Problem In Networks Research Articles

A systemic approach for assessing infrastructure component importance in hazard-prone communities

Investing in pre-event disaster mitigation interventions for physical infrastructure, such as structural retrofits and enhancements, can be costly due to limited resources. To prioritize investments, infrastructure components are ranked by their criticality within the overall system. To be effective in real-world deployment, this approach must account for the complex interactions between components, as failures can occur simultaneously across large geographical areas due to the hazard footprint. As a result, hazard-specific uncertainties and spatial correlations may lead to distinctive failure patterns. In this study, we propose a novel data-driven framework leveraging Monte Carlo simulation, that harnesses the individual realizations to capture and model realistic component damage patterns under a specified hazard scenario. This framework addresses a gap in literature by moving beyond traditional methods that often treat component failures as independent events. By capturing the interdependence between bridges, primarily through failure interactions, and system-wide effects, our method provides a more comprehensive criticality assessment. The simulation data provides a foundation for the framework, which applies to a wide variety of infrastructure networks and performance metrics. To demonstrate the method's effectiveness, a simplified transportation network from Shelby County, TN subjected to an earthquake event is analyzed. The proposed framework provides an effective approach for component ranking, suitable for decision-making where human intuition and simple methods are insufficient. Its broad applicability suggests a potential for large-scale and interdependent network problems.

In-Materia Annealing and Combinatorial Optimization Based on Vertical Memristive Array.

Due to its area and energy efficiency, a memristive crossbar array (CBA) has been extensively studied for various combinatorial optimization applications, from network problems to circuit design. However, conventional approaches include heavily burdening software fine-tuning for the annealing process. Instead, this study introduces the "in-materia annealing" method, where the inter-layer interference of vertically stacked memristive CBA is utilized as an annealing method. When mapping combinatorial optimization problems into the configuration layer of the CBA, exponentially decaying annealing profiles are generated in nearby noise layers. Moreover, in-materia annealing profiles can be controlled by changing compliance current, read voltage, and read pulse width. Therefore, the annealing profiles can be arbitrarily controlled and generated individually for each cell, providing rich noise sources to solve the problem efficiently. Consequently, the experimental and simulation of Max-Cut and weighted Max-Cut problems achieve notable results with the minimum software burden.

Role of Artificial Intelligence in the Computer Network: Review

A combination Artificial Intelligence (AI) with networks systems increases from efficiency and performance of the networks. Today routine life needs huge of data and applications shared between people that lead to a traffic load on networks, therefore intelligence networks produces are important abilities of AI in controlling and avoiding troubleshooting. Also AI improves from security and management of databases .Effective high-intelligent networks operate networks in automation systems .It is one of the most hopeful computer networks future. The development of artificial intelligence in the computer network relies on the importance of applications security and cloud computing. In recent years, a big development has occurred in Artificial Intelligence and majority of computer networks that leads to the of AI in the applications of network techniques. This paper reviews a role of combination of Artificial Intelligence with Computer Networks. Discussed two topics combination of AI in the computer network (Cloud Computing and Security). AI in cloud computing increases efficiency and significant networking. In addition, reduced the cost and minimized base of clouding. Artificial Intelligence controls huge amounts of databases in the cloud avoiding problems in heavy traffic networks. AI is a good solution in cyber-attacks, also solving most problems of security. Today more intelligent interaction with computer networks. Automate solutions in more application applying successful training and testing data sets. Artificial intelligence contributes to all fields of life and encourages the development of new technologies and uses. More robots, data automation, and many applications in the Internet of Things are expected.

Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks.

Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WSN-based tasks, their effectiveness is often compromised by limited training data, leading to issues such as overfitting and reduced accuracy, especially when the number of sensor nodes is low. A key strategy to mitigate overfitting involves increasing both the quantity and diversity of the training data. To address the limitations posed by small datasets, this paper proposes an intelligent data augmentation strategy (DAS)-based deep neural network (DNN) that enhances the localization accuracy of WSNs. The proposed DAS replicates the estimated positions of unknown nodes generated by the Dv-hop algorithm and introduces Gaussian noise to these replicated positions, creating multiple modified datasets. By combining the modified datasets with the original training data, we significantly increase the dataset size, which leads to a substantial reduction in normalized root mean square error (NRMSE). The experimental results demonstrate that this data augmentation technique significantly improves the performance of DNNs compared to the traditional Dv-hop algorithm at a low number of nodes while maintaining an efficient computational cost for data augmentation. Therefore, the proposed method provides a scalable and effective solution for enhancing the localization accuracy of WSNs.

Maximizing influence via link prediction in evolving networks

Influence Maximization (IM), targeting the optimal selection of k seed nodes to maximize potential information dissemination in prospectively social networks, garners pivotal interest in diverse realms like viral marketing and political discourse dissemination. Despite receiving substantial scholarly attention, prevailing research predominantly addresses the IM problem within the confines of existing networks, thereby neglecting the dynamic evolutionary character of social networks. An inevitable requisite arises to explore the IM problem in social networks of future contexts, which is imperative for certain application scenarios. In this light, we introduce a novel problem, Influence Maximization in Future Networks (IMFN), aimed at resolving the IM problem within an anticipated future network framework. We establish that the IMFN problem is NP-hard and advocate a prospective solution framework, employing judiciously selected link prediction methods to forecast the future network, and subsequently applying a greedy algorithm to select the k most influential nodes. Moreover, we present SCOL (Sketch-based Cost-effective lazy forward selection algorithm Optimized with Labeling technique), a well-designed algorithm to accelerate the query of our IMFN problem. Extensive experimental results, rooted in five real-world datasets, are provided, affirming the efficacy and efficiency of the proffered solution and algorithms.

A controllability method on the social Internet of Things (SIoT) network

In recent years, one type of complex network called the Social Internet of Things (SIoT) has attracted the attention of researchers. Controllability is one of the important problems in complex networks and it has essential applications in social, biological, and technical networks. Applying this problem can also play an important role in the control of social smart cities, but it has not yet been defined as a specific problem on SIoT, and no solution has been provided for it. This paper addresses the controllability problem of the temporal SIoT network. In this regard, first, a definition for the temporal SIoT network is provided. Then, the unique relationships of this network are defined and modeled formally. In the following, the Controllability problem is applied to the temporal SIoT network (CSIoT) to identify the Minimum Driver nodes Set (MDS). Then proposed CSIoT is compared with the state-of-the-art methods for performance analysis. In the obtained results, the heterogeneity (different types, brands, and models) has been investigated. Also, 69.80 % of the SIoT sub-graphs nodes have been identified as critical driver nodes in 152 different sets. The proposed controllability deals with network control in a distributed manner.

IOS-based mobile application for network performance analysis

In today's world, when technology is developing faster and faster, many people face network malfunctions and threats. Ensuring the security and performance of your own network is a critical task for many IT professionals and ordinary users. There are many tools and ways to monitor, analyse and secure your network, but a mobile app is an extremely convenient option. A mobile application that combines several network functions at once saves time and is always at hand. Networking tools in a smartphone can replace many different desktop applications and websites (web APIs), provide command line tools, etc. The iOS operating system has high security standards and its ecosystem is quite closed. This protects users from a lot of malware and makes the device extremely resistant to hacking. However, it also imposes some restrictions on the network and system for the average user. That's why iOS apps that include a variety of networking tools are a really useful tool for users. To keep the network in an efficient and secure state, iOS has specific ways to monitor and detect network problems: Wi-Fi analysers, checking network routes and interfaces, checking Internet speed, ping, traceroute, etc. The use of these technologies in the application under consideration will enable efficient analysis of network productivity.

Analisis Akuntabilitas Pelayanan Kenaikan Pangkat Pegawai Negeri Sipil

Dian Ekawati. Analysis of Accountability for Promotion of Civil Servant Ranks (Case Study of Application of SAPK BKN in BKD, South Sumatra Province). Accountability of Service Promotion for Civil Servants (Case Study of Application of SAPK BKN in BKD of South Sumatra Province) has basically been going well, but because there were some disturbances related to network problems and application system problems, so there were some things that were delayed in the management process. . It has not been added to the terms and files that are not appropriate so that many promotion files have not been approved and must be repaired. The supporting factor is the existence of technological advances that become service innovations, in the form of online applications, namely online SAPK in the management of promotions, it will provide convenience in the management of promotions. The inhibiting factor is that the existence of signal interference in several districts is a weak side of the online system that must be improved, as well as the requirements for completeness of files that have not been met by employees hampering the approval process for promotions.

Delivery Route Scheduling of Heterogeneous Robotic System with Customers Satisfaction by Using Multi-Objective Artificial Bee Colony Algorithm

This study addresses the route scheduling problem for the heterogeneous robotic delivery system (HRDS) that perform delivery tasks in an urban environment. The HRDS comprises two distinct types of vehicles: an unmanned ground vehicle (UGV), which is constrained by road networks, and an unmanned aerial vehicle (UAV), which is capable of traversing terrain but has limitations in terms of energy and payload. The problem is formulated as an optimal route scheduling problem in a road network, where the goal is to find the route with minimum delivery cost and maximum customer satisfaction (CS) enabling the UAV to deliver packages to customers. We propose a new method of route scheduling based on an improved artificial bee colony algorithm (ABC) and the non-dominated sorting genetic algorithm II (NSGA-II) that provides the optimal delivery route. The effectiveness and superiority of the method we proposed are demonstrated by comparison in simulations. Moreover, the physical experiments further validate the practicality of the model and method.

Analisis Penggunaan Metode XConnect Main and Backup Link Berbasis VPN MPLS Layer 2 pada Jaringan Metro-Ethernet

The rapid development of information and communication technology requires excellent scalability, which is crucial for network performance availability. MPLS is a technology that can be used for communication in high-speed backbone networks based on virtual private networks (VPN). MPLS provides solutions to various current computer network problems such as speed, scalability, quality of service (QoS), and network traffic management. This research aims to optimize the scalability of MPLS Layer 2 VPN-based network traffic by applying the xconnect main and backup method implemented in metro Ethernet networks. The network simulation implementation was created using the Emulated Virtual Environment – Next Generation (EVE-NG) application to obtain data on convergence time and QoS parameters according to TIPHON standards, as well as the use of MPLS and Route Reflector to enhance MPLS backbone performance. This research shows that both main and backup MPLS L2VPN paths have good index values in packet delivery according to TIPHON standards, with an average rating of 3.0. Thus, this research can help optimize path scalability to improve the quality of MPLS L2VPN network traffic.

Distributed enhanced multi-objective evolutionary algorithm based on decomposition for cluster analysis in wireless sensor network

Conventional clustering algorithms do not recognize patterns and structures with contradicting objectives in large, distributed datasets. Distributed clustering leverages rapid processing capabilities to allow multiple nodes to work together. This paper proposes a Distributed clustering based on Multiobjective Evolutionary Algorithm by Decomposition (D-MOEA/d) to solve various multiobjective optimization problems in wireless sensor networks (WSNs). In MOEA/d, a multiobjective optimization problem decomposes into several scalar optimization subproblems, each focusing on a distinct objective. Each subproblem is expressed as a clustering problem that uses local data to perform distributed clustering. The proposed method has been extended to achieve improved accuracy in less time by using a smaller feature subset with less redundancy. The Distributed Enhanced MOEA/d (DE-MOEA/d) avoids local optima by achieving diversity in the population using fuzzy-based nearest neighbor selection, sparse population initialization, and evolved mutation operator. This integration improves the accuracy of the clustering process at WSN nodes, ensuring the attainment of well-balanced solutions across multiple optimization criteria in the distributed environment. Average Euclidean and total symmetrical deviations are the two cost functions used to minimize while clustering on the MOEA/d framework. Six real-life WSN datasets are used to assess the performance of the proposed technique: (1) the Delhi air pollution dataset, (2) the Canada weather station dataset, (3) the Thames River water quality dataset, (4) the Narragansett Bay water quality dataset, (5) the Cook Agricultural land dataset and 6) Gordon Soil dataset. The simulation results of both proposed algorithms are compared with Multiobjective distributed particle swarm optimization (DMOPSO) and Distributed K-means (DK-Means). The proposed algorithm DE-MOEA/d performs better in terms of the Silhouette index (SI), Dunn index (DI), Davies–Bouldin index (DBI), and Kruskal–Wallis (KW) statistical test.

Adaptive sparse structure development with pruning and regeneration for spiking neural networks

Spiking Neural Networks (SNNs) are more biologically plausible and computationally efficient. Therefore, SNNs have the natural advantage of drawing the sparse structural plasticity of brain development to alleviate the energy problems of deep neural networks caused by their complex and fixed structures. However, previous SNNs compression works are lack of in-depth inspiration from the brain development plasticity mechanism. This paper proposed a novel method for the adaptive structural development of SNN (SD-SNN), introducing dendritic spine plasticity-based synaptic constraint, neuronal pruning and synaptic regeneration. We found that synaptic constraint and neuronal pruning can detect and remove a large amount of redundancy in SNNs, coupled with synaptic regeneration can effectively prevent and repair over-pruning. Moreover, inspired by the neurotrophic hypothesis, neuronal pruning rate and synaptic regeneration rate were adaptively adjusted during the learning-while-pruning process, which eventually led to the structural stability. Experimental results on spatial and temporal datasets demonstrate that our method can flexibly learn appropriate compression rate for various tasks and effectively achieve superior performance while massively reducing the network energy consumption. Specifically, for the neuromorphic DVS-Gesture dataset, 98.56% accuracy with 1.45% improvement is achieved by our method when the compression rate reaches 61.10%. Our code is available at: https://github.com/BrainCog-X/Brain-Cog/tree/main/examples/Structural_Development/SD-SNN.

Design of ant colony planning algorithms for multi-stage grids of distribution networks considering network resilience

Network Destruction Resilience is the ability of a system to maintain good operation in the event of an external attack or internal failure. The resilience of distribution network is crucial to guarantee the reliability of power supply. In this paper, we design a planning algorithm considering network destructibility for the multi-stage ant colony planning problem of distribution networks. The method establishes the multi-stage network planning objective function of distribution network from the perspectives of total investment cost and annual operation cost of multi-node network frame, destruction resistance, active power and reactive network loss of distribution network, etc. Then based on the constraints of the model, the improved ant colony algorithm is used to solve the multi-stage network planning objective function of distribution network, and the results of the anti-termite colony planning of multi-stage network frame of distribution network are obtained. In order to verify the effectiveness of the algorithm, simulation experiments are carried out on real distribution network data. The results show that the proposed ant colony planning algorithm for multi-stage grid frames can effectively improve the destruction resistance of distribution networks, and reduce the total investment cost and annual operation cost of multi-stage grid frames, and reduce the network loss rate data of multi-stage grid frames of distribution networks after application. It provides an effective method for planning the distribution network.

Coordinated Planning of Soft Open Points and Energy Storage Systems to Enhance Flexibility of Distribution Networks

With the large-scale penetration of distributed generation (DG), the volatility problems of active distribution networks (ADNs) have become more prominent, which can no longer be met by traditional regulation means and need to be regulated by introducing flexible resources. Soft open points (SOP) and energy storage systems (ESS) can regulate the tidal currents on spatial and temporal scales, respectively, to improve the flexibility of ADN. To this end, in-depth consideration of DG admission is given to establish flexibility assessment indicators from the power side of ADN. The conditional deep convolution generative adversarial network (C-DCGAN) is used to generate the output scenario of DG. On this basis, the SOP and ESS two-layer planning models, which take account of the potential for improvement in the flexibility of ADN, are constructed. Among them, the upper layer is the site selection and volume determination layer, which considers the economy of the system with the optimization objective of minimizing the annual integrated cost; the lower layer is the operation optimization layer, which considers the flexibility of the system and takes the highest average daily flexibility level as the optimization objective. The planning model is solved using genetic algorithm-particle swarm optimization (GA-PSO) and second-order cone programming (SOCP). The case analysis verifies the rationality and effectiveness of the planning model.

Improved event-based fault detection filter for networked fuzzy systems under DoS attacks

This paper investigates an improved event-based fault detection method for networked fuzzy systems under denial-of-service (DoS) attacks. In order to solve the bandwidth occupation problem of communication network, a resilient event-triggered transmission strategy is developed. Additionally, a fault detection filter is designed to estimate the time of fault occurrence by using the residual signal. Under this framework, a novel Lyapunov functional related to attack parameters is established to analyze the exponential convergence of the error signals, and the filter gains and event-triggered parameters are obtained by solving linear matrix inequalities. The designed functional reduces the conservatism of the stability criteria significantly in contrast with the previous discontinuous Lyapunov functionals. Finally, a simulation example is provided to verify the effectiveness of the proposed method.

Event-triggered impulsive control for nonlinear stochastic delayed systems and complex networks

In this paper, we probe the pth moment exponential stability (p−ES) of stochastic delayed systems subject to event-triggered delayed impulsive control (ETDIC), where the impulsive intensities are assumed to be positive random variables. Based on event-triggered mechanism (ETM) in the sense of expectation, some new sufficient conditions are developed to ensure the stability of the addressed system with Zeno-free behavior. The Lyapunov–Razumikhin method is adopted to handle the time-varying delay in continuous dynamics, and the concept of average random impulsive estimation (ARIE) is introduced to reduce the design requirements of the controller. Especially, the proposed ETDIC strategy not only generates the impulse time sequence according to the predesigned ETM, but also removes the limitations on the size of time delays. Furthermore, the ETM serves as a solution for synchronization problems in complex neural networks. Finally, two examples are given to illustrate the effectiveness of our conclusions.

UMF-Net: A UNet-based multi-branch feature fusion network for colon polyp segmentation

The early diagnosis of colorectal cancer heavily relies on colonoscopy, but clinical examinations often face challenges in detecting polyps due to various influencing factors. Polyp segmentation models can be categorized into two architectures: those based on convolutional neural networks (CNNs), which specialize in local modeling, and those based on Transformers, which excel at capturing global context. However, existing methods that combine these architectures often do so simply without addressing the large parameter size of Transformer architectures and the fixed weights problem of convolutional networks. In our study, we propose a Transformer-to-CNNs block by combining advanced components from Transformers with deformable convolutions (DCN) to replace the Transformer block. Deformable convolutions (DCN) enable learning offsets to capture long-range dependencies, apply adaptive weights, and select only a fixed number of pixels, thereby reducing the parameter size effectively. Based on approach, we introduce a novel architecture called UMF-Net (UNet-based Multi-Branch Feature Fusion Network), which adopts a multi-branch architecture and a unique feature fusion strategy to aggregate features from these branches. Additionally, we incorporate a Residual Coarse-to-Fine block to enhance the model’s learning capabilities further. In our evaluation process, we assess the performance of our proposed method using metrics such as mDice, mIoU, Fβω, Sα, mEξ, and MAE on four widely used benchmark datasets. The experimental results demonstrate that UMF-Net outperforms other state-of-the-art methods while effectively improving colon polyp segmentation accuracy.

A Mobile-Based Network Tools Information System to Enhanced Connectivity and Security

This paper addresses the common challenges faced by network administrators in managing and troubleshooting network issues. Traditional desktop-based network management tools often restrict mobility and responsiveness, hindering the ability to address network problems promptly. To address these limitations, this study presents the development of a mobile-based network tools information system. The proposed solution integrates various network management functionalities into a mobile application, enabling administrators to monitor, diagnose, and resolve network issues remotely. Employing a design and implementation approach, the research includes user requirements analysis, system design, and rigorous testing. Results show significant improvements in response times and diagnostic accuracy. User feedback highlights high satisfaction with the system's functionality and ease of use. Overall, the mobile-based network tools information system offers a versatile and effective solution for network management, enhancing operational efficiency and responsiveness in diverse network environments.

Obtaining and qualitative analysis of time-lagged correlations between seawater quality parameters

Abstract In the regulation of seawater quality, it is crucial to understand the interactions between parameters and the time-lagged effects. This paper focuses on the problem of how to obtain and analyze time-lagged correlations between seawater quality parameters, an issue that has not attracted the attention of researchers. In this paper, a graph neural network-based model, Dynamic Adjacency Weight Network, is proposed to model the time-lagged correlation between seawater quality parameters. By regarding the parameters as nodes, the proposed model retains the relationships between the parameters in a weighted adjacency matrix, overcoming the problem of traditional deep neural networks that are difficult to be visualized. Meanwhile, the proposed multivariate multistep shift prediction strategy enables the proposed model to accurately obtain the time-lagged correlation information between parameters at different time intervals. In addition, the proposed model also addresses some of the details worth considering when obtaining correlations between seawater quality parameters. The proposed model performs well in the prediction of water quality parameters. This indirectly verifies the validity of the obtained correlations and overcomes the difficulty of verifying the validity of statistical methods. This study provides new ideas and methods for seawater quality monitoring and research.

Sink location problems in dynamic flow grid networks

A dynamic flow network consists of a directed graph, where nodes called sources represent locations of evacuees, and nodes called sinks represent locations of evacuation facilities. Each source and each sink are given supply representing the number of evacuees and demand representing the maximum number of acceptable evacuees, respectively. Each edge is given capacity and transit time. Here, the capacity of an edge bounds the rate at which evacuees can enter the edge per unit time, and the transit time represents the time which evacuees take to travel across the edge. The evacuation completion time is the minimum time at which each evacuee can arrive at one of the evacuation facilities. Given a dynamic flow network without sinks, once sinks are located on some nodes or edges, the evacuation completion time for this sink location is determined. We then consider the problem of locating sinks to minimize the evacuation completion time, called the sink location problem. The problems have been given polynomial-time algorithms only for limited networks such as paths [1–3], cycles [1], and trees [4–6], but no polynomial-time algorithms are known for more complex network classes. In this paper, we prove that the 1-sink location problem can be solved in polynomial-time when an input network is a grid with uniform edge capacity and transit time.