Clustered Network Model Research Articles

QoS-based energy-efficient hybrid routing protocols in RSU assisted VANET using clustering mechanism

ABSTRACT Vehicular ad hoc networks (VANET) communication framework are employed to improve communication between vehicles on the highway and urban roads. Evaluating the best routing path by satisfying the QoS (Quality of Service) plays a substantial role in networking. Researchers for maximising the routing efficiency have employed numerous methods. But these criteria cannot be attained effectively due to certain limitations, including frequent route failure, high overhead, increased packet loss rate, less throughput and packet delivery rate. Hence, this work presents an effective strategy to offer better communication services with less energy consumption and high flexibility using QoS-based energy efficient routing framework with clustering (QoS_EErcF) network model. Initially, different clusters are formed by considering the constraints like node direction, link reliability, speed and distance. The cluster heads (CH) are chosen from the clusters with the dissemination of gateway nodes and cluster members using an Amended rapid cosine similarity-founded clustering mechanism (Arc_SimC). The best routing strategy for transmitting the data from source to destination is performed by using Integrated Pelican with grasshopper optimisation algorithm (IPel_Ghop). Constraints such as bandwidth, node distance, energy, packet loss rate, network traffic and end-to-end delay are considered to find the optimal path. The results are simulated using MATLAB, whereas the energy efficiency of a proposed model is obtained to be 98.37%, and the packet delivery ratio is attained as 99.60%.

Prolegomena about the cluster-network model organization of the educational system in musical academies of Ukraine

The article „Prolegomena about the cluster-network model organization of the educational system in the musical academies of Ukraine” discusses the complexity of higher musical education and the need for new scientific approaches to address the changing pedagogical reality. The authors propose a cluster-network model for organizing the educational system in musical academies, which is based on the following components: conceptual – tasks, methods, forms, means; axiological– values; psychological and pedagogical – mechanisms of learning values; organizational and management – management of elements of the educational structure. The cluster-network model aims to increase the competitiveness of the educational system by developing the students’ competencies that meet the requirements of the social order. It also emphasizes the importance of spirituality in education and the need for a holistic approach to human development. The authors argue that the ideal of human education is constantly changing, but the ability to correlate the concepts, ideas, and theories acquired during cognitive activity with an individual value system is a key indicator of successful education. The article highlights the importance of understanding the inner class and spiritual being of a person, as well as the need to implement some methods that contribute to penetrating into the phenomenological secrets of human consciousness for researching and revealing the most important mental structures of man. It also discusses the role of dialogue as a way of existing and coexisting- with the „other” in the communication process. The proposed model aims to address the challenges of the postmodern era, where the world is thought of as an open text not limited in its manifestations and interpretations. It emphasizes the importance of continuous education and the integration of knowledge from related disciplines to develop digital education and innovative work practices.

A CLUSTERED FRACTAL DISCRETE FRACTURE NETWORK MODEL FOR FRACTURED COAL

The fracture network in fractured coal is the main channel of coal seam gas flow. Not only the geometric topology properties (such as fractal characteristics) of a single fracture but also the connection topology properties (interconnection characteristics between fractures) of the fracture network have an important impact on the fluid flow in fracture networks. In this study, the connection topology properties of the fracture network in the fractured coal are explored based on the complex network theory for the first time. The property parameters such as the fracture node degree, the clustering coefficient, and the average path length are analyzed. It shows that the average clustering coefficient of the fracture network in fractured coal is larger, and the average path length is smaller. The connection property of the fracture network in the fractured coal presents a typical “small-world” clustering model. Further, by considering the fractal characteristics of the single fracture and the clustering characteristics of the fracture network, an improved clustered fractal discrete fracture network (DFN) model is developed. Then, based on the lattice Boltzmann method, the permeability properties of the generated clustered fractal DFNs are analyzed. The results show that the permeability of DFNs is positively correlated with the average clustering coefficient of fracture network, and negatively correlated with the fractal dimension of fracture. Therefore, the topological clustering characteristics of fracture networks and the fractal characteristics of fractures cannot be ignored in describing the fluid flow in the fracture network, and our clustered fractal DFN model provides a new idea for guiding the optimization design in DFN engineering.

Research on Pollution Prediction Model Based on Pollutant Knowledge Graph

At present, the main conventional method to study pollution diffusion is to establish a mathematical model to analyse the relationship between pollution sources. The lack of analysis on the characteristics and attributes of pollution sources leads to unsatisfactory identification results. To solve this problem, a pollution diffusion prediction method based on pollution atlas was proposed. The pollution knowledge graph was constructed to describe the relationship between pollution source attributes and the interaction and influence between pollution source and region. The diffusion characteristics of pollution were extracted from the atlas, and DBSCAN was used to achieve clustering and deep neural network model was built to predict the pollution degree at different depths. The model is verified in a typical contaminated site. The experimental results show that the proposed method is effective and has good recognition effect and convergence speed.

The Role of Gossiping in Information Dissemination over a Network of Agents.

We consider information dissemination over a network of gossiping agents. In this model, a source keeps the most up-to-date information about a time-varying binary state of the world, and n receiver nodes want to follow the information at the source as accurately as possible. When the information at the source changes, the source first sends updates to a subset of m≤n nodes. Then, the nodes share their local information during the gossiping period, to disseminate the information further. The nodes then estimate the information at the source, using the majority rule at the end of the gossiping period. To analyze the information dissemination, we introduce a new error metric to find the average percentage of nodes that can accurately obtain the most up-to-date information at the source. We characterize the equations necessary to obtain the steady-state distribution for the average error and then analyze the system behavior under both high and low gossip rates. We develop an adaptive policy that the source can use to determine its current transmission capacity m based on its past transmission rates and the accuracy of the information at the nodes. Finally, we implement a clustered gossiping network model, to further improve the information dissemination.

Adaptive traffic aware clustering and routing model for wireless sensor networks

<span>Designing effective clustering algorithms plays a very important role in improving network lifetime target coverage (NLTC) in heterogeneous wireless sensor networks (HWSNs). However, the current clustering mechanism failed to address hotspot problem resulting in poor coverage and network lifetime performance. Recently, unequal clustering has been used for addressing hotspot problem in HWSNs. However, when performing inter-cluster routing, load balancing is not efficient and the energy degradation is high, which affects the overall network performance. This work introduces an effective model namely adaptive traffic aware clustering and routing (ATACR) model to address hotspot and load balancing issues. The ATACR introduces a novel unequal clustering and ideal distribution of load in different cluster levels. The ATACR model improves network lifetime, reduces control channel overhead, and attains better throughput in comparison with existing routing models.</span>

Personalized Federated Learning Algorithm with Adaptive Clustering for Non-IID IoT Data Incorporating Multi-Task Learning and Neural Network Model Characteristics.

The proliferation of IoT devices has led to an unprecedented integration of machine learning techniques, raising concerns about data privacy. To address these concerns, federated learning has been introduced. However, practical implementations face challenges, including communication costs, data and device heterogeneity, and privacy security. This paper proposes an innovative approach within the context of federated learning, introducing a personalized joint learning algorithm for Non-IID IoT data. This algorithm incorporates multi-task learning principles and leverages neural network model characteristics. To overcome data heterogeneity, we present a novel clustering algorithm designed specifically for federated learning. Unlike conventional methods that require a predetermined number of clusters, our approach utilizes automatic clustering, eliminating the need for fixed cluster specifications. Extensive experimentation demonstrates the exceptional performance of the proposed algorithm, particularly in scenarios with specific client distributions. By significantly improving the accuracy of trained models, our approach not only addresses data heterogeneity but also strengthens privacy preservation in federated learning. In conclusion, we offer a robust solution to the practical challenges of federated learning in IoT environments. By combining personalized joint learning, automatic clustering, and neural network model characteristics, we facilitate more effective and privacy-conscious machine learning in Non-IID IoT data settings.

Flood Disaster Assessment Method Based on a Stacked Denoising Autoencoder

In recent years, extreme weather has occurred frequently, and the risk of heavy rainfall and flooding faced by the people has risen. It is therefore an urgent requirement to carry out applied research on heavy rainfall and flooding risk assessment. We took Henan Province, where a major flood disaster occurred in 2021, as an example to analyze the impact factors of urban flooding and conduct a risk assessment. Indicators were first selected from population, housing, and the economy, and correlation analysis was used to optimize the indicator system. Then, a deep clustering network model based on a stacked denoising autoencoder (SDAE) was constructed, the feature information implied in the disaster indicators was abstracted into potential features through the coding and decoding of the network, and a small number of potential features were used to express the complex relationship between the disaster indicators. The results of the study show that the high-risk areas of flood damage in Henan Province in 2021 account for 2.3%, the medium-risk areas account for 9.4%, and the low-risk areas account for 80.3%. These evaluation results are in line with the actual situation in Henan Province, and the division of the grade in some areas is more reasonable compared with the entropy weighting method, which is a commonly used method of disaster assessment. The new model does not need to calculate weights to cope with changes in indicators and disaster conditions. The research results can provide scientific reference for urban flood risk management, disaster prevention and mitigation, and regional planning.

Intermittent output tracking control of heterogeneous multi-agent systems over wide-area clustered communication networks

In this paper, an output tracking problem is investigated for a wide-area heterogeneous multi-agent system with multiple leaders through intermittent communication, where the communication network consists of clustered subnetworks distributed in a wide area. Firstly, a hierarchical clustered network model is built to describe the communication network topology of heterogeneous agents communicated by an intermittent communication mechanism. Subsequently, dynamic compensators are designed for the heterogeneous agents to estimate the leaders’ state information and a dynamic state feedback hybrid control strategy is designed for each agent to ensure the output tracking consensus. Furthermore, the proposed hybrid control strategy is extended to a case where the complete state information of the agent itself is also not available. Then, under the proposed hybrid control strategy, the closed-loop tracking error system is modeled by a switched system. Consequently, sufficient conditions are derived for the stability of the closed-loop tracking error system under the proposed hybrid control strategy by using a piecewise Lyapunov function and convex synthesis methods. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed hybrid control strategy.

A partially shared joint clustering framework for detecting protein complexes from multiple state-specific signed interaction networks

Detecting protein complexes is critical for studying cellular organizations and functions. The accumulation of protein–protein interaction (PPI) data enables the identification of protein complexes computationally. Although a great number of computational methods have been proposed to identify protein complexes from PPI networks, most of them ignore the signs of PPIs that reflect the ways proteins interact (activation or inhibition). As not all PPIs imply co-complex relationships, taking into account the signs of PPIs can benefit the identification of protein complexes. Moreover, PPI networks are not static, but vary with the change of cell states or environments. However, existing methods are primarily designed for single-network clustering, and rarely consider joint clustering of multiple PPI networks. In this study, we propose a novel partially shared signed network clustering (PS-SNC) model for identifying protein complexes from multiple state-specific signed PPI networks jointly. PS-SNC can not only consider the signs of PPIs, but also identify the common and unique protein complexes in different states. Experimental results on synthetic and real datasets show that our PS-SNC model can achieve better performance than other state-of-the-art protein complex detection methods. Extensive analysis on real datasets demonstrate the effectiveness of PS-SNC in revealing novel insights about the underlying patterns of different cell lines.

Flight risk evaluation based on flight state deep clustering network

Flight risk evaluation based on data-driven approach is an essential topic of aviation safety management. Existing risk analysis methods ignore the coupling and time-variant characteristics of flight parameters, and cannot accurately establish the mapping relationship between flight state and loss-of-control risk. To deal with the problem, a flight state deep clustering network (FSDCN) model was proposed to mine latent loss-of-control risk information implicating in raw flight parameters. FSDCN integrates the feature extraction and clustering into an end-to-end deep hybrid network to extract latent risk features from multivariate time-series flight parameters and cluster them. In the FSDCN model, a sequential multi-attention encoder–decoder network is designed to extract embedded risk features, and the feature clustering layer is designed to iteratively refine clustering effects and feature extraction. Besides, a loss-of-control classifier is added to optimize the risk feature vector expression and ensure sufficient dividing feature for facilitate clustering. The multi-task joint learning strategy is adopted to improve the clustering performance of the model further. According to extracted risk features and similarity metrics, the optimal clusters number of flight states is set as 5. Comparative experiments show that FSDCN significantly performs better than other clustering models with performance percentage error below 6%. Through statistical analysis of clustering results, the risk level is quantified for each cluster. Three high-difficulty maneuver cases are presented to demonstrate FSDCN for flight risk evaluation. The flight parameter sequences of the maneuver cases are input into the well-trained FSDCN to obtain the risk prediction results. The spatiotemporal distribution characteristics of the risk-quantized results are consistent with flight parameters over-limit situations, which demonstrates the effectiveness of FSDCN on clustering flight states. The experimental results on flight maneuver cases show that FSDCN can find potential loss-of-control risk features according to multivariate time-series flight data and provide support for in-flight risk warnings.

Lead–lag detection and network clustering for multivariate time series with an application to the US equity market

In multivariate time series systems, it has been observed that certain groups of variables partially lead the evolution of the system, while other variables follow this evolution with a time delay; the result is a lead–lag structure amongst the time series variables. In this paper, we propose a method for the detection of lead–lag clusters of time series in multivariate systems. We demonstrate that the web of pairwise lead–lag relationships between time series can be helpfully construed as a directed network, for which there exist suitable algorithms for the detection of pairs of lead–lag clusters with high pairwise imbalance. Within our framework, we consider a number of choices for the pairwise lead–lag metric and directed network clustering model components. Our framework is validated on both a synthetic generative model for multivariate lead–lag time series systems and daily real-world US equity prices data. We showcase that our method is able to detect statistically significant lead–lag clusters in the US equity market. We study the nature of these clusters in the context of the empirical finance literature on lead–lag relations, and demonstrate how these can be used for the construction of predictive financial signals.

Development and Application of Smart SPIN Model: Measuring the Spectrum, Penetration, Impact and Network of Smart City Industries in South Korea

The research agenda on smart cities has increasingly extended not only on perspectives of social–economic relations between technologies and cities but also on the industrial economic ecosystem. The purpose of this study is to focus on an analytical method for the characteristics of a smart city’s ecology and industry. With that thought, we have developed a smart SPIN (Spectrum, Penetration, Impact and Network) model and applied it to analyze the ecology of the Korean smart city industry in general. This model consists of smart spectrum model, smart penetration model, smart impact path model and smart network clustering model. The smart SPIN model shows great potential as an analytical method for the smart city industry ecosystem. As a source of data for analyses from 1960, 1985 and 2015 via input–output table, we revised these data into 25 and 8 industries related to the smart city ecosystem. Additionally, we applied the 2015 GDP deflator. The results of analysis are as follows: First, spectrum, the number of smart industries is increasing. This means that the smart city industry scope and area are expanding. Second, analysis of the smart penetration model and smart ecological industry can be applied into other industries. In other words, traditional industries can crossover and utilize smart technology. Third, with the results of our analysis of the smart impact path model, production paths are increasing while parameter paths did not show a triple parameter path. This means the value chain of the smart city industry is highly divested, but the structure of the industry is weakening. Fourth, smart network analysis shows important clusters to be centered on traditional industries: the clusters do not appear in smart industry centers. This means the impact of the smart city is not strong. Our analysis shows that, today, the Korean industrial ecosystem of smart cities is interacting with existing industries and raising it to a more intelligent and smarter level. Thus, there is a need for this kind of analysis study in order to find optimized smart city industry ecosystem.

A clustering- and maximum consensus-based model for social network large-scale group decision making with linguistic distribution

Nowadays, with the increasing complexity of decision-making environment, more and more large-scale group decision making (LGDM) problems are faced. Due to the existence of social network relationships among experts, social network analysis (SNA) is proved to be an effective analysis method for LGDM problems. Meanwhile, it is crucial for LGDM issues to determine the weights of decision groups and to lessen the large-scale DMs’ dimension, which will affect the result of decision making directly. This study proposes a clustering- and maximum consensus-based resolution framework with linguistic distribution (LD) for social network large-scale group decision making (SNLGDM) problems. In the consensus framework, independent sub-groups can be obtained by the division of large-scale DMs according to trust relationship using the proposed SNA-based trust network clustering model, and the LD assessments are used to represent the preference relation of sub-groups. Following this, by considering three dependable sources: consistency, similarity, and in-centrality degree, this paper devises a maximum consensus-based method, which can generate the sub-groups’ comprehensive weight by maximizing the level of consensus between sub-groups and the collective matrix. Meanwhile, the final ranking of alternatives can be obtained based on collective preference relation. Conclusively, the availability and advantage of this research are verified through numerical example, coefficient analysis and comparative analysis.

Version Age of Information in Clustered Gossip Networks

We consider a network consisting of a single source and <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> receiver nodes that are grouped into equal-sized clusters. Each cluster corresponds to a distinct community such that nodes that belong to different communities cannot exchange information. We use dedicated cluster heads in each cluster to facilitate communication between the source and the nodes within that cluster. Inside clusters, nodes are connected to each other according to a given network topology. Based on the connectivity levels within clusters, each node relays its current stored version of the source update to its neighboring nodes by <i>local gossiping</i>. We consider disconnected, ring, and fully connected network topologies for each cluster. For each of these network topologies, we characterize the average version age at each node and find the average version age scaling as a function of the network size <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. Next, by allowing gossiping among the cluster heads, we improve the version age scaling at the receiver nodes. Then, focusing on a ring network topology in each cluster, we introduce hierarchy to the considered clustered gossip network model without using dedicated cluster heads. Finally, we find the version age-optimum cluster sizes as a function of the source, cluster head, and node update rates through numerical evaluations.

РАЗРАБОТКА ИМИТАЦИОННОЙ МОДЕЛИ ИСПОЛЬЗОВАНИЯ РОЯ БЕСПИЛОТНЫХ ЛЕТАТЕЛЬНЫХ АППАРАТОВ В СЕЛЬСКОМ ХОЗЯЙСТВЕ

Introduction: Due to the rapid technological development of communication and network technologies and the emergence of the Internet of Things technology, unmanned aerial vehicles (UAVs) have become used in almost all areas of society, from military to civilian. Agriculture is one area where UAVs will be used to monitor vast areas of crops. UAVs will be able to receive data on the state of the soil, improve agriculture and use plant protection products from insects and birds. Consequently, agriculture will be the largest market for UAVs. Objective. The article discusses the possibility of using a swarm of unmanned aerial vehicles in hard-to-reach agricultural areas to create a wide coverage area for transmitting information from ground-based wireless sensor networks. Architectural solutions for flying sensor networks are investigated and a simulation model is presented that integrates flying sensor networks (LSNs) and terrestrial wireless sensor networks (WSNs) for data transmission. The model studies the clustering of an UAV swarm using the K-means method and the search for the shortest path in routing using the Dijkstra algorithm. Result. Computer simulation of optimal routing for a swarm of 250 UAVs has been carried out. The results of UAV swarm clustering modeling are presented, a UAV network clustering model is presented using the machine learning method the K-means algorithm, and an algorithm for routing data through the UAV swarm network using Dijkstra's algorithm, which finds the shortest path through the formed clusters.

Urban Boundary Demarcation—An iCN Model Approach

During the last two decades, determining the urban boundaries of cities has become one of the major concerns in the urban and regional planning subject domains. Many scholars have tried to model the change of urban boundaries as it helps with sustainable development, population projections and social policy making, but such efforts have been futile, owing to the complex nature of urbanization and the theoretical and technical limitations of the proposed applications. Hence, many countries continue to rely on the administrative boundary demarcation, which rarely represent the actual urbanizing pattern. In such context, this study utilized the “Intersection-Based Clustered Network Model—(iCN Model)” to determine the urban boundaries of cities and selected Sri Lanka as the study area and considered few cities to test the model empirically, with satellite imagery classified urban boundaries. The findings of the study depict that the iCN Model is capable of capturing the complex and dynamic socioeconomic interdependencies of cities via the transportation network configurations. Therefore, the proposed approach is an excellent proxy to derive the urban boundaries of cities, which correspond with the same, derived by the satellite imageries. The proposed model is entirely based on open-source GIS applications and is free to implement and modify using the methods described in this paper.

How Small and Medium-Sized Enterprises Can Uptake the Sustainable Development Goals through a Cluster Management Organization: A Case Study

Even though many small and medium-sized enterprises (SMEs) realize the importance of the Sustainable Development Goals (SDGs) for their sustainable business development, they face difficulties when individually pursuing this target. This paper aims to provide cluster-based SMEs with a framework to facilitate the uptake of the SDGs through a cluster network model. By focusing on the case study of a furniture production Cluster Management Organization (CMO Habic a network-based approach to sustainability management is analyzed in order to integrate the SDGs framework into SMEs’ everyday language and day-to-day operations. Through a cluster model approach, industry-specific sustainability material issues are tackled jointly and with the aim of applying a common solution to a similar challenge, Lehi-ODS, an online sustainability management tool for SME self-evaluation, has been developed wherein the participating SMEs can gain an overarching knowledge about the UN 2030 Agenda, assess their own alignment with the SDGs and with respect to that of their peers, and prioritize initiatives to correctly deliver them. The main conclusion derived from the paper is that a CMO is deemed to be a valuable driver in identifying common sector-specific challenges of corporate sustainability and in addressing collectively the question of contributing to the SDGs.

Breaking the curse of dimensionality: hierarchical Bayesian network model for multi-view clustering

Clustering high-dimensional data under the curse of dimensionality is an arduous task in many applications domains. The wide dimension yields the complexity-related challenges and the limited number of records leads to the overfitting trap. We propose to tackle this problematic using the graphical and probabilistic power of the Bayesian network. Our contribution is a new loose hierarchical Bayesian network model that encloses latent variables. These hidden variables are introduced for ensuring a multi-view clustering of the records. We propose a new framework for learning our proposed Bayesian network model. It starts by extracting the cliques of highly dependent features and it proceeds to learn representative latent variable for each features’ clique. The experimental results of our comparative analysis prove the efficiency of our model in tackling the distance concentration challenge. They also show the effectiveness of our model learning framework in skipping the overfitting trap, on benchmark high-dimensional datasets.

Estimation of gender-specific connectional brain templates using joint multi-view cortical morphological network integration

The estimation of a connectional brain template (CBT) integrating a population of brain networks while capturing shared and differential connectional patterns across individuals remains unexplored in gender fingerprinting. This paper presents the first study to estimate gender-specific CBTs using multi-view cortical morphological networks (CMNs) estimated from conventional T1-weighted magnetic resonance imaging (MRI). Specifically, each CMN view is derived from a specific cortical attribute (e.g. thickness), encoded in a network quantifying the dissimilarity in morphology between pairs of cortical brain regions. To this aim, we propose Multi-View Clustering and Fusion Network (MVCF-Net), a novel multi-view network fusion method, which can jointly identify consistent and differential clusters of multi-view datasets in order to capture simultaneously similar and distinct connectional traits of samples. Our MVCF-Net method estimates a representative and well-centered CBTs for male and female populations, independently, to eventually identify their fingerprinting regions of interest (ROIs) in four main steps. First, we perform multi-view network clustering model based on manifold optimization which groups CMNs into shared and differential clusters while preserving their alignment across views. Second, for each view, we linearly fuse CMNs belonging to each cluster, producing local CBTs. Third, for each cluster, we non-linearly integrate the local CBTs across views, producing a cluster-specific CBT. Finally, by linearly fusing the cluster-specific centers we estimate a final CBT of the input population. MVCF-Net produced the most centered and representative CBTs for male and female populations and identified the most discriminative ROIs marking gender differences. The most two gender-discriminative ROIs involved the lateral occipital cortex and pars opercularis in the left hemisphere and the middle temporal gyrus and lingual gyrus in the right hemisphere.