clustering task.

关键词： clustering methods clustering algorithms Noise measurement Contrastive learning Representation learning Partitioning algorithms Noise Mutual information Loss measurement Data structures Adaptive fusion contrastive components ensemble clustering latent representation

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Detecting attacks on the internet of things network in the computing fog layer with an embedded learning approach based on clustering and blockchain

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第4期28卷 1-31页

作者： Abdolmanan, Goushlavandani Babaei Bayat, Peyman Ekbatanifard, Gholamhossein Islamic Azad Univ Dept Comp Engn Rasht Branch Rasht Iran Islamic Azad Univ Dept Comp Engn Lahijan Branch Lahijan Gilan Iran

The internet of things (IoT) is exposed to various cyber attacks that target its security. An intrusion detection system (IDS) in the IoT must be able to detect attacks and their types and identify complex and new attacks. The IDS in the IOT must have a distributed architecture to process large network traffic. Training machine learning methods and learning key features increases the accuracy of the IDS. This manuscript proposes a distributed IDS deployed within the fog layer. The initial step involves leveraging game theory and Generative Adversarial Networks (GANs) and traffic balancing. Subsequently, enhanced with opposition-based learning, the Jellyfish Search (JS) optimizer algorithm facilitates feature selection within fog nodes. Utilizing majority voting and weighting mechanisms, fog nodes determine optimal feature vectors. To prevent manipulation, feature vectors are exchanged via blockchain. Each fog node employs three classifiers-Multi-Layer Perceptron (MLP), Random Forest (RF), and XGBoost-utilizing ensemble learning techniques. Fog nodes maintain blacklists, updating them via blockchain. The Embedded Learning + IJSO (EJSO) framework offers benefits such as network traffic balancing, intelligent dimension reduction, embedded learning, and privacy preservation through blockchain integration. Experimental evaluation conducted on NSL-KDD and UNSW-NB15 datasets demonstrates the efficacy of the EJSO method in attack detection. The introduced clustering method, incorporating fuzzy clustering and the Arithmetic Optimization Algorithm (AOA), showcases high accuracy, sensitivity, and precision rates. Specifically, in the NSL-KDD dataset, the EJSO method achieves accuracy, sensitivity, and precision rates of 99.87, 99.66, and 99.12%, respectively. In the UNSW-NB15 dataset, these rates stand at 98.82, 98.74, and 98.59%, respectively. Additionally, the EJSO method outperforms existing techniques regarding dimension reduction and attack detection accuracy, surpassing

关键词： Intrusion detection system Distributed architecture Fog computing Internet of things clustering algorithms Blockchain technology

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clustering and Fusion of Distributed Multitone Frequency Measurement Data for Enhanced Grid-Level Power Quality Monitoring

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Macii, David Pegoraro, Paolo Attilio Sitzia, Carlo Univ Trento Dept Ind Engn I-38122 Trento Italy Univ Cagliari Dept Elect & Elect Engn I-09123 Cagliari Italy

The increasing diffusion of both nonlinear loads and renewable-based distributed energy resources (DERs) may cause serious power quality (PQ) problems due to the injection of significant harmonics and interharmonics. To mitigate some of these problems, the controllers of smart power electronic converters (PECs) can embed techniques for harmonics' compensation. However, to maximize their performance, the most relevant narrowband disturbances should be detected and their frequency estimated with high accuracy. Given that harmonics and interharmonics maintain consistent frequencies across different points in the grid, this article introduces a flexible technique that leverages multiple distributed estimates to accurately identify critical narrowband disturbances propagating throughout the system. The proposed approach relies on four main steps: the initial two are designed for implementation in various measurement devices to identify local interferers, while the latter two are intended to operate on a central aggregator. This central unit collects the multitone distributed frequency and amplitude estimates to cluster and fuse data corresponding to the same interferers. A key novelty of the proposed approach lies in the clustering algorithm that relies on an adaptive threshold depending on the actual measurement uncertainty of the available data. This method effectively filters out potential outliers, reducing the risk of misdetections, before calculating the uncertainty-weighted average of frequency values within each cluster. Several Monte Carlo (MC) and grid-level simulations confirm the excellent performance of the proposed approach in identifying critical sinusoidal disturbances, thus paving the way for better distributed compensation strategies to be implemented in PECs.

关键词： Harmonic analysis Frequency estimation Noise Frequency shift keying Vectors Estimation Root mean square Narrowband clustering algorithms Power harmonic filters Data fusion density-based spatial clustering of applications with noise (DBSCAN) estimation of signal parameters via the rotational invariance technique (ESPRIT) frequency estimation harmonics and interharmonics detection power electronic converters (PECs) synchronized measurement

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