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User Security-Oriented Information-Centric IoT Nodes clustering With Graph Convolution Networks

IEEE INTERNET OF THINGS JOURNAL 2024年第5期11卷 8311-8326页

作者： Gharib, Anastassia Ibnkahla, Mohamed Princess Sumaya Univ Technol Dept Comp Engn Amman 11941 Jordan Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

Information-centric Internet of Things (IoT) sensor networks allow users to access data directly from the sensing layer. This is done through cluster heads (CHs), which are selected as a result of IoT nodes' clustering. To respond to users' data requests, CHs aggregate, encrypt, and store locally sensed data in rounds. For data encryption, security resources are allocated to sensor nodes every round. To satisfy user security needs, very often, security resources are overutilized leading to higher energy consumption and shorter network lifetime. Meanwhile, sensor nodes' and users' mobility may result in link failures. Therefore, efficient clustering and security resource allocation is required to ensure users' data and security needs are satisfied while optimizing network resource utilization. Graph convolution networks (GCNs) can help to address this challenge. GCNs perform learning on graphs while considering non-Euclidean nodes' relations and features. Using GCNs, user awareness, and IoT nodes' features can be incorporated into the cluster-based management of mobile information-centric IoT sensor networks. Therefore, this article proposes user-aware clustering with security resource allocation (USRA) using GCNs. In USRA, the proposed clustering algorithm improves communication reliability by optimizing users' and nodes' coverage. Meanwhile, the proposed security resource allocation plan prevents overutilization of security resources by considering user security needs in each cluster. Compared to existing works, USRA achieves lower energy consumption on security while ensuring high user security satisfaction. This promotes a longer network lifetime. USRA further contributes to higher communication reliability and throughput with stable data delivery latency to users.

关键词： Security Resource management clustering algorithms Internet of Things Energy consumption Convolution Encryption clustering graph convolution networks (GCNs) security user satisfaction wireless sensor networks

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Joint Optimization of Power Generation and Voyage Scheduling in Ship Power System Based on Operating Scene clustering and Multitask Deep Reinforcement Learning

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2024年第4期10卷 8432-8442页

作者： Shang, Chengya Fu, Lijun Xiao, Haipeng Bao, Xianqiang Xu, Xinghua Naval Univ Engn Natl Key Lab Electromagnet Energy Wuhan 430033 Peoples R China

The variability in sailing environment and load demand, along with seasonal fluctuations in port electricity prices, presents significant challenges for operating ship power systems (SPSs) in all-electric ships (AESs). However, traditional single-task deep reinforcement learning (DRL) struggles to cope with the high randomness of SPS operation scenarios. This article proposes a novel real-time joint optimization method for power generation and voyage scheduling in SPSs that considers operating scene clustering and multitask DRL (MTDRL). A unified triplet is used to describe operational uncertainty, and the SPS operation scenes are clustered. Then, the importance weighted actor-learner architecture (IMPALA) combined with random network distillation (RND) mechanism, what is called IMPALA-RND algorithm, is applied to minimize operational costs by adjusting power generation and voyage scheduling. The proposed method can achieve differentiated learning for clustered multitask operational scenes and enhance the agent's capability to explore unknown states. A case study is analyzed based on historical operational datasets of four-DG SPS. Numerical results verify the superiority and real-time performance of the proposed algorithm for joint optimization in multitask operation scenarios.

关键词： Optimization clustering algorithms Costs Multitasking Marine vehicles Power systems Generators Energy management strategy multitask deep reinforcement learning (MTDRL) random network distillation (RND) scene clustering ship power systems (SPSs)

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clustering-Based algorithms to Semantic Summarizing Graph with Multi-attributes’ Hierarchical Structures

Clustering-Based Algorithms to Semantic Summarizing Graph wi...

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IEEE International Conference on e-Business Engineering (ICEBE)

作者： Chong Sun Xiantao Cai YiRan Hu Wen Ying Chen Jun Tie Computer Science department South-Central University For Nationalities Wuhan China Computer Science department Wuhan University Wuhan China

ISBN: (纸本)9781509061204

K-SGS is a novel graph summarization method which solves the scale limits. By using the concept hierarchy of the nodes' attributes, K-SGS can group the nodes in a flexible way. It groups the nodes not only with same values but also with similar values. Besides the edges' information loss, it also considers the nodes' information loss during the summarization and model the summarization as multi-objective planning. We proposal two hierarchical agglomerative algorithms, one is based on forbearing stratified sequencing method and the other is based on unified objective function method. The experiment on real life dataset shows that our methods can solve the problem and get the graph summaries with good quality.

关键词： Loss measurement Semantics clustering algorithms Computer science Electronic mail Planning Conferences

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Comparative analysis of K-Means with other clustering algorithms to improve search result

Comparative analysis of K-Means with other clustering algori...

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International Conference on Green Computing and Internet of Things (ICGCIoT)

作者： Shashi Mehrotra Shruti Kohli Birla Institute of Technology Mesra India

The paper identifies the scope of improvement for the search result of a web site. The study includes some commonly used clustering algorithms to identify the usage of clustering approach for improving web elements analysis, in various ways. As the Search result option is extensively used at almost every web site, the main focus is to optimize search result of a web site using clustering approach. Sementic web using the concept of ontology is included, to retrieve more relevant and meaning full serach results. Some most commomly used algorithms are experimented using web data, and it is observed that K-Means clustering algorithm gives best result in term of accuracy and speed. Thus the proposed hybrid model will be using K-Means and Genetic algorithm to overcome the drawbacks of K-Means. The evaluation parameters; accuracy in terms of objects placement in correct cluster, relevancy, speed and user satisfaction are the main parameters considered for the study.

关键词： clustering algorithms Algorithm design and analysis Partitioning algorithms Social network services Data analysis Web sites Internet

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A Density clustering-Based CFAR Algorithm for Ship Detection in SAR Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Li, Yang Wang, Zeyu Chen, Hongmeng Li, Yachao Beijing Univ Posts & Telecommun Sch Informat & Commun Engn State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Inst Radio Measurement Beijing 100854 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

The clutter selection strategy based on sliding window in the conventional constant false alarm rate (CFAR) algorithm leads to different clutter qualities between pixels of the same target in a complex environment. To solve the problem, this letter proposes an improved CFAR algorithm based on density clustering. First, a two-parameter CFAR is used to detect ship targets. Then, density clustering is performed on each detected target pixel based on spatial distance and detection threshold to improve the target detection accuracy. Finally, false alarms caused by speckle noise are eliminated by using the number of times a pixel is clustered. The experimental results show that compared with the conventional CFAR algorithm and the superpixel-level CFAR detectors for ship detection in synthetic aperture radar (SAR) imagery (SP-CFAR), the proposed algorithm achieves a detection accuracy improvement of over 14.8% in heterogeneous clutter scenarios and dense target scenarios, while maintaining a low false alarm rate no higher than 0.13% in strong noise environments.

关键词： clustering algorithms Clutter Marine vehicles Radar polarimetry Signal processing algorithms Detectors Noise Constant false alarm rate (CFAR) density clustering ship detection synthetic aperture radar (SAR)

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Energy-Efficient clustering in WSNs Using Multi-Objective Genetic algorithms

Energy-Efficient Clustering in WSNs Using Multi-Objective Ge...

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Smart Systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： P. Sivaprakash Haritha K Kondalarao Punati D. Shyamala Devi R. R. Ramya M. Perarasi Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of AIADS St Joseph's College Of Engineering Semmancheri Chennai Department of Electronics and Communication Engineering Hyderabad Institute of Technology and Management Telangana India Department of Information Technology Annai College of Engineering and Technology Kumbakonam India Department of Information Technology University College of Engineering Nagercoil India Department of Electronics and Communication Engineering R.M.K. Engineering College Tamil Nadu India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

This research proposes a Multiobjective Genetic Algorithm (MOGA) for energy efficient clustering in Wireless Sensor Networks (WSN). clustering is a key tactic for improving network scalability and energy efficiency in WSNs since it lowers communication overhead and lengthens network lifetime. The proposed MOGA addresses this by optimizing several goals at once, such as improving network longevity, decreasing energy usage, and guaranteeing consistent energy distribution among sensor nodes. Under the guidance of a Pareto-based methodology, the algorithm evolves possible solutions using genetic operators including crossover, mutation, and selection in order to find a set of ideal trade-offs. The effectiveness of the proposed strategy is demonstrated by simulation results showing significant improvements in energy efficiency and network lifetime compared to conventional clustering methods. In large-scale WSNs, this method offers a reliable and expandable option for energy-efficient clustering. A MOGA was used to achieve a 93.5% efficiency.

关键词： Wireless sensor networks Scalability Heuristic algorithms Taxonomy clustering algorithms Smart systems Energy efficiency Real-time systems Reliability Genetic algorithms

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Research on degree of video completion of Internet videos with clustering algorithms

Research on degree of video completion of Internet videos wi...

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International Conference on Control, Automation and Information Sciences (ICCAIS)

作者： Jiawei He Yingyun Yang Anming Wei Wenhui Dong Communication University of China Beijing China State Administration of Press Publication Radio Film and Television Academy of Broadcasting Planning Beijing China

ISBN: (纸本)9781479998937

clustering algorithms are some of the most important algorithms used to describe data attributes and very effective ways to mine and analyze big data set. In this paper, the whole description of vector space of Internet users is acquired by clustering and analyzing user behavior data set. Moreover, all users are divided into different clusters according to KPI which is to correlate different users in terms of their degree of video completion. Our research shows that KPI of total length correlates degree of completion better than other KPIs. This KPI is drastically negatively correlated with user degree of video completion. Finally, we compare the accuracy and efficiency of three different algorithms which we used to cluster our research data in this paper.

关键词： clustering algorithms Streaming media Algorithm design and analysis Correlation clustering methods Internet TV

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An innovative clustering approach utilizing frequent item sets

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Multimedia Tools and Applications 2025年第10期84卷 7835-7861页

作者： Manzali, Youness Barry, Khalidou Abdoulaye Flouchi, Rachid Balouki, Youssef Elfar, Mohamad LPAIS Laboratory Faculty of Sciences Sidi Mohamed Ben Abdellah University Fez Morocco Laboratory of Microbial Biotechnology and Bioactive Molecules Science and Technologies Faculty Sidi Mohamed Ben Abdellah University Fez Morocco Science and Technologies Faculty University Hassan 1st Settat Morocco

clustering is a method in data mining that belongs to the category of unsupervised learning. Cluster analysis categorizes data into different classes by identifying the internal organization of objects in the data set and their relationships. Many clustering methods are designed with specific assumptions about the underlying data distribution or cluster shapes. If these assumptions do not hold in a particular dataset, the performance of the clustering algorithm may suffer. This paper introduces a novel frequency clustering method called CFI (clustering based on Frequent Itemsets). This innovative approach opens up a new avenue for research in frequency clustering, departing from conventional distance-based methods. CFI has the potential to reveal compelling patterns or associations among features in the data. The CFI algorithm includes three main steps. Firstly, we generate frequent item sets. Secondly, we built the centroids of each cluster based on a new measure called FI-distance, which combines the Euclidian distance with a similarity measure for item sets. Third, each object is assigned to the appropriate cluster based on its membership degree. Various experiments were conducted on synthetic and real-world datasets, utilizing three performance criteria: the Davies Bouldin score, the silhouette width criterion, and the Calinski-Harabasz score. The CFI method was initially compared to state-of-the-art methods and subsequently to automatic clustering methods. The results indicate the superiority of the CFI algorithm compared to the other methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： clustering algorithms

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Brain storm optimization algorithms with k-medians clustering algorithms

Brain storm optimization algorithms with k-medians clusterin...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Haoyu Zhu Yuhui Shi Xi'an Jiaotong-Liverpool University Suzhou China

Brain storm optimization (BSO) algorithm is a novel swarm intelligence algorithm inspired by human beings' brainstorming process in problems solving. Generally, BSO algorithm has five main steps, which are initialization, evaluation, clustering, disruption and updating. In these five steps, the clustering step is critical to BSO algorithms. Original BSO algorithms use k-means methods as clustering algorithms, but k-means algorithm is affected by extreme values easily and the speed of algorithm is not high enough. In this paper, a variation of k-means clustering algorithm, called k-medians clustering algorithm, is investigated to replace k-means clustering algorithm. In addition, one modification is applied to both clustering algorithms, which is to replace the calculated cluster center with an individual closest to it. Experimental results show that the effectiveness of BSO does not change obviously, but the higher efficiency can be obtained.

关键词： clustering algorithms Noise

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Word clustering algorithms Based on Word Similarity

Word Clustering Algorithms Based on Word Similarity

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International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Lichi Yuan School of Information Technology Jiangxi University of Finance & Economics Nanchang China

ISBN: (纸本)9781479986477

Category-based statistical language model is an important method to solve the problem of sparse data, but there are two bottlenecks about this model: (1) the problem of word clustering, it is hard to find a suitable clustering method that has good performance and has not large amount of computation. (2) class-based method always loses some prediction ability to adapt the text of different domain. In order to solve above problems, a definition of word similarity by utilizing mutual information is presented. Based on word similarity, the definition of word set similarity is given. Experiments show that word clustering algorithm based on similarity is better than conventional greedy clustering method in speed and performance, the perplexity is reduced from 283 to 218.

关键词： clustering algorithms Data models Predictive models Adaptation models Semantics Algorithm design and analysis Probabilistic logic

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