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Energy-efficient scalable routing algorithm based on hierarchical agglomerative clustering for Wireless Sensor Networks

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ALEXANDRIA ENGINEERING JOURNAL 2025年 120卷 95-105页

作者： Chai, Xuguang Wu, Yalin Feng, Lei Hebei Vocat Univ Technol & Engn Dept Informat Engn Xingtai 054000 Peoples R China Hong Kong Univ Sci & Technol Peking Univ Shenzhen Peking Univ Shenzhen HospDept UltrasoundInst Ultrason MedMe Shenzhen 518036 Peoples R China

In hierarchical Wireless Sensor Networks (WSNs), the unbalanced energy consumption caused by multi-hop routing poses requirements for the cluster head's selection and distribution of clusters. Aiming to improve the network lifetime and achieve the balance of energy consumption among sensor nodes, we propose an Energy efficient Scalable Routing algorithm based on hierarchical agglomerative clustering (ESR-HAC) for WSNs by jointly optimizing the cluster formation and energy efficiency of inter-cluster communication. Firstly, to reduce the transmission cost and time/space complexity during the clustering process, a hierarchical clustering method is proposed to achieve a reasonable cluster distribution. Secondly, by taking into account of multiple parameters such as the coverage range, connectivity and remaining energy of sensor nodes, a cost function for cluster head's selection is defined to reduce the communication overhead between member nodes and cluster heads. Furthermore, to resolve the hot spot problem caused by inter-cluster data forwarding, a genetic algorithm is introduced to obtain the optimal inter cluster routing to balance the network energy consumption. The experimental results show that our algorithm can obtain better energy balance of sensor nodes, ensure the effectiveness of data delivery and extend network lifetime.

关键词： Wireless Sensor Networks Cluster head hierarchical agglomerative clustering Energy-efficient scalable routing algorithm

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hierarchical agglomerative clustering Algorithm method for distributed generation planning

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年第Mar.期56卷 259-269页

作者： Vinothkumar, K. Selvan, M. P. ABB Global Ind & Serv Ltd Madras 600089 Tamil Nadu India Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli 620015 Tamil Nadu India

Planning of distributed generator (DG) units in the distribution system employing hierarchical agglomerative clustering Algorithm (HACA) is proposed in this paper. The proposed method overcomes the dependency of existing methods of DG placement, either on the entire global preference information or on the experience of the distribution system planner. The proposed method is validated with weighted sum method and its effectiveness is tested using two distribution systems of different size and configuration. The results of the simulation study demonstrate the suitability of proposed method in solving the problem involving multiple objectives in DG planning studies. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Distributed generator (DG) planning Performance indices Multi-objective function hierarchical agglomerative clustering Algorithm (HACA)

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hierarchical agglomerative clustering with Ordering Constraints

Hierarchical Agglomerative Clustering with Ordering Constrai...

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3rd International Conference on Knowledge Discovery and Data Mining

作者： Zhao, Haifeng Qi, ZiJie Univ Calif Davis Dept Comp Sci Davis CA 95616 USA

ISBN: (纸本)9780769539232

Many previous researchers have converted background knowledge as constraints to obtain accurate clustering. These clustering methods are usually called constrained clustering. Previous ordering constraints are instance level non-hierarchical constraints, like must-link and cannot-link constraints, which do not provide hierarchical information. In order to incorporate the hierarchical background knowledge into agglomerative clustering, we extend instance-level constraint to hierarchical constraint in this paper. We name it as ordering constraint. Ordering constraints can be used to capture hierarchical side information and they allow the user to encode hierarchical knowledge such as ontologies into agglomerative algorithms. We experimented with ordering constraints on labeled newsgroup data. Experiments showed that the dendrogram generated by ordering constraints is more similar to the pre-known hierarchy than the dendrogram generated by previous agglomerative clustering algorithms. We believe this work will have a significant impact on the agglomerative clustering field.

关键词： hierarchical agglomerative clustering constrained clustering ordering constraint

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Raman, FTIR, and UV-Vis Spectroscopic Investigation of Some Oils and Their hierarchical agglomerative clustering (HAC)

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FOOD ANALYTICAL METHODS 2023年第7期16卷 1237-1251页

作者： Motahari, Hamid Mousavi, Seyedeh Somayeh Haghighi, Parvaneh Yazd Univ Dept Phys POB 89195-741 Yazd Iran Amirkabir Univ Technol Fac Math & Comp Sci Dept Stat Tehran Iran

Eight edible and non-edible oils have been characterized using UV-Vis absorption and transmission, FTIR, and Raman spectroscopy. Oils' molecular structure, characteristics, and type of bonds have been analyzed separately. Black seed oil and Olive oil have the maximum number of absorbance peaks in the UV-Vis range. There are absorbance peak positions in the range of 250 to 350 nm within all samples. Moreover, FTIR vibrational modes related to molecular structures have been characterized. Also, hierarchical agglomerative clustering was done using the Euclidean metric and the Ward linkage method. Cluster dendrogram trees were drawn based on the UV-Vis transmission and FTIR spectral data, which show four clusters for these eight types of oils. There are two distinct regions in the Raman spectra of edible oils. The first region is 1000-1800 cm(-1) as the fingerprint region. The second one is about 2500 to 4200 cm(-1). clustering of Raman spectral data shows two main clusters: edible vegetable and non-edible oils. Also, the first cluster has two distinctive sub-clusters, including cooking oils and medical skin care oils. It shows that the Raman spectra can effectively separate the oils in this clustering method. It means that the molecular structure, transparency, colors, densities, chemical bonding, and other features of oils can be classified by this clustering method. Therefore, spectroscopic data clustering can use for more accurate oil diagnosis and oil classification. In conclusion, the hierarchical agglomerative clustering can be used effectively as a complementary method alongside UV-Vis, FTIR, and Raman spectroscopy.

关键词： Oil UV-Vis Raman FTIR hierarchical agglomerative clustering

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Quantum hierarchical agglomerative clustering Based on One Dimension Discrete Quantum Walk with Single-Point Phase Defects

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Computers, Materials & Continua 2020年第11期65卷 1397-1409页

作者： Gongde Guo Kai Yu Hui Wang Song Lin Yongzhen Xu Xiaofeng Chen College of Mathematics and Informatics Fujian Normal UniversityFuzhou350007China School of Computing and Mathematics University of UlsterNorthern IrelandBT370QBUK School of Electronic Information Science Fujian Jiangxia UniversityFuzhou350108China

As an important branch of machine learning,clustering analysis is widely used in some fields,e.g.,image pattern recognition,social network analysis,information security,and so *** this paper,we consider the designing of clustering algorithm in quantum scenario,and propose a quantum hierarchical agglomerative clustering algorithm,which is based on one dimension discrete quantum walk with single-point phase *** the proposed algorithm,two nonclassical characters of this kind of quantum walk,localization and ballistic effects,are *** first,each data point is viewed as a particle and performed this kind of quantum walk with a parameter,which is determined by its *** that,the particles are measured in a calculation *** terms of the measurement result,every attribute value of the corresponding data point is modified *** this way,each data point interacts with its neighbors and moves toward a certain center *** last,this process is repeated several times until similar data points cluster together and form distinct *** experiments on the synthetic and real world data demonstrate the effectiveness of the presented *** with some classical algorithms,the proposed algorithm achieves better clustering ***,combining quantum cluster assignment method,the presented algorithm can speed up the calculating velocity.

关键词： Quantum machine learning discrete quantum walk hierarchical agglomerative clustering

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HACCR: hierarchical agglomerative clustering-Based Connectivity Restoration Approach in Mobile Wireless Sensor and Robot Networks

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WIRELESS PERSONAL COMMUNICATIONS 2024年第3期138卷 1773-1798页

作者： Mogharabi, Alireza Taghizadeh, Alireza Manochehri, Kooroush Islamic Azad Univ Dept Comp Engn Parand Branch Parand Iran Amirkabir Univ Technol Dept Comp Engn Garmsar Campus Garmsar Iran

Wireless sensor networks consist of numerous sensor nodes that monitor and send environmental data, such as temperature measurements and motion detection information, to a central station. Due to the nature of hop-by-hop data transmission in such networks, the failure of several nodes can adversely affect the functionality of the remaining healthy nodes, making the connection to the central station impossible. This study introduces the hierarchical agglomerative clustering-Based Connectivity Restoration (HACCR) approach to address the problem of network reconnection after node failures. HACCR operates in two phases: backbone formation and recovery process. In the backbone formation phase, nodes exchange data packets to determine their locations and run the hierarchical agglomerative clustering algorithm to form clusters. The clusters' geometric centers are then used to construct an Equivalent Graph, from which a Minimum Spanning Tree (MST) is derived using the Kruskal algorithm. In the recovery process, mobile nodes are deployed to restore connectivity by moving towards the cluster centers and establishing communication links along the MST. HACCR's performance is validated through comprehensive simulation studies, considering metrics such as the number of deployed mobile nodes, total traveled distance, and maximum distance traveled. The results demonstrate that HACCR outperforms baseline approaches in terms of efficiency and effectiveness.

关键词： Mobile wireless sensor networks (MWSN) Connectivity restoration hierarchical agglomerative clustering Network recovery Machine learning Distributed algorithm

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Identification of coherent trajectories by modal characteristics and hierarchical agglomerative clustering

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ELECTRIC POWER SYSTEMS RESEARCH 2018年 158卷 170-183页

作者： Arrieta Paternina, Mario R. Zamora-Mendez, Alejandro Ortiz-Bejar, Jose Chow, Joe H. Ramirez, Juan M. Natl Autonomous Univ Mexico UNAM Mexico City 04510 Mex Mexico Michoacan Univ St Nicholas Hidalgo UMSNH Morelia 58030 Mich Mexico Ctr Invest & Innovat Tecnol Informac & Comunicac Aguascalientes 20313 Ags Mexico Rensselaer Polytech Inst Troy NY 12180 USA CINVESTAV Guadalajara 45019 Jal Mexico

This paper introduces a novel method to identify coherent generators using the inter-area modal characteristics of power systems. The key idea is to extract the inter-area modes from the simulated data and then to apply a clustering strategy. Thus, the proposed method consists of extracting the phase of the oscillatory modes via a modal identification technique and applying a hierarchical agglomerative clustering technique together with the Elbow's method to gather the phases of each mode, enabling to provide coherent trajectories of generators. The proposed method uses a Taylor-Fourier filtering strategy to remove noises and nonlinearity in the time evolution of coherent generators. Simulated signals with noise added are used for assessing the proposition. Results corroborate the proposed strategy for identifying coherent trajectories in large-scale power systems. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Coherency hierarchical agglomerative clustering Inter-area modes Modal identification Slow coherency

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Using hierarchical agglomerative clustering in wireless sensor networks: An energy-efficient and flexible approach

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AD HOC NETWORKS 2010年第3期8卷 328-344页

作者： Lung, Chung-Horng Zhou, Chenjuan Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

In wireless sensor networks (WSNs), hierarchical network structures have the advantage of providing scalable and resource efficient solutions. To find an efficient way to generate clusters, this paper adapts the well-understood hierarchical agglomerative clustering (HAC) algorithm by proposing a distributed HAC (DHAC) algorithm. With simple six-step clustering, DHAC provides a bottom-up clustering approach by grouping similar nodes together before the cluster head (CH) is selected. DHAC can accommodate both quantitative and qualitative information types in clustering, while offering flexible combinations using four commonly used HAC algorithm methods, SLINK, CLINK, UPGMA, and WPGMA. With automatic CH rotation and re-scheduling, DHAC avoids reclustering and achieves Uniform energy dissipation through the whole network. Simulation results in the NS-2 platform demonstrate the longer network lifetime of the DHAC than the better-known clustering protocols, LEACH and LEACH-C. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Wireless sensor networks clustering-based routing hierarchical agglomerative clustering Energy efficiency

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SDN-Based hierarchical agglomerative clustering Algorithm for Interference Mitigation in Ultra-Dense Small Cell Networks

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ETRI JOURNAL 2018年第2期40卷 227-236页

作者： Yang, Guang Cao, Yewen Esmailpour, Amir Wang, Deqiang Shandong Univ Sch Informat Sci & Engn Jinan Shandong Peoples R China Univ New Haven Dept Elect & Comp Engn & Comp Sci West Haven CT USA

Ultra-dense small cell networks (UD-SCNs) have been identified as a promising scheme for next-generation wireless networks capable of meeting the ever-increasing demand for higher transmission rates and better quality of service. However, UD-SCNs will inevitably suffer from severe interference among the small cell base stations, which will lower their spectral efficiency. In this paper, we propose a software-defined networking (SDN)-based hierarchical agglomerative clustering (SDN-HAC) framework, which leverages SDN to centrally control all sub-channels in the network, and decides on cluster merging using a similarity criterion based on a suitability function. We evaluate the proposed algorithm through simulation. The obtained results show that the proposed algorithm performs well and improves system payoff by 18.19% and 436.34% when compared with the traditional network architecture algorithms and non-cooperative scenarios, respectively.

关键词： hierarchical agglomerative clustering Interference mitigation Software-defined networking Ultra-dense small cell networks

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A hybrid linear text segmentation algorithm using hierarchical agglomerative clustering and discrete particle swarm optimization

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INTEGRATED COMPUTER-AIDED ENGINEERING 2014年第1期21卷 35-46页

作者： Wu, Ji-Wei Tseng, Judy C. R. Tsai, Wen-Nung Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan Chung Hua Univ Dept Comp Sci & Informat Engn Hsinchu Taiwan

Linear text segmentation plays an important role in many natural language processing tasks. Many algorithms have been proposed and shown to improve the performance of linear text segmentation. However, the previous studies often suffer from either lower segmentation accuracy or higher computational complexity. Moreover, parameter setting is another critical problem in some algorithms. Although manual assignment is an approach to solve this problem, it may increase the user's burden, and the parameters provided may not always be suitable to reflect the real metadata of a text. In this paper, a hybrid algorithm, TSHAC-DPSO, is proposed to tackle these problems. A novel linear Text Segmentation algorithm based on hierarchical agglomerative clustering (TSHAC) is proposed to rapidly generate a satisfactory solution without an auxiliary knowledge base, parameter setting, or user involvement;then an efficient evolutional algorithm, Discrete Particle Swarm Optimization (DPSO), is adopted to generate the global optimal solution by refining the solution created by TSHAC. TSHAC-DPSO fully utilizes the merits of both algorithms which not only improve the accuracy of linear text segmentation, but also make the execution more efficient and flexible. The experimental results show that TSHAC-DPSO provides comparable segmentation accuracy with several well-known linear text segmentation algorithms.

关键词： Linear text segmentation hierarchical agglomerative clustering discrete particle swarm optimization natural language processing

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