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A New clustering-Based Approach for Target Tracking to Optimize Energy Consumption in Wireless Sensor Networks

WIRELESS PERSONAL COMMUNICATIONS 2020年第4期114卷 3337-3349页

作者： Hosseini, Rahim Mirvaziri, Hamid Shahid Bahonar Univ Comp Engn Dept Kerman Iran

The emergence of wireless sensor networks is counted among the most significant achievements of the late 20's. Nowadays, by the ever increasing utilization of new technologies, accessibility of distributed processing technologies and the rising demand for these technologies in regions unreachable or hard-to-reach for humans, their application is greatly expanded. In this study, the objective application is target tracking in protected zones. In this respect, tracking and accurate position reporting of targets in the defined time unit is the main objective. Nevertheless, one of the challenges in these networks is their short lifetime. This is due to the energy limitation of the power supply in each unit. The topic of target tracking in wireless sensor networks is of great importance because on one hand, for their lack of recharge ability, sensor nodes get depleted quite quickly, while on the other hand, losing the target is a highly undesirable event that may severely hamper the functionality of the network. Thus, we are searching for a solution that minimizes tracking errors. In this paper, we propose a strategy for target tracking in wireless sensor networks that is able to generate the least possible tracking error while consuming the minimum energy.

关键词： Target tracking k-means clustering algorithm Wireless sensor networks Energy optimization

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Hybrid clustering algorithms with GRASP to Construct an Initial Solution for the MVPPDP

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Computers, Materials & Continua 2020年第3期62卷 1025-1051页

作者： Abeer I.Alhujaylan Manar I.Hosny Computer Science Department College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia College of Computer Qassim UniversityBuraydahSaudi Arabia

Mobile commerce(m-commerce)contributes to increasing the popularity of electronic commerce(e-commerce),allowing anybody to sell or buy goods using a mobile device or tablet anywhere and at any *** demand for e-commerce increases tremendously,the pressure on delivery companies increases to organise their transportation plans to achieve profits and customer *** important planning problem in this domain is the multi-vehicle profitable pickup and delivery problem(MVPPDP),where a selected set of pickup and delivery customers need to be served within certain allowed trip *** this paper,we proposed hybrid clustering algorithms with the greedy randomised adaptive search procedure(GRASP)to construct an initial solution for the *** approaches first cluster the search space in order to reduce its dimensionality,then use GRASP to build routes for each *** compared our results with state-of-the-art construction heuristics that have been used to construct initial solutions to this *** results show that our proposed algorithms contribute to achieving excellent performance in terms of both quality of solutions and processing time.

关键词： Multi-vehicle profitable pickup and delivery problem k-means clustering algorithm ant colony optimisation greedy randomised adaptive search procedure metaheuristic algorithms

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A Lightweight Mutual Privacy Preserving k-means clustering in Industrial IoT

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IEEE TRANSACTIONS ON NETWORk SCIENCE AND ENGINEERING 2024年第2期11卷 2138-2152页

作者： Hu, Chunqiang Liu, Jianshuo Xia, Hui Deng, Shaojiang Yu, Jiguo Chongqing Univ Sch Big Data & Software Engn Chongqing 400030 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400030 Peoples R China Ocean Univ China Coll Comp Sci & Technol Qingdao 266100 Peoples R China Qilu Univ Technol Big Data Inst Jinan 250316 Peoples R China

In many industrial Internet of Things application schemes, participants, such as IoT devices, often share information with each others for analysis and categorization purpose. Through the analysis and statistical processing of the information, some necessary information is exchanged, e.g., the attributes in manufacturing IoT devices' gathered data. In this context, many devices are necessarily trusted when sharing information, which raises concerns about data privacy leakage. As the number of IoT devices in the network increases, the scale of pairwise keys increases rapidly. Furthermore, the limitation of device's computing ability makes it hard to perform centralized computing. To address these issues, we propose a light weight k-means clustering scheme that performs clustering with high accuracy, maintaining at a low key management cost, and securing the private attributes of each participant or the intermediate variables. In our proposed scheme, we use aprivate optimal initial cluster center generation algorithm based on attribute weights, in order to achieve the better clustering quality. Secondly, we securely find the nearest clustering center for each participant. We dynamically split and merge clustering centers respectively, in order to meet the optimal clustering result. Last but not least, we calculate the clustering centers without revealing any private attributes about the participants' information during the process. The analysis indicates that our proposed scheme canresist collusion attacks and ensures that the cost of overall pair wisekey management and computation cost performed in data center are relatively low.

关键词： k-means clustering algorithm privacy preservation lightweight clustering algorithm homomorphic encryption

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Automatic data clustering using nature-inspired symbiotic organism search algorithm

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kNOWLEDGE-BASED SYSTEMS 2019年第Jan.1期163卷 546-557页

作者： Zhou, Yongquan Wu, Haizhou Luo, Qifang Abdel-Baset, Mohamed Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Key Lab Guangxi High Sch Complex Syst & Computat Nanning 530006 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China Zagazig Univ Fac Comp & Informat El Zera Sq Zagazig 44519 Sharqiyah Egypt

The symbiotic organism search (SOS) is a recently proposed metaheuristic optimization algorithm that simulates the symbiotic interaction strategies adopted by organisms to survive and propagate in an ecosystem. clustering is a popular data analysis and data mining technique, and k-means clustering is one of the most commonly used methods. However, its effectiveness is highly dependent on the initial solution, and the algorithm may become trapped around local optima. In view of these drawbacks of the k-means method, this paper describes the use of the SOS algorithm to solve clustering problems. Ten standard datasets from the UCI Machine Learning Repository are used to evaluate the effectiveness of SOS against that of optimization algorithms including differential evolution, cuckoo search, flower pollination, particle swarm optimization, artificial bee colony, multi-verse optimizer, and k-means. Experimental results show that the SOS algorithm not only achieves superior accuracy, but also exhibits a higher level of stability. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Symbiotic organism search k-means clustering algorithm clustering analysis Metaheuristic optimization

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Segmentation of thermal infrared images of cucumber leaves using k-means clustering for estimating leaf wetness duration

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International Journal of Agricultural and Biological Engineering 2020年第3期13卷 161-167页

作者： Dongmei Wen Aixin Ren Tao Ji Isabel Maria Flores-Parra Xinting Yang Ming Li Beijing Research Center for Information Technology in Agriculture/National Engineering Research Center for Information Technology in Agriculture/National Engineering Laboratory for Agri-product Quality Traceability/National Meteorological Service Center of Urban Agriculture Beijing 100097China College of Plant Protection China Agricultural UniversityBeijing 100193China Department of Sustainable Crop Production UniversitàCattolica del Sacro Cuore29122 PiacenzaItaly Department of Informatics University of AlmeriaAlmeriaCP04120Spain

Leaf wetness duration(LWD)is a critical parameter used to predict plant disease,but its determination under actual field conditions is a major *** this study,a method for determining LWD using thermal infrared imaging was developed and applied to cucumber plants grown in a solar *** images of the plant leaves were captured using an infrared scanning camera,and a leaf wetness area segmentation method consisting of two procedures was ***,a color space conversion was performed automatically by an image-processing ***,the k-means clustering algorithm was applied to enable the segmentation of the wetness area on the thermal ***,to enable overall thermal image analysis,an initial leaf wetness threshold(LWT)of 5%was defined(where wetness values higher than 5%indicated that the leaf was in a wet state).The results of comparative experiments conducted using thermal images of plant leaves captured using an infrared scanning camera and human visual observation indicated that the estimated LWD values were generally higher than the observed LWD values,because slight leaf wetness condensations were overlooked by the human eye but detected by the infrared scanning *** these differences were not found to be statistically significant in this study,the proposed method for determining LWD using thermal infrared imaging may provide a new LWD detection method for cucumber and other plants grown in solar greenhouses.

关键词： thermal imaging k-means clustering algorithm leaf wetness duration cucumber

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POLSAR Image Classification via clustering-WAE Classification Model

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IEEE ACCESS 2018年 6卷 40041-40049页

作者： Xie, Wen Xie, Ziwei Zhao, Feng Ren, Bo Xian Univ Posts & Telecommun Minist Publ Secur Key Lab Elect Informat Proc Applicat Scene Invest Xian 710121 Shaanxi Peoples R China Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Shaanxi Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710126 Shaanxi Peoples R China

Considering the clustering algorithms could explore the label information automatically, this paper proposes a new method in terms of polarimetric synthetic aperture radar (POLSAR) image classification, which named a clustering-wishart-auto-encoder (WAE) classification model. With considering the statistical distribution characteristic of the POLSAR image, the WAE classification model, which proposed by ourselves, could improve the classification performance of the POLSAR image to some extent. The clustering-WAE classification model, that embedded the k-means clustering algorithm into the objective function of the WAE model, has the ability to improve the network performance. Our proposed method could minimize the difference of intra-class data and maximize the difference of inter-class data, from which the obtained POLSAR image features will be more compact to their corresponding cluster centers. Via simultaneously considering the compactness and statistical distribution of data, our method is capable of improving the POLSAR image classification results. The effectiveness of our proposed classification model has been demonstrated on four real POLSAR data sets.

关键词： POLSAR image classification k-means clustering algorithm AE network WAE classification model clustering-WAE classification mode

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The FRCk clustering algorithm for determining cluster number and removing outliers automatically

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2021年第5期24卷 485-494页

作者： Guo, Yubin Wu, Yuhang Zhang, Xiaopeng Bo, Aofeng Li, Ximing South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China South China Agr Univ Coll Software Engn Guangzhou 510642 Peoples R China South China Agr Univ Informat & Network Serv Ctr Guangzhou 510642 Peoples R China Guangzhou HolandAI Technol Co Ltd Guangzhou 510006 Peoples R China

clustering algorithm is one of the most popular unsupervised algorithms for data grouping. The k-means algorithm is a popular clustering algorithm for its simplicity, ease of implementation and efficiency. But for k-means algorithm, the optical cluster number is difficult to predict, while it is sensitive to outliers. In this paper, we divide outliers into two types, and then prompt a clustering algorithm to remove the two-type outliers and calculate the optimal cluster number in each clustering iteration. The algorithm is a fusion of rough clustering and k-means, abbreviated as FRCk algorithm. In the FRCk algorithm, outliers are removed precisely, therefore the optical cluster number can be more accurate, and the quality of clustering result can be improved accordingly. And this algorithm is proven effective by experiment.

关键词： clustering k-means clustering algorithm optical cluster number outlier

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Residential Building Duration Prediction Based on Mean clustering and Neural Network

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ADVANCES IN CIVIL ENGINEERING 2024年第1期2024卷

作者： Ji, Fanrong Nan, Yunquan Wei, Aifang Fan, Peiyan Luo, Zhaoyuan Song, Xiaoqing Shandong Jianzhu Univ Sch Management Engn Jinan 250101 Peoples R China Shandong Jianzhu Univ Coll Surveying & Geoinformat Jinan 250101 Peoples R China Univ Canberra Sch Design & Built Environm Canberra 2601 Australia

The duration of a residential building project will directly influence its successful implementation;hence, it is essential to estimate a reasonable timeframe. In this study, a genetic algorithm (GA) was employed to optimize and refine the weights and thresholds of a back propagation (BP) neural network, thereby creating a GA-BP neural network model. A dataset comprising 111 instances of residential building durations was gathered, segmented into 90 training sets and 21 test sets. The model was validated and assessed through root mean square error (RMSE), correlation coefficient (R), and average error rate, demonstrating that the GA-BP neural network model is effective in predicting the duration of residential buildings. To enhance the predictive accuracy of the GA-BP neural network model, this research utilized an artificial bee colony (ABC)-improved k-means clustering algorithm to categorize 111 experimental datasets and 33 new datasets. The results indicated that the ABC-k-means-GA-BP model exhibited robust generalization capabilities and high predictive accuracy, with the fitness function showing optimal performance after 10, 15, and 35 generations, and the best validation performances recorded as 0.0019156, 0.00035905, and 0.0036914. This validates that the proposed ABC-k-means-GA-BP neural network model significantly aids in forecasting the construction period of residential buildings, which holds substantial practical value for enhancing construction efficiency.

关键词： artificial bee colony algorithm BP neural network duration prediction genetic algorithm k-means clustering algorithm

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Optimizing the eigenvectors of spectral data with an initial clustering approach: Spectral versus colorimetric criteria

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COLOR RESEARCH AND APPLICATION 2022年第4期47卷 866-877页

作者： Mahbadi, Ali Akbar Amirshahi, Seyed Hossein Amirkabir Univ Technol Dept Text Engn Tehran Polytech 350 Hafez Ave Tehran 15875 Iran

clustering of spectral data based on the correlation distance coefficient of the reflectance spectra and the color difference values is presented to minimize the information loss caused by the compression of spectral data when the first three most important eigenvectors are considered. The suggested criteria used a distance function to partition the reflectance spectra of Munsell chips into subgroups using a k-means algorithm. The cumulative energy contributions of the first three eigenvectors of the groups are calculated and compared to those that are computed based on the 10 principal hues of Munsell color system as well as nonclustered spectra. The performance of the suggested methods is evaluated by the colorimetric and spectrophotometric errors between the ground truth and the reconstructed spectra. While the clustering methods yield significantly better results than nonclustered data, the clustering approach based on the correlation distance coefficient of spectral data provides the best overall performance for all considered measures compared to colorimetric clustering strategies.

关键词： color difference value correlation distance k-means clustering algorithm principal component analysis spectral reconstruction

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An improved method for k-means clustering based on internal validity indexes and inter-cluster variance

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2022年第3期25卷 253-261页

作者： Zhu, Guangli Li, Xiaoqing Zhang, Shunxiang Xu, Xin Zhang, Biao Anhui Univ Sci & Technol Sch Comp Sci & Engn Huainan Peoples R China

It is difficult to get the best cluster number of k-means clustering algorithm by using the traditional internal validity indexes. Therefore, a good clustering result cannot be obtained usually. To solve this problem, this paper proposes an improved method for k-means clustering based on internal validity indexes and inter-cluster variance. Firstly, some integers distributed in the interval [2, root n] are selected as initial cluster numbers. Further, each initial cluster number is selected to carry out k-means clustering and obtain a clustering result. Secondly, two initial cluster numbers k(D) and k(C) are extracted respectively under two optimal validity index values (i.e., DB and CH). The k(D) and k(C) are extracted by comparing and analysing the validity index values. Finally, the validity index ICS-VAR is proposed to select the best cluster number k(B), while k(D) is not equal to k(C). Experimental results show that the improved method can obtain a better cluster number under a certain condition.

关键词： k-means clustering algorithm internal validity indexes inter-cluster variance initial cluster number

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