Cluster Nodes Research Articles

Energy‐efficient optimal sink placement using extended pelican optimization‐based clustering with Voronoi‐based node deployment

SummaryA wireless sensor network (WSN) is a network of spatially distributed autonomous sensor nodes that collaborate to monitor physical or environmental conditions, collect data, and transmit it to a sink node. WSNs have a wide range of applications across various domains due to their ability to provide real‐time data collection, remote monitoring, and data analysis. Still, in a WSN with a fixed sink, sensor nodes closer to the sink tend to have higher traffic loads because they forward data to nodes further away. This can lead to hotspots and uneven energy consumption. Introducing a mobile sink can distribute the traffic more evenly across the network, reducing congestion and balancing the energy consumption among nodes. Hence, this research proposes a novel WSN environment with a focus on energy‐efficient routing. The network is deployed using Voronoi‐based criteria to address network coverage issues. The clustering of nodes is employed using the proposed extended pelican optimization (ExPo) algorithm to improve network lifetime and energy efficiency, critical concerns in WSNs due to limited sensor node battery capacity. Cluster heads (CHs) aggregate and process data locally, reducing the energy needed for long‐range communication. Then, an energy‐efficient optimal sink placement (EEOSP) approach is used to optimize the placement of the mobile sink. The proposed system model is evaluated based on various metrics, including average residual energy, delay, network lifetime, packet delivery ratio, and throughput and acquired the values of 0.99 J, 3.68 ms, 99.55%, 99.55%, and 81 Mbps, respectively.

Neuro‐fuzzy‐based cluster formation scheme for energy‐efficient data routing in IOT‐enabled WSN

SummaryInternet of things–enabled wireless sensor networks face challenges like inflexibility, poor scalability, suboptimal cluster head selection, and energy inefficiencies. This is due to the faster data transmission rates between cluster nodes during data packet routing. This creates unnecessary energy consumption burdens for those actively transmitting nodes. Conceptually, an effective cluster formation phase supports better data routing mechanisms, while sustaining the energy efficiency of individual nodes. This paper proposes a Neuro‐Fuzzy based Cluster Formation (NFCF) scheme to facilitate adaptive and energy‐efficient cluster topologies. NFCF utilizes fuzzy logic and neural networks to identify optimal super nodes for flexible cluster formations. This approach enables configurable cluster sizes along with inclusion/exclusion criteria for member nodes based on energy thresholds. Parameters evaluated for node selection include the degree of super node, expected energy per cluster, energy variance, and residual energy. Nodes not meeting the thresholds are excluded. The neural network updates fuzzy rules to guide optimal clustering decisions based on anticipated energy dynamics under different conditions. The performance of the proposed NFCF scheme is evaluated based on objective function changes related to data transmission, individual node energy variation, energy variance before and after transmissions, and averaged end‐to‐end delay across transmission cycles. Results are compared against genetic fuzzy clustering, fuzzy energy‐aware clustering, fuzzy‐based distributed clustering, fuzzy logic‐based multi‐hop clustering, and fuzzy weighted k‐means clustering.

Purity Skeleton Dynamic Hypergraph Neural Network

Recently, in the field of Hypergraph Neural Networks (HGNNs), the effectiveness of dynamic hypergraph construction has been validated, which aims to reduce structural noise within the hypergraph through embeddings. However, the existing dynamic construction methods fail to notice the reduction of information contained in the hypergraphs during dynamic updates. This limitation undermines the quality of hypergraphs. Moreover, dynamic hypergraphs are constructed from graphs. Several key nodes play a crucial role in graph, but they are overlooked in hypergraphs. In this paper, we propose a Purity Skeleton Dynamic Hypergraph Neural Network (PS-DHGNN) to address the above issues. Firstly, we leverage purity skeleton method to dynamically construct hypergraphs via the fusion embeddings of features and topology simultaneously. This method effectively reduces structural noise and prevents the loss of information. Secondly, we employ an incremental training strategy, which implements a batch training strategy based on the importance of nodes. The key nodes, as the skeleton of hypergraph, are still highly valued. In addition, we utilize a novel loss function for learning structure information between hypergraph and graph. We conduct extensive experiments on node classification and clustering tasks, which demonstrate that our PS-DHGNN outperforms state-of-the-art methods. Note on real-world traffic flow datasets, PS-DHGNN demonstrates excellent performance, which is highly meaningful in practice.

Clust&See3.0 : clustering, module exploration and annotation.

Cytoscape is an open-source software to visualize and analyze networks. However, large networks, such as protein interaction networks, are still difficult to analyze as a whole. Here, we propose Clust&See3.0, a novel version of a Cytoscape app that has been developed to identify, visualize and manipulate network clusters and modules. It is now enriched with functionalities allowing custom annotations of nodes and computation of their statistical enrichments. As the wealth of multi-omics data is growing, such functionalities are highly valuable for a better understanding of biological module composition, as illustrated by the presented use case. In summary, the originality of Clust&See3.0 lies in providing users with a complete tool for network clusters analyses: from cluster identification, visualization, node and cluster annotations to annotation statistical analyses.

Energy efficient routing for improving lifetime in MWSN: A clustering approach

A Mobile wireless sensor network (MWSN) consists of mobile sensor nodes, which can be deployed in any specific environment, and due to its’ mobility it can perform with rapid topological transformations of a network. The sensor nodes having limited battery power are used to collect specific data and this raw data is sent to a static sink node of the network. Under such a scenario, to avoid frequent disconnections due to topological change in the network and can avail more reliable data transmissions in energy awareness perspective, an energy efficient routing protocol for MWSNs to improve its lifetime is proposed in this paper by utilizing a clustering approach. A MWSN with random number of sensor nodes are initially considered and then, clustering algorithm K-means is used to find a predefined number of clusters with their initial cluster heads (CHs) and the centroid location of these clusters is also determined. The role of these CHs is to elect our DDBLACH (distance to sink and cluster centroid with battery level aware cluster head) nodes from each cluster, by sending and receiving intra-cluster messages among other member sensor nodes. A DDBLACH node is determined by using three factors, such as minimum distance from cluster centroid location, minimum distance from sink and the maximum battery level of the node from each cluster. These DDBLACH nodes are used to collect data from intra-cluster sensors and thereafter, send those towards sink node for further processing using tree based hierarchical routes. Finally, an energy efficient routing technique for MWSNs is proposed for data transmission from these DDBLACH nodes of clusters to a sink of the network. Here simulation results indicate the supremacy of our proposed scheme over other existing methods in various aspects, such as improving the presence of alive nodes and average network lifetime.

Cu(II)-Organic Framework for Carboxylative Cyclization of Propargylic Amines with CO2.

Effective CO2 transformations hold essential significance for carbon neutrality and sustainable energy development. Carboxylative cyclization of propargylic amines with CO2 serves as an atom-economic reaction to afford oxazolidinones, showing broad applications in organic synthesis and pharmaceutical fields. However, most catalysts involved noble metals, exhibited low efficiency, or required large amounts of base. Hence, it is imperative to explore alternative noble-metal-free catalysts in order to achieve efficient conversion while minimizing the use of additives. Herein, a novel nanopore-based Cu(II)-organic framework (1) based on a new imidazole carboxylic ligand was successfully constructed and exhibited excellent stability. Catalytic investigations revealed that the combination of 1 with 1,4-diaza[2.2.2]bicyclooctane (DABCO) efficiently catalyzed the carboxylative cyclization of propargylic amines with CO2, achieving turnover numbers of 142 based on the catalyst and 7.1 based on DABCO. 1 as a heterogeneous catalyst maintained high catalytic performance even after being reused at least 5 cycles, with its structure remaining stable. The strong activation of Cu(II) cluster nodes of catalyst 1 toward -NH- groups within organic substrates, as demonstrated by mechanism experiments, along with excellent CO2 adsorption performance and the presence of regular 1D channels, synergistically facilitates the reaction rate. This research presents the first instance of a Cu(II)-organic framework achieving this cyclization reaction, offering wide prospects for novel catalyst design and CO2 utilization.

Parallel PSO for Efficient Neural Network Training Using GPGPU and Apache Spark in Edge Computing Sets

The training phase of a deep learning neural network (DLNN) is a computationally demanding process, particularly for models comprising multiple layers of intermediate neurons.This paper presents a novel approach to accelerating DLNN training using the particle swarm optimisation (PSO) algorithm, which exploits the GPGPU architecture and the Apache Spark analytics engine for large-scale data processing tasks. PSO is a bio-inspired stochastic optimisation method whose objective is to iteratively enhance the solution to a (usually complex) problem by approximating a given objective. The expensive fitness evaluation and updating of particle positions can be supported more effectively by parallel processing. Nevertheless, the parallelisation of an efficient PSO is not a simple process due to the complexity of the computations performed on the swarm of particles and the iterative execution of the algorithm until a solution close to the objective with minimal error is achieved. In this study, two forms of parallelisation have been developed for the PSO algorithm, both of which are designed for execution in a distributed execution environment. The synchronous parallel PSO implementation guarantees consistency but may result in idle time due to global synchronisation. In contrast, the asynchronous parallel PSO approach reduces the necessity for global synchronization, thereby enhancing execution time and making it more appropriate for large datasets and distributed environments such as Apache Spark. The two variants of PSO have been implemented with the objective of distributing the computational load supported by the algorithm across the different executor nodes of the Spark cluster to effectively achieve coarse-grained parallelism. The result is a significant performance improvement over current sequential variants of PSO.

BRMECQN: Design of an efficient integration of Bioinspired Routing Model with Energy efficient Clustering for high QoS Wireless Networks

In this dynamic domain of communication in wireless network technology, the need for advanced models that boost the Quality of Service (QoS) parameters concerning energy efficiency, speed, throughput, packet delivery ratio, and minimization of jitter is of prime concern. Traditional routing and clustering methods in wireless networks are often confronted with limitations such as suboptimal resource utilization, reduced energy efficiency, and lower QoS metrics. In this regard, the present work introduces an innovative integration of bioinspired routing models with energy-efficient clustering to enhance the performance of wireless networks. A novel Fan-Shaped Clustering approach is employed in the proposed model for a proper grouping of the network nodes, which can reduce the energy consumption and enhance the load balancing across the network. The model performs a further integration of Elephant Herding Optimization and Bat Optimization algorithms following the clustering. This integration has been done to develop an optimal route finding mechanism between the clustered nodes through its bioinspired approach, to utilize the global search capability of Elephant Herding Optimization, and the proficiency of Bat Optimizations in local searches. The proposed model, when tested empirically on different configurations of networks, presents significant improvements over methods in practice. The results suggest a 10.5% improvement in energy efficiency, an improvement in speed of 8.3%, a rise in throughput of 3.5%, an improvement in Packet Delivery Ratio of 4.5%, and a reduction in jitter of 1.9%. These have been substantial improvements indicating that the proposed model overcomes the limitations of the existing ones and at the same time places a new benchmark in the performance of wireless networks. The impact of this work is immense, providing a scalable and efficient solution for next-generation wireless networks. This integration of bioinspired algorithms in the routing and clustering of the network shall open new avenues for research and development into the field with the potential of more resilient, efficient, and high-performing wireless communication systems. This work, therefore, adds immense impetus to the development of wireless network technologies to uphold higher QoS and better resource management in an era of ever-growing digital communication demands.

Distributed Caching System with Strict Consistency Guarantee for Critical Information Infrastructure

The place of caching in modern information systems is considered. Typical models of network applications architecture construction and consistency guarantees that they provide to the end user are presented. A comparative analysis of analogues is given, which allows one to outline the features of the proposed system. The developed architecture is described. The architecture of a separate cluster node is described, as well as the architecture and implementation of software development kits for different programming languages. The specifics of designing complex client libra­ries for distributed systems are considered. The importance of consistency models is noted, their interrelationships and hierarchy are described based on the summarized information of existing research. A brief comparison of the two popular consensus algorithms, Raft and Paxos, is given in order to determine the basics of designing a common architecture. Industry leaders in caching systems emphasize performance, deliberately relaxing the consistency of the stored data. Using such systems in an environment that requires maintaining the order of events in the external world is difficult or involves the need for additional tools to maintain data consistency. This paper proposes a caching system that guarantees strict data consistency and provides a single interface for handling it, similar to industry leaders.

Cluster-based Graph Collaborative Filtering

Graph Convolution Networks (GCNs) have significantly succeeded in learning user and item representations for recommendation systems. The core of their efficacy is the ability to explicitly exploit the collaborative signals from both the first- and high-order neighboring nodes. However, most existing GCN-based methods overlook the multiple interests of users while performing high-order graph convolution. Thus, the noisy information from unreliable neighbor nodes ( \(e.g.\) , users with dissimilar interests) negatively impacts the representation learning of the target node. Additionally, conducting graph convolution operations without differentiating high-order neighbors suffers the over-smoothing issue when stacking more layers, resulting in performance degradation. In this paper, we aim to capture more valuable information from high-order neighboring nodes while avoiding noise for better representation learning of the target node. To achieve this goal, we propose a novel GCN-based recommendation model, termed Cluster-based Graph Collaborative Filtering (ClusterGCF). This model performs high-order graph convolution on cluster-specific graphs, which are constructed by capturing the multiple interests of users and identifying the common interests among them. Specifically, we design an unsupervised and optimizable soft node clustering approach to classify user and item nodes into multiple clusters. Based on the soft node clustering results and the topology of the user-item interaction graph, we assign the nodes with probabilities for different clusters to construct the cluster-specific graphs. To evaluate the effectiveness of ClusterGCF, we conducted extensive experiments on four publicly available datasets. Experimental results demonstrate that our model can significantly improve recommendation performance.

Geometric Tuning of Coordinatively Unsaturated Copper(I) Sites in Metal-Organic Frameworks for Ambient-Temperature Hydrogen Storage.

Porous solids can accommodate and release molecular hydrogen readily, making them attractive for minimizing the energy requirements for hydrogen storage relative to physical storage systems. However, H2 adsorption enthalpies in such materials are generally weak (-3 to -7 kJ/mol), lowering capacities at ambient temperature. Metal-organic frameworks with well-defined structures and synthetic modularity could allow for tuning adsorbent-H2 interactions for ambient-temperature storage. Recently, Cu2.2Zn2.8Cl1.8(btdd)3 (H2btdd = bis(1H-1,2,3-triazolo-[4,5-b],[4',5'-i])dibenzo[1,4]dioxin; CuI-MFU-4l) was reported to show a large H2 adsorption enthalpy of -32 kJ/mol owing to π-backbonding from CuI to H2, exceeding the optimal binding strength for ambient-temperature storage (-15 to -25 kJ/mol). Toward realizing optimal H2 binding, we sought to modulate the π-backbonding interactions by tuning the pyramidal geometry of the trigonal CuI sites. A series of isostructural frameworks, Cu2.7M2.3X1.3(btdd)3 (M = Mn, Cd; X = Cl, I; CuIM-MFU-4l), was synthesized through postsynthetic modification of the corresponding materials M5X4(btdd)3 (M = Mn, Cd; X = CH3CO2, I). This strategy adjusts the H2 adsorption enthalpy at the CuI sites according to the ionic radius of the central metal ion of the pentanuclear cluster node, leading to -33 kJ/mol for M = ZnII (0.74 Å), -27 kJ/mol for M = MnII (0.83 Å), and -23 kJ/mol for M = CdII (0.95 Å). Thus, CuICd-MFU-4l provides a second, more stable example of optimal H2 binding energy for ambient-temperature storage among reported metal-organic frameworks. Structural, computational, and spectroscopic studies indicate that a larger central metal planarizes trigonal CuI sites, weakening the π-backbonding to H2.

Backdoor attacks on unsupervised graph representation learning

Unsupervised graph learning techniques have garnered increasing interest among researchers. These methods employ the technique of maximizing mutual information to generate representations of nodes and graphs. We show that these methods are susceptible to backdoor attacks, wherein the adversary can poison a small portion of unlabeled graph data (e.g., node features and graph structure) by introducing triggers into the graph. This tampering disrupts the representations and increases the risk to various downstream applications. Previous backdoor attacks in supervised learning primarily operate directly on the label space and may not be suitable for unlabeled graph data. To tackle this challenge, we introduce GRBA,11https://github.com/fbd3/GRBA.git. a gradient-based first-order backdoor attack method. To the best of our knowledge, this constitutes a pioneering endeavor in investigating backdoor attacks within the domain of unsupervised graph learning. The initiation of this method doesn’t necessitate prior knowledge of downstream tasks, as it directly focuses on representations. Furthermore, it is versatile and can be applied to various downstream tasks, including node classification, node clustering and graph classification. We evaluate GRBA on state-of-the-art unsupervised learning models, and the experimental results substantiate the effectiveness and evasiveness of GRBA in both node-level and graph-level tasks.

Node Clustering on Attributed Graph Using Anchor Sampling Strategy and Debiasing Strategy

Node Clustering on Attributed Graph Using Anchor Sampling Strategy and Debiasing Strategy

Attribute graph clustering via transformer and graph attention autoencoder

Graph clustering is a crucial technique for partitioning graph data. Recent research has concentrated on integrating topology and attribute information from attribute graphs to generate node embeddings, which are subsequently clustered using classical algorithms. However, these methods have some limitations, such as insufficient information inheritance in shallow networks or inadequate quality of reconstructed nodes, leading to suboptimal clustering performance. To tackle these challenges, we introduce two normalization techniques within the graph attention autoencoder framework, coupled with an MSE loss, to facilitate node embedding learning. Furthermore, we integrate Transformers into the self-optimization module to refine node embeddings and clustering outcomes. Our model can induce appropriate node embeddings for graph clustering in a shallow network. Our experimental results demonstrate that our proposed approach outperforms the state-of-the-art in graph clustering over multiple benchmark datasets. In particular, we achieved 76.3% accuracy on the Pubmed dataset, an improvement of at least 7% compared to other methods.

A neural network-guided correlation thresholding technique for wireless sensor networks

Wireless sensor networks comprise autonomous nodes monitoring environments while managing energy consumption. Extending network lifespan involves minimizing redundant transmissions by filtering identical information from correlated sensor nodes. Correlation depends on spatial locations and it quantifies sensor relationships. It allows nodes classification as correlated or uncorrelated using a threshold value between 0 and 1 which enhances network lifespan by 60–65 %. Selecting the optimal threshold considering network metrics remains a challenge. This study proposes a neural network-guided correlation thresholding technique (NNCT). NNCT aims to identify the optimal threshold for filtering redundant reports by training a neural network on varied parameters like node density, threshold, coverage, and events using different clustering methods. The process involves implementing spatial correlation models, virtual clustering, event detection, and iterative threshold-based node selection. The neural network achieves a high accuracy of R = 0.97813, offering a promising adaptive solution for automated threshold selection in correlation-based filtering.

Gamma amplitude-envelope correlations are strongly elevated within hyperexcitable networks in focal epilepsy

Methods to quantify cortical hyperexcitability are of enormous interest for mapping epileptic networks in patients with focal epilepsy. We hypothesize that, in the resting state, cortical hyperexcitability increases firing-rate correlations between neuronal populations within seizure onset zones (SOZs). This hypothesis predicts that in the gamma frequency band (40–200 Hz), amplitude envelope correlations (AECs), a relatively straightforward measure of functional connectivity, should be elevated within SOZs compared to other areas. To test this prediction, we analyzed archived samples of interictal electrocorticographic (ECoG) signals recorded from patients who became seizure-free after surgery targeting SOZs identified by multiday intracranial recordings. We show that in the gamma band, AECs between nodes within SOZs are markedly elevated relative to those elsewhere. AEC-based node strength, eigencentrality, and clustering coefficient are also robustly increased within the SOZ with maxima in the low-gamma band (permutation test Z-scores > 8) and yield moderate discriminability of the SOZ using ROC analysis (maximal mean AUC ~ 0.73). By contrast to AECs, phase locking values (PLVs), a measure of narrow-band phase coupling across sites, and PLV-based graph metrics discriminate the seizure onset nodes weakly. Our results suggest that gamma band AECs may provide a clinically useful marker of cortical hyperexcitability in focal epilepsy.

Research on control and management of smart grid optical network based on optical transmission control Protocol (OTN) technology

Smart grids can improve the operational efficiency and reliability of power systems, and optical transmission control protocol (OTN) technology is widely used in the field of power communication. This article focuses on the control and management of smart grid optical networks, aiming to achieve efficient control and management of smart grid optical networks through OTN technology. This article proposes a smart grid OTN management system based on Optical Transmission Control Protocol (OTN) technology. This system achieves the management and optimization of power OTN business by researching functions such as container management and service orchestration, pod and physical node management, application and cluster status monitoring, application logs and cluster logs. In this system, the sources of data are very rich. Data can be obtained from databases, file streams and Flow network. This can ensure that the system receives comprehensive data support for better business analysis and decision-making. This article further studies the optimization methods of optical network control for power OTN services to improve network reliability and reduce risk levels. The experimental results indicate that the use of OTN technology can improve the operational efficiency and reliability of the smart grid, and achieve remote monitoring and control of the smart grid.

Congestion Transition on Random Walks on Graphs.

The formation of congestion on an urban road network is a key issue for the development of sustainable mobility in future smart cities. In this work, we propose a reductionist approach by studying the stationary states of a simple transport model using a random process on a graph, where each node represents a location and the link weights give the transition rates to move from one node to another, representing the mobility demand. Each node has a maximum flow rate and a maximum load capacity, and we assume that the average incoming flow equals the outgoing flow. In the approximation of the single-step process, we are able to analytically characterize the traffic load distribution on the single nodes using a local maximum entropy principle. Our results explain how congested nodes emerge as the total traffic load increases, analogous to a percolation transition where the appearance of a congested node is an independent random event. However, using numerical simulations, we show that in the more realistic case of synchronous dynamics for the nodes, entropic forces introduce correlations among the node states and favor the clustering of empty and congested nodes. Our aim is to highlight the universal properties of congestion formation and, in particular, to understand the role of traffic load fluctuations as a possible precursor of congestion in a transport network.

Does the Presence of Matted Nodes in Colon Adenocarcinoma Influence 5-Year Overall Survival?

Background and Objectives: Colon cancer (CC) is prevalent globally, constituting 11.9% of cases in Mexico. Lymph node metastases are established prognostic indicators, with extracapsular lymph node extension (ENE) playing a crucial role in modifying prognosis. While ENE is associated with adverse factors, certain aspects, like matted nodes (lymph node conglomerates), are underexplored. Matted nodes, clusters of lymph nodes infiltrated by cancer cells, are recognized as an independent prognostic factor in other cancers. This study investigates the prognostic implications of matted nodes in CC. Materials and Methods: From a retrospective analysis of 502 CC consecutive cases treated with colectomy (2005-2018), we identified 255 (50.8%) cases with lymph node metastasis (our study group), which were categorized into two groups: (1) lymph node metastasis alone (n = 208), and (2) lymph node metastasis with matted nodes (n = 47). A comparative survival analysis was performed. Results: Of the 255 patients, 38% had lymph node metastasis. Patients with matted nodes (18.4%) showed an association with higher pN stage and lymphovascular invasion. The 5-year survival rate for patients with matted nodes was 47.7%, compared to 60% without (p = 0.096); however, this association demonstrated only a statistical tendency. Multivariate analysis identified clinical stage and adjuvant chemotherapy use as independent factors contributing to survival. Conclusions: This study underscores matted nodes as potential prognostic indicators in CC, emphasizing their association with higher pN stage and reduced survival. Although the patients with matted nodes showed lower survival, this figure did not search statistical significance, but a tendency was detected, which necessitates precise further research, which is essential for validating these findings and integrating matted nodes into the broader context of colorectal cancer management.

Attributed graph clustering under the contrastive mechanism with cluster-preserving augmentation

Attributed graph clustering is a fundamental task in complex network analysis. Many existing graph clustering methods utilize graph representation learning techniques to learn node representations, subsequently applying K-means for clustering. Despite the significant attention and promising outcomes of graph contrastive learning in graph representation learning, we find two essential problems that need to be solved. (1) How to achieve augmentation for contrast that preserves the cluster structure of a given graph? (2) How to design an effective contrastive learning mechanism that collaborates with clustering? Therefore, we propose an attributed graph clustering method under the contrastive mechanism with cluster-preserving augmentation, integrating node representation learning and clustering into a unified framework. Specifically, we construct a contrasting view based on a generated kNN graph and edge betweenness centrality to preserve the cluster structure in the original graph. Meanwhile, a multilevel contrast mechanism based on pseudo-label-guided negative sampling is proposed to maximize the agreement between node representations in multiple latent spaces. We jointly optimize a specific clustering objective during the contrastive process, leading to refined cluster distribution directly in training. Extensive experiments on several datasets demonstrate that our proposed model consistently outperforms existing state-of-the-art methods on clustering.