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Energy-Efficient clustering algorithm for Magnetic Induction-Based Underwater Wireless Sensor Networks

IEEE ACCESS 2019年 7卷 5975-5983页

作者： Wang, Sai Nguyen, Thu L. N. Shin, Yoan Soongsil Univ Sch Elect Engn Seoul 06978 South Korea

Magnetic induction (MI) communication is a promising technology for next-generation low-power underwater wireless sensor networks (UWSNs). clustering algorithm design becomes an important and challenging issue in today's MI-based UWSNs. In contrast to the conventional approaches which suffer from continuous movement of ocean current and traffic loads in different areas of the network, we consider a clustering algorithm based on the Voronoi diagram and node density distribution to improve the energy efficiency and to prolong the network lifetime. In particular, we propose a jellyfish breathing process for cluster head selection and an automatic adjustment algorithm for sensor nodes. The simulation results show that the proposed clustering algorithm achieves a high network capacity rate and a good equalization for the remaining energy.

关键词： Underwater sensor network magnetic induction communication clustering algorithm Poisson point distribution Voronoi diagram jellyfish breathing process

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Small and Medium Enterprises Innovation Management System Based on clustering algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2018年第4期102卷 2885-2896页

作者： Xu, Zhilong Ningbo Univ Technol Int Exchange Coll Ningbo 315211 Zhejiang Peoples R China

For most small and medium-sized enterprise (SME), the establishment of special customer management system requires specialized hardware and professional technicians, which will greatly increase the management cost of enterprises. Customer relationship management (CRM) based on cloud computing can not only reduce the management cost of SME, but also improve the business agility of SME, and help SME create new business models and market opportunities. In this study, a CRM system based on customer service is designed by using the customer classification method based on the improved K-means clustering algorithm. The system adopts the system architecture model based on network to realize the information interaction and resource sharing among different roles. The management of customer service is the core content of system design and development, and it realizes the development of CRM system for SME. The whole system effectively solves the problems such as low efficiency and limited service provision of customer management, as well as the low integrity of customer work, so as to achieve the purpose of improving the management capacity of SME.

关键词： clustering algorithm Customer management SME CRM K-means algorithm

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A Distance Metric for Uneven Clusters of Unsupervised K-Means clustering algorithm

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IEEE ACCESS 2022年 10卷 86286-86297页

作者： Raeisi, Mostafa Sesay, Abu B. Univ Calgary Dept Elect & Software Engn Calgary AB T2N 1N4 Canada

In this paper, we propose a new distance metric for the K-means clustering algorithm. Applying this metric in clustering a dataset, forms unequal clusters. This metric leads to a larger size for a cluster with a centroid away from the origin, rather than a cluster closer to the origin. The proposed metric is based on the Canberra distances and it is useful for cases that require unequal size clusters. This metric can be used in connected autonomous vehicle wireless networks to classify mobile users such as pedestrians, cyclists, and vehicles. We use a combination of mathematical and exhaustive search to establish its validity as a true distance metric. We compare the K-Means algorithm using the proposed distance metric with five other distance metrics for comparison. These metrics include the Euclidean, Manhattan, Canberra, Chi-squared, and Clark distances. Simulation results depict the effectiveness of our proposed metric compared with the other distance metrics in both one-dimensional and two-dimensional randomly generated datasets. In this paper, we use three internal evaluation measures namely the Compactness, Sum of Squared Errors (SSE), and Silhouette measures. These measures are used to study the proper number of clusters for each of the K-Means algorithms and also select the best run among multiple centroid initializations. The elbow method and the local maximum approach are used alongside the evaluation measures to select the optimal number of clusters.

关键词： Measurement clustering algorithms Classification algorithms Partitioning algorithms Euclidean distance Road traffic Training data Unsupervised learning Canberra distance chi-squared distance clustering algorithm distance metrics Euclidean distance K-means algorithm unsupervised learning

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Combined deep belief network in deep learning with affinity propagation clustering algorithm for roller bearings fault diagnosis without data label

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JOURNAL OF VIBRATION AND CONTROL 2019年第2期25卷 473-482页

作者： Xu, Fan Tse, Peter W. City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China

Unlike many traditional feature extraction methods of vibration signal such as ensemble empirical mode decomposition (EEMD), deep belief network (DBN) in deep learning can extract the useful information automatically and reduce the reliance on experts, with signal processing technology, and troubleshooting experience. In conventional fault diagnosis, data labels are required for classifiers such as support vector machine, random forest, and artificial neural networks. These are usually based on expert knowledge, for training and testing. But the process is usually tedious. The clustering model, on the other hand, can finish the roller bearings fault diagnosis without data labels, which is more efficient. There are some common clustering models which include fuzzy C-means (FCM), Gustafson-Kessel (GK), Gath-Geva (GG) models, and affinity propagation (AP). Unlike FCM, GK, and GG, which require knowledge or experience to pre-set the number of cluster center points, AP clustering algorithm can obtain the cluster center point according to the responsibility and availability calculations for all data points automatically. To the best of the authors' knowledge, AP is rarely used for fault diagnosis. In this paper, a method which combines DBN, with several hidden layers, and AP for roller bearings fault diagnosis is proposed. For data visualization, the principal component analysis (PCA) is deployed to reduce the dimension of the extracted feature. The first two principal components are employed as the input of the FCM, GK, GG, and AP models for roller bearings faults diagnosis. Compared with other combination models such as EEMD-PCA-FCM/GK/GG and DBN-PCA-FCM/GK/GG, the proposed method, from the experimental results, is superior to the aforementioned combination models.

关键词： Deep belief network deep learning affinity propagation clustering algorithm roller bearings fault diagnosis

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Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai

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TRANSPORTATION PLANNING AND TECHNOLOGY 2019年第8期42卷 739-756页

作者： Zhou, Yang Yang, Chao Zhu, Rongrong Tongji Univ Coll Transportat Engn Minist Educ Key Lab Rd & Traff Engn 4800 Caoan Rd Shanghai Peoples R China ZheJiang Dahua Technol Co Ltd Hangzhou Zhejiang Peoples R China

Smartphones have been advocated as the preferred devices for travel behavior studies over conventional surveys. But the primary challenges are candidate stops extraction from GPS data and trip ends distinction from noise. This paper develops a Resident Travel Survey System (RTSS) for GPS data collection and travel diary verification, and then uses a two-step method to identify trip ends. In the first step, a density-based spatio-temporal clustering algorithm is proposed to extract candidate stops from trajectories. In the second step, a random forest model is applied to distinguish trip ends from mode transfer points. Results show that the clustering algorithm achieves a precision of 96.2%, a recall of 99.6%, mean absolute error of time within 3?min, and average offset distance within 30 meters. The comprehensive accuracy of trip ends identification is 99.2%. The two-step method performs well in trip ends identification and promotes the efficiency of travel survey systems.

关键词： Travel survey GPS data trip end identification clustering algorithm smartphone case study

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Security Improvement of UAV Group IoT in Polygonal Area Based on Similarity clustering algorithm

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JOURNAL OF TESTING AND EVALUATION 2024年第3期52卷 1775-1788页

作者： Yan, Xiang Xu, Jia Northwest Univ Sch Informat Sci & Technol 1 Xuefu AveGuodu Educ Technol Ind Pk Xian 710000 Peoples R China Northwestern Polytech Univ Sch Elect & Informat 1 Xuefu AveGuodu Educ Technol Ind Pk Xian 710000 Peoples R China

Currently, unmanned aerial vehicle (UAV) group Internet of Things (IoT) has poor stability, poor endurance, and low security. In this regard, we report a polygon area UAV group IoT clustering algorithm for large-scale, high-speed mobile IoT environment. First, the UAV group IoT was divided into a polygon area and a few cluster heads were used to cover the polygon area uniformly. Then, the clustering index in the maximum speed similarity clustering algorithm was induced into the weighted clustering algorithm. Simultaneously, the link retention rate, relative node degree, and node residual energy ratio were improved to select the IoT node with the maximum weight as the cluster head. Finally, the IoT security was improved by reputation calculation. The results show that the clustering algorithm in this paper can reduce the number of clusters and lower the handover rate between clusters, improve the stability of clustering, prolong the survival time of the minimum node, improve the overall endurance of the IoT, and have high security.

关键词： polygon area unmanned aerial vehicle Internet of Things clustering algorithm weighting

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A Novel Fuzzy Distance-Based Minimum Spanning Tree clustering algorithm for Face Detection

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COGNITIVE COMPUTATION 2022年第4期14卷 1350-1361页

作者： Li, Yang Zhou, Wenju Shanghai Univ Sch Mech & Elect Engn & Automat Shanghai 200444 Peoples R China

Solving a clustering algorithm can usually be simplified into an optimization problem. Using relevant knowledge in graph theory, many optimization problems can be transformed into solving minimum spanning tree problems. Minimal spanning trees are also widely used in areas closely related to cognitive computing such as for face recognition by face cognition and gene data analysis by gene cognition. However, the minimum spanning tree has the shortcoming of the distance between neighbours because of which the minimum spanning tree algorithm cannot cluster unbalanced data. Thus, the face recognition rate is low, and facial expression cognition is difficult. In this paper, a minimum spanning tree algorithm based on fuzzy distance is proposed for the shortcomings of the minimum spanning tree (FCP). First, a relative neighbourhood distance measure is proposed by introducing neighbourhood rough set theory;the neighbourhood matrix is obtained based on the distance. Second, the minimum spanning tree is solved by the prim algorithm and the neighbourhood matrix. Finally, the minimum spanning tree is partitioned to realize clustering of the minimum spanning tree. In this paper, the UCI dataset and Olivetti face database are selected to verify the performance of the algorithm, and the algorithm is evaluated by three evaluation criteria. The experimental results show that the proposed algorithm can not only cluster data of any shape but also deal with unbalanced data containing noise points. Especially in face cognitive computing, the values of ACC, AMI, and ARI can reach 0.852, 0.843, and 0.782, respectively. In this study, the algorithm can obtain very good clustering results for data with good geometric structure, and the overall performance is better than other algorithms. In face recognition detection, the improved cognitive computing of faces makes it possible to accurately recognize different expressions from the same person.

关键词： Minimum spanning tree Fuzzy distance clustering algorithm Face recognition

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A Grid-Based clustering algorithm via Load Analysis for Industrial Internet of Things

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IEEE ACCESS 2018年 6卷 13117-13128页

作者： Zhang, Jing Feng, Xin Liu, Zhuang Changchun Univ Sci & Technol Coll Comp Sci & Technol Changchun 130022 Jilin Peoples R China

In the Industrial Internet of Things (IIoT), wireless sensor network (WSN) technology makes devices that communicate with each other. The information integrated from multiple data sources will be transformed into productivity. However, the clusters close to the base station take a considerable load over multi-hop transmission, and in this case, the lifetime of the industrial WSN is restricted. To solve this problem, a grid-based clustering algorithm via load analysis for IIoT is presented in this paper. First, the network load is quantitatively analyzed and then a load model is constructed. Furthermore, a set of expressions is deduced to indicate the network load distribution. It is concluded that the number of delivered packets in each level is related to the grid length at that level. The optimal grid length is obtained by solving polynomials to achieve the uniform energy consumption of nodes at each level. Finally, the network is partitioned into unequal grids according to the optimal cluster size and all the nodes of a grid are formed into a cluster. Results of the experiments show that compared with ACT, ER-HEED, and RUHEED, our algorithm balances energy depletion effectively and extends the whole network lifetime.

关键词： clustering algorithm energy-balanced load analysis Industrial Internet of Things wireless sensor network

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A K-anonymous clustering algorithm based on the analytic hierarchy process

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2019年 59卷 76-83页

作者： Wang, Kun Zhao, Wei Cui, Junjie Cui, Yanpeng Hu, Jianwei Xidian Univ Sch Cyber Engn Xian 710071 Shaanxi Peoples R China

To protect the privacy of users, tables generally must be anonymized before publication. All existing anonymous methods have deficiencies. They do not consider the differences in attributes, or the optimization of information loss and time efficiency. his paper proposes a new method called KACM to realize k-anonymity. This method is mainly used for hybrid tables. The calculation of the distance between records considers the connection between quasi-identifier attributes and sensitive attributes, their effect on the sensitive privacy, and the information loss during the anonymity process. In the clustering process, the records with the minimum distance are always selected to add, and the clustering is individually controlled according to k to realize the equalization division of the equivalence class and reduce the total amount of distance calculation. Finally, the validity and practicability of the method are proved using theory and experiment. (C) 2019 Elsevier Inc. All rights reserved.

关键词： clustering algorithm Analytic hierarchy process

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A Stable clustering algorithm Based on Link Stability for Mobile Ad Hoc Networks

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JOURNAL OF INTERNET TECHNOLOGY 2008年第3期9卷 257-266页

作者： Sheu, Pi-Rong Wang, Chia-Wei Natl Yunlin Univ Sci & Technol Dept Elect Engn Yunlin Taiwan

Recently, extensive research efforts have been devoted to the design of efficient clustering algorithms to divide all the nodes in a mobile ad hoc network into multiple clusters to form a clustered architecture. A clustered architecture is more stable if it can hold for a longer period of time. In a clustered architecture, due to node mobility, a node may depart from its original cluster and enter another cluster dynamically. Such a change may cause the clustered architecture to be reconfigured, leading to the instability of the network. Frequent information exchanges among the participating nodes and re-computation of clusters involve high communication and computation overheads. Therefore, it is obvious that a more stable clustered architecture will directly lead to the performance improvement of the whole network. In this paper, we propose an efficient clustering algorithm that can establish a more stable clustered architecture by keeping a node with many weak links from being selected as a clusterhead. Computer simulations show that the clustered architectures generated by our clustering algorithm are more stable than those generated by other clustering algorithms.

关键词： Clustered Architecture clustering algorithm Link Stability Mobile Ad Hoc Network

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