network clustering
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2022 ◽  
Vol 16 (1) ◽  
pp. 1-34
Yiji Zhao ◽  
Youfang Lin ◽  
Zhihao Wu ◽  
Yang Wang ◽  
Haomin Wen

Dynamic networks are widely used in the social, physical, and biological sciences as a concise mathematical representation of the evolving interactions in dynamic complex systems. Measuring distances between network snapshots is important for analyzing and understanding evolution processes of dynamic systems. To the best of our knowledge, however, existing network distance measures are designed for static networks. Therefore, when measuring the distance between any two snapshots in dynamic networks, valuable context structure information existing in other snapshots is ignored. To guide the construction of context-aware distance measures, we propose a context-aware distance paradigm, which introduces context information to enrich the connotation of the general definition of network distance measures. A Context-aware Spectral Distance (CSD) is then given as an instance of the paradigm by constructing a context-aware spectral representation to replace the core component of traditional Spectral Distance (SD). In a node-aligned dynamic network, the context effectively helps CSD gain mainly advantages over SD as follows: (1) CSD is not affected by isospectral problems; (2) CSD satisfies all the requirements of a metric, while SD cannot; and (3) CSD is computationally efficient. In order to process large-scale networks, we develop a kCSD that computes top- k eigenvalues to further reduce the computational complexity of CSD. Although kCSD is a pseudo-metric, it retains most of the advantages of CSD. Experimental results in two practical applications, i.e., event detection and network clustering in dynamic networks, show that our context-aware spectral distance performs better than traditional spectral distance in terms of accuracy, stability, and computational efficiency. In addition, context-aware spectral distance outperforms other baseline methods.

2022 ◽  
Vol 13 (2) ◽  
pp. 1-14
Ankit Temurnikar ◽  
Pushpneel Verma ◽  
Gaurav Dhiman

VANET (Vehicle Ad-hoc Network) is an emerging technology in today’s intelligent transport system. In VANET, there are many moving nodes which are called the vehicle running on the road. They communicate with each other to provide the information to driver regarding the road condition, traffic, weather and parking. VANET is a kind of network where moving nodes talk with each other with the help of equipment. There are various other things which also make complete to VANET like OBU (onboard unit), RSU (Road Aside Unit) and CA (Certificate authority). In this paper, a new PSO enable multi-hop technique is proposed which helps in VANET to Select the best route and find the stable cluster head and remove the malicious node from the network to avoid the false messaging. The false can be occurred when there is the malicious node in a network. Clustering is a technique for making a group of the same type node. This proposed work is based on PSO enable clustering and its importance in VANET. While using this approach in VANET, it has increased the 20% packet delivery ratio.

Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 150
Sen Peng ◽  
Jing Cheng ◽  
Xingqi Wu ◽  
Xu Fang ◽  
Qing Wu

Pressure sensor placement is critical to system safety and operation optimization of water supply networks (WSNs). The majority of existing studies focuses on sensitivity or burst identification ability of monitoring systems based on certain specific operating conditions of WSNs, while nodal connectivity or long-term hydraulic fluctuation is not fully considered and analyzed. A new method of pressure sensor placement is proposed in this paper based on Graph Neural Networks. The method mainly consists of two steps: monitoring partition establishment and sensor placement. (1) Structural Deep Clustering Network algorithm is used for clustering analysis with the integration of complicated topological and hydraulic characteristics, and a WSN is divided into several monitoring partitions. (2) Then, sensor placement is carried out based on burst identification analysis, a quantitative metric named “indicator tensor” is developed to calculate hydraulic characteristics in time series, and the node with the maximum average partition perception rate is selected as the sensor in each monitoring partition. The results showed that the proposed method achieved a better monitoring scheme with more balanced distribution of sensors and higher coverage rate for pipe burst detection. This paper offers a new robust framework, which can be easily applied in the decision-making process of monitoring system establishment.

2022 ◽  
Vol 19 (3) ◽  
pp. 2381-2402
Danial Sharifrazi ◽  
Roohallah Alizadehsani ◽  
Javad Hassannataj Joloudari ◽  
Shahab S. Band ◽  

<abstract> <p>Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.</p> </abstract>

Valery Romaniuk ◽  
Olexandr Lysenko ◽  
Valery Novikov ◽  
Ihor Sushyn

Background. The article presents the results of a study of methods of positioning, localization and data collection from nodes of a mobile wireless sensor network using intelligent adaptive telecommunication air platforms. To implement the study of this research topic, an analysis of literary sources on this topic was carried out. Based on a fairly rich bibliographic material, this work has the main task of examining, analyzing and systematizing already known approaches to positioning objects in wireless sensor networks using intelligent adaptive telecommunication air platforms and suggesting options for their development. Objective. The aim of the work is to improve the methods of direct data collection of TA from the nodes of BSM, the general directions of synthesis of which are defined in the work. Methods. Methods of cluster analysis (network clustering), graph theory (research of analytical models of BSM with TA functioning, construction of cluster topology), theory of telecommunication networks (when calculating bandwidth in BSM with TA radio channels) and theory were used to solve the formulated problem. (when developing a positioning model for telecommunications air platforms) Results. A technique for evaluating the effectiveness of methods for collecting data from wireless sensor networks using intelligent adaptive telecommunication air platforms is proposed. Conclusions. The method of collecting TA monitoring data from the main nodes of clustered BSM has been improved. The method of estimation of efficiency of methods of data collection with BSM by telecommunication air platforms is offered.

2021 ◽  
Ratri Parida ◽  
Manoj Kumar Dash ◽  
Anil Kumar ◽  
Edmundas Kazimieras Zavadskas ◽  
Sunil Luthra ◽  

2021 ◽  
Vol 11 (23) ◽  
pp. 11448
Ahmed Mahdi Jubair ◽  
Rosilah Hassan ◽  
Azana Hafizah Mohd Aman ◽  
Hasimi Sallehudin ◽  
Zeyad Ghaleb Al-Mekhlafi ◽  

Recently, Wireless Sensor Network (WSN) technology has emerged extensively. This began with the deployment of small-scale WSNs and progressed to that of larger-scale and Internet of Things-based WSNs, focusing more on energy conservation. Network clustering is one of the ways to improve the energy efficiency of WSNs. Network clustering is a process of partitioning nodes into several clusters before selecting some nodes, which are called the Cluster Heads (CHs). The role of the regular nodes in a clustered WSN is to sense the environment and transmit the sensed data to the selected head node; this CH gathers the data for onward forwarding to the Base Station. Advantages of clustering nodes in WSNs include high callability, reduced routing delay, and increased energy efficiency. This article presents a state-of-the-art review of the available optimization techniques, beginning with the fundamentals of clustering and followed by clustering process optimization, to classifying the existing clustering protocols in WSNs. The current clustering approaches are categorized into meta-heuristic, fuzzy logic, and hybrid based on the network organization and adopted clustering management techniques. To determine clustering protocols’ competency, we compared the features and parameters of the clustering and examined the objectives, benefits, and key features of various clustering optimization methods.

Ecosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
Glen A. Sargeant ◽  
Margaret A. Wild ◽  
Gregory M. Schroeder ◽  
Jenny G. Powers ◽  
Nathan L. Galloway

2021 ◽  
Vol 937 (3) ◽  
pp. 032089
K Chernysheva ◽  
N Karpuzova ◽  
S Afanasyeva ◽  
A Korolkova

Abstract The article discusses the capabilities of the Loginom analytical platform for processing long-term field experience data; such software components are used as data transformation (row filter, sorting, grouping, cross-table, cross-diagram, sliding window); preprocessing (editing emissions, smoothing), research (correlation analysis, factor analysis), Data Mining (self-organizing network, clustering) to identify the effect of crop rotations, soil liming, application of various combinations of mineral and organic fertilizers, weather conditions on yield of oats and barley.

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