Network Configuration Research Articles

Mathematical Modeling and Analysis of Energy Aware Probabilistic Distribution Based Cluster Head Selection Algorithm for Wireless Sensor Networks

From a mathematical perspective, Wireless Sensor Networks (WSNs) are increasingly recognized for their utility across multiple sectors such as environmental oversight, healthcare monitoring, and process automation within industries. A paramount obstacle within WSNs is the management of energy efficiency, given that the sensor nodes are typically powered by batteries with finite energy reserves. The criticality of developing efficient routing protocols cannot be overstated, as they are instrumental in extending the operational lifespan of the network and guaranteeing dependable data communication. This study is centered around the mathematical modelling and performance evaluation of an innovative routing methodology that integrates machine learning algorithms to augment energy efficiency within WSNs. The novel routing strategy dynamically adjusts its operations in response to the immediate environmental and network states, aiming to minimize energy expenditure and elevate the network's overall efficiency. Through comprehensive numerical simulations, this research scrutinizes the efficacy of the machine learning-enhanced routing protocol against conventional routing methodologies, accentuating its advantages in energy savings and reliability in data transmission. The simulation framework encompasses a variety of network configurations, traffic distributions, and environmental contexts, employing metrics such as energy utilization, network longevity, packet delivery ratio, and latency to offer an in-depth examination of the machine learning-based routing approach's performance. Findings from the simulations affirm the algorithm's enhanced energy efficiency, which contributes to prolonged operation of sensor nodes and steadfast data communication across dynamically changing network landscapes. The implications of this study highlight the transformative potential of machine learning in redefining routing protocol design and optimization within energy-restricted WSNs. By elevating both energy efficiency and network functionality, this research marks a significant stride towards realizing sustainable and dependable WSNs, paving the way for their broader application in essential services.

Impact of Indirect Tax on the Gross Domestic Product of Nepalese Economy

This study investigated the impact of indirect tax on GDP in Nepal from fiscal year 2057/2058 to 2078/2079 based on empirical evidence. Descriptive and analysis research designed was used. To meet this objective, time series macroeconomic data of GDP and indirect tax were used. Data are collected from economic survey, ministry of finance. The ordinary Least Square technique was employed to test the hypotheses formulated. The result shows that indirect tax contributes significantly to GDP. During the periods under review, the growth rate of indirect tax revenue was 16.52 % on average and the average ratio of indirect tax revenue to that of GDP was 9.98 %. The coefficient of determination was 98.4 %. P-value is very low (p < 0.001). The hypothesis suggests a positive relationship between indirect tax revenue and GDP in Nepal. The regression model indicates significance, rejecting the null hypothesis. Indirect tax revenue plays a crucial role in Nepal's GDP. The findings also reveal that there is the existence of both a positive and strong relationship between indirect tax revenue and GDP. Hence, the Government of Nepal should search for a way to boost the revenue from indirect tax by mostly supporting the configurations of networks among all the agencies of government and taxing authorities of the federal level, each providence, and local bodies to meet the growth and to facilitate public services for the country.

Circulatory system-based optimization algorithm with dynamic penalty function for optimum design of large-scale water distribution networks

Water distribution networks (WDNs) are a crucial component of urban infrastructure, and their optimization plays a vital role in minimizing construction costs. However, existing heuristic algorithms for WDN optimization often rely on specific parameters, limiting their performance and hindering their applicability to diverse network configurations, particularly in large-scale systems. To address this challenge, a novel method called circulatory system-based optimization (CSBO) is proposed, which minimizes the need for algorithm-specific parameters and achieves optimal designs across WDNs of various scale. The impact of a dynamic penalty function tailored to pressure constraints on the optimal design is also investigated. This study encompasses the optimization of the Goyang and combined gravity networks and the large-scale Balerma irrigation and Kang and Lansey networks under identical conditions. The results of the standard and dynamic CSBO approaches prove that the proposed algorithms have a superior performance, particularly in convergence rate and stability, compared to previous methods.

Exploring the potential of AI-driven optimization in enhancing network performance and efficiency

The exponential growth of network complexity and data volume in modern digital ecosystems has underscored the need for innovative approaches to optimize network performance and efficiency. This paper delves into the potential of AI-driven optimization techniques in addressing this imperative. Leveraging artificial intelligence (AI) algorithms such as machine learning and deep learning, the study investigates how AI can revolutionize network management and operation to achieve higher levels of performance and reliability. Through a comprehensive review of existing literature and case studies, this paper elucidates the fundamental principles, methodologies, and applications of AI-driven optimization in diverse network environments. It examines how AI algorithms can analyze vast amounts of network data, identify patterns, and make data-driven decisions to optimize network configurations, routing protocols, and resource allocation strategies. Moreover, the study explores how AI-driven optimization can enhance network security, fault tolerance, and scalability by autonomously detecting and mitigating potential threats and vulnerabilities. The Review succinctly encapsulates the main findings and insights derived from the analysis, emphasizing the transformative potential of AI-driven optimization for network performance and efficiency enhancement. It underscores the benefits of AI-driven approaches in automating complex optimization tasks, reducing operational overhead, and adapting dynamically to changing network conditions and user demands. Additionally, the Review discusses the challenges and considerations associated with the implementation of AI-driven optimization techniques, including algorithmic bias, data privacy concerns, and ethical implications. In conclusion, the Review underscores the critical role of AI-driven optimization in addressing the evolving challenges of network management and operation. It advocates for continued research and development efforts aimed at harnessing the full potential of AI-driven optimization to unlock new levels of performance and efficiency in network infrastructures. By embracing AI-driven approaches, organizations can streamline network operations, improve user experience, and drive innovation in the digital era.

Implementation of a Lossless Moving Target Defense Mechanism

The contemporary world, dominated by information technologt (IT), necessitates sophisticated protection mechanisms against attacks that pose significant threats to individuals, companies, and governments alike. The unpredictability of human behavior, coupled with the scattered development of applications and devices, complicates supply chain maintenance, making it impossible to develop a system entirely immune to cyberattacks. Effective execution of many attack types hinges on prior network reconnaissance. Thus, hindering effective reconnaissance serves as a countermeasure to attacks. This paper introduces a solution within the moving target defense (MTD) strategies, focusing on the mutation of Internet protocol (IP) addresses in both edge and core network switches. The idea of complicating reconnaissance by continually changing IP addresses has been suggested in numerous studies. Nonetheless, previously proposed solutions have adversely impacted the quality of service (QoS) levels. Implementing these mechanisms could interrupt Transmission Control Protocol (TCP) connections and result in data losses. The IP address mutation algorithms presented in this study were designed to be fully transparent to transport layer protocols, thereby preserving the QoS for users without degradation. In this study, we leveraged the benefits of software-defined networking (SDN) and the Programming-Protocol-Ondependent Packet Processors (P4) language, which specifies packet processing methodologies in the data plane. Employing both SDN and P4 enables a dynamic customization of network device functionalities to meet network users’ specific requirements, a feat unachievable with conventional computer networks. This approach not only enhances the adaptability of network configurations but also significantly increases the efficiency and effectiveness of network management and operation.

A Comparative Study of Quality of Service (QoS) Metrics in Reactive Routing Protocols DSR and AODV in Manet

This study is based on the analysis of specific Quality of Service (QoS) metrics. The acquisition of these metrics was carried out by modifying codes in C++ language within the Ns3 network simulation software. The choice of the Random Way Point mobility model contributed to the generation of metrics, which were subsequently used in the evaluation and comparison of two selected protocols, DSR and AODV. These evaluations focused on critical parameters such as Throughput, Delay, and Jitter. To conduct meaningful comparisons, three different scenarios were designed, each characterized by the variation in the number of nodes used. This approach allowed for a comprehensive assessment of the protocols' effectiveness in different MANET network configurations. Ultimately, the selection of the most accurate protocols was based on a detailed analysis of metrics in various MANET scenarios. This process provided a deeper understanding of how DSR and AODV perform in specific environments, enabling the identification of more effective protocols according to the particular demands of each scenario.

A Stereo Camera Simulator for Large‐Eddy Simulations of Continental Shallow Cumulus Clouds Based on Three‐Dimensional Path‐Tracing

AbstractThe complex spatial and temporal structure of cumulus clouds complicates their representation in weather and climate models. Classic meteorological instrumentation struggles to fully capture these features. Networks of multiple high‐resolution hemispheric cameras are increasingly used to fill this data gap, and provide information on this missing multi‐dimensional spatial information. In this study, a path‐tracing algorithm is used to generate virtual camera images of resolved clouds in large‐eddy simulations (LES). These images are then used as a camera network simulator, allowing reconstructions of three‐dimensional cloud edges from the model output. Because the actual LES cloud field is fully known, the combined path‐tracing and reconstruction method can be statistically analyzed. The method is applied to LES realizations of summertime shallow cumulus at the Jülich Observatory for Cloud Evolution (JOYCE), Germany, which also routinely operates a camera network. We find that the path‐tracing method allows accurate reconstruction of up to 70% of the visible cloud edges. Additional sensitivity tests show that the method is robust for changes in its hyperparameters. The sensitivity to cloud optical thickness is also investigated, finding a cloud boundary placement error of approximately 182 m. This error can be considered typical for cloud boundary reconstruction using real stereo camera imagery. The results provide proof of principle for future use of the method for evaluating LES clouds against camera network imagery, and for further optimizing the configuration of such camera networks.

Linear Anti-interference Algorithm for Digital Signal Transmission in Fiber Optic Communication Networks based on Link Analysis

In order to achieve accurate transmission of protection signals in fiber optic communication networks, it is necessary to perform channel balancing configuration of fiber optic communication networks and adaptive forwarding control processing of relay protection signals, the author proposes an accurate transmission method for relay protection signals in fiber optic communication networks based on time-varying multipath fading suppression and adaptive beamforming. The system analyzes the sources of wireless long-distance pain signal interference signals, introduces anti-interference technologies such as two-dimensional joint processing (STAP), provides anti-interference algorithms and related gain analysis, and conducts signal processing gain simulation using MATLAB. Based on the analysis of comprehensive simulation results, at a given symbol length, the signal bandwidth increases, and the processing gain infinitely approaches the given theoretical limit value, rather than increasing nonlinearly. The reason is that the channel is affected by noise, and the channel estimation value and signal conjugate multiplication produce a noise quadratic term. At this point, the estimated value of the coherent region channel is reduced by the influence of noise, and the signal-to-noise ratio loss caused by the noise quadratic term is reduced, so the processing gain increases. During the process of infinite increase in signal bandwidth, the input signal-to-noise power ratio of the receiver tends to decrease towards an infinite value, limited by the size of the coherent region. The channel estimation value increases under the influence of noise, and the noise quadratic term is the main factor affecting the output noise power. When the symbol length is greater than the coherent time, the smaller the maximum Doppler frequency shift and the larger the coherent detection area, the greater the processing gain.

Sensor placement algorithm for faults detection in electrical secondary distribution network using dynamic programming method: focusing on dynamic change and expansion of the network configurations

Modern power grids are developing toward smartness through the use of sensors in gathering data for situation awareness, visibility, and fault detection. In most developing countries, fault detection in the electrical secondary distribution network (SDN) is very challenging due to the lack of automated systems for network monitoring. Systems for monitoring faults require sensor placement on each node, which is not economically feasible. Hence, optimal placement algorithms are required to ensure that the network is observable with few sensors possible. The existing sensor placement methods based on mathematical and heuristic approaches are efficient for transmission and primary distribution networks which are mostly static in size and layout. Such methods may not be efficient in SDN which is dynamic in size and have a relatively large number of nodes. This study proposes an enhanced dynamic programming method for sensor placement to enhance fault detection in SDN. The proposed algorithm employs the depth search concepts and the parent–children relationship between nodes to determine sensor types and locations considering the optimal cost. The proposed algorithm was compared with other methods including particle swarm optimization, genetic algorithm, and chaotic crow search algorithm using different network configurations. The results revealed that the proposed algorithm suggested the minimum number of sensors and shortest convergence time of 1.27 min. The results also revealed that, on network expansion, maintaining the location of the existing sensors is more cost-effective by 20% than reallocating the existing sensors. Furthermore, the results revealed that an average of 30% of nodes, need sensors to observe the entire network, hence cost optimization.

R&D team network configurations, knowledge diversity and breakthrough innovation: a combined effect framework

PurposeThis study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.Design/methodology/approachBased on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.FindingsIt has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.Originality/valuePrevious studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection

This study critically reviews the scientific literature regarding machine-learning approaches for optimizing smart bin collection in urban environments. Usually, the problem is modeled within a dynamic graph framework, where each smart bin’s changing waste level is represented as a node. Algorithms incorporating Reinforcement Learning (RL), time-series forecasting, and Genetic Algorithms (GA) alongside Graph Neural Networks (GNNs) are analyzed to enhance collection efficiency. While individual methodologies present limitations in computational demand and adaptability, their synergistic application offers a holistic solution. From a theoretical point of view, we expect that the GNN-RL model dynamically adapts to real-time data, the GNN-time series predicts future bin statuses, and the GNN-GA hybrid optimizes network configurations for accurate predictions, collectively enhancing waste management efficiency in smart cities.

Exploring the Potential of Wireless Integrated Configuration Technology as a Learning Tool

The objective of this study was to determine the feasibility of utilising wireless integrated configuration technology among students enrolled in networking courses. The proposed method entails using Raspberry Pi as the key hardware component to facilitate wireless configuration, hence facilitating seamless connectivity and communication across wireless devices. The networking solution facilitates the transport of data, exchange of resources, and cooperation in a wireless manner, eliminating the need for physical cables or wires. The research methodology for this study entails descriptive analysis in the form of a survey, which will have an impact on the quantitative findings. The results of the needs study found that over 90% of students agreed that the use of wireless configuration devices would improve understanding and interest them in doing network configuration. Based on the findings from interview sessions with experts, it has been demonstrated that the utilisation of wireless integrated configuration technology possesses the capacity to engender an enjoyable learning experience for students. This technology enables students to engage in configuration tasks wirelessly, utilising numerous devices concurrently. Additionally, experts believe that students can learn how to configure it technically as well as theoretically. The results of this study were successful in determining the level of potential for the development of wireless devices and their usage in networking courses.

A Spatiotemporal Hierarchical Analysis Method for Urban Traffic Congestion Optimization Based on Calculation of Road Carrying Capacity in Spatial Grids

Traffic congestion is a globally widespread problem that causes significant economic losses, delays, and environmental impacts. Monitoring traffic conditions and analyzing congestion factors are the first, challenging steps in optimizing traffic congestion, one of the main causes of which is regional spatiotemporal imbalance. In this article, we propose an improved spatiotemporal hierarchical analysis method whose steps include calculating road carrying capacity based on geospatial data, extracting vehicle information from remote sensing images to reflect instantaneous traffic demand, and analyzing the spatiotemporal matching degree between roads and vehicles in theory and in practice. First, we defined and calculated the ratio of carrying capacity in a regional road network using a nine-cell-grid model composed of nested grids of different sizes. By the conservation law of flow, we determined unbalanced areas in the road network configuration using the ratio of the carrying capacity of the central cell to that of the nine grid cells. Then, we designed a spatiotemporal analysis method for traffic congestion using real-time traffic data as the dependent variables and five selected spatial indicators relative to the spatial grids as the independent variables. The proposed spatiotemporal analysis method was applied to Chengdu, a typical provincial capital city in China. The relationships among regional traffic, impact factors, and spatial heterogeneity were analyzed. The proposed method effectively integrates GIS, remote sensing, and deep learning technologies. It was further demonstrated that our method is reliable and effective and enhances the coordination of congested areas by virtue of a fast calculation speed and an efficient local balance adjustment.

Attachment relationship quality with mothers and fathers and child temperament: An individual participant data meta-analysis.

A growing body of research suggests that, compared with single parent-child attachment relationships, child developmental outcomes may be better understood by examining the configurations of child-mother and child-father attachment relationships (i.e., attachment networks). Moreover, some studies have demonstrated an above-chance level chance of concordance between the quality of child-mother and child-father attachment relationships, and child temperament has been offered as a plausible explanation for such concordance. To assess whether temperament plays a role in the development of different attachment network configurations, in this preregistered individual participant data meta-analysis we tested the degree to which the temperament dimension of negative emotionality predicts the number of secure, insecure-avoidant, insecure-resistant, and disorganized attachment relationships a child has with mother and father. Data included in the linear mixed effects analyses were collected from seven studies sampling 872 children (49% female; 83% White). Negative emotionality significantly predicted the number of secure (d = -0.12) and insecure-resistant (d = 0.11), but not insecure-avoidant (d = 0.04) or disorganized (d = 0.08) attachment relationships. Nonpreregistered exploratory analyses indicated higher negative emotionality in children with insecure-resistant attachment relationships with both parents compared to those with one or none (d = 0.19), suggesting that temperament plays a small yet significant role in child-mother/child-father insecure-resistant attachment relationships concordance. Taken together, results from this study prompt a more in-depth examination of the mechanism underlying the small yet significantly higher chance that children with increased negative emotionality have for developing multiple insecure-resistant attachment relationships. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Monte Carlo simulations of spherocylinders interacting with site-dependent square-well potentials

Monte Carlo simulations are performed to study the self-assembly of a dilute system of spherocylinders interacting with square-well potential. The interactions are defined between randomly placed sites on the axis of the spherocylinder, akin to the interacting groups on a rigid rodlike molecule. This model therefore also serves as a minimal coarse-grained representation of a system of low molecular weight or stiff polymers with contour lengths significantly lower than the persistence length, interacting predominantly with short-range interactions (e.g., hydrogen bonding). The spherocylinder concentration, square-well interaction strength and range, and fraction of interacting sites are varied to study the phase behavior of the system. We observe the formation of dispersed, bundled, and network configurations of the system that may be compared with previous atomistic simulation results of weak polyelectrolytes.

Resting-state brain network connectivity is an independent predictor of responsiveness to language therapy in chronic post-stroke aphasia

Post-stroke aphasia recovery, especially in the chronic phase, is challenging to predict. Functional integrity of the brain and brain network topology have been suggested as biomarkers of language recovery. This study sought to investigate functional connectivity in four predefined brain networks (i.e., language, default mode, dorsal attention, and salience networks), in relation to aphasia severity and response to language therapy. Thirty patients with chronic post-stroke aphasia were recruited and received a treatment targeting word finding. Structural and functional brain scans were acquired at baseline and resting state functional connectivity for each network was calculated. Additionally, graph measures quantifying network properties were calculated for each network. These included global efficiency for all networks and average strength and clustering coefficient for the language network. Linear mixed effects models showed that mean functional connectivity in the default mode, dorsal attention, and salience networks as well as graph measures of all four networks are independent predictors of response to therapy. While greater mean functional connectivity and global efficiency of the dorsal attention and salience networks predicted greater treatment response, greater mean functional connectivity and global efficiency in the default mode network predicted poorer treatment response. Results for the language network were more nuanced with more efficient network configurations (as reflected in graph measures), but not mean functional connectivity, predicting greater treatment response. These findings highlight the prognostic value of resting-state functional connectivity in chronic treatment-induced aphasia recovery.

Integrated Departure Time and Parking Location Choices in a Morning Commute Problem under a Partially Automated Environment

This study formulates the joint decisions of commuters on departure time and parking location choices in a morning commute problem where the commuters travel with autonomous vehicles (AVs) or human-driven vehicles (HVs). Under a mixed traffic environment, we aim to explore the impacts of parking capacity and parking pricing on the equilibrium travel pattern and the system performance. We build a dynamic equilibrium model for the morning commute problem by assuming that the parking slots can be grouped into central and peripheral clusters based on the distance between the parking location and the workplace. We first analyze the parking location preferences of commuters towards the two parking clusters under a mixed traffic environment. We then examine the equilibrium conditions and identify all the equilibrium travel patterns. We further analyze the system performance measured by the total travel cost with respect to the parking prices and the capacity of the central cluster. The optimal parking pricing scheme is also derived to minimize the total travel cost. We conduct numerical analysis to demonstrate the change in the total travel cost against the parking capacity of the central cluster and its parking price. Sensitivity analysis is performed to show the impacts of the network configuration on the total travel cost.

Vibrating Micro-Capsule as an Autonomous Device for in-situ Endoscopic Scan and Body Tissue Characterization

This work concerns the implementation of a diagnostic technique for the digestive system using a device designed based on a spherical vibrating capsule of 1 cm in diameter and autonomous by its onboard electronics. For local scanning, this device is also equipped with a network of high-frequency optimized sensors. Depending on the desired diagnosis, this device can adapt its electromechanical characteristics to the nature of the application in transmission and/or simultaneous reception. It is also designed for network configuration to provide a 3D mapping of the acoustic properties of the environment under investigation (biological tissues).

Strategic Manufacturing Network Performance Assessment Tool

Abstract The long-term configuration of the production network includes the allocation of products and processes. Numerous, sometimes conflicting objectives are pursued, such as market access, economies of scale and risk mitigation. However, in order to systematically configure the network, it is necessary to strategically analyse the strengths and weaknesses of the sites. This article presents a fuzzy inference-based approach for the holistic evaluation of the production networks. The tool was applied in two practical case studies. The results and implications are also presented.

Current Topics in International Manufacturing

Abstract Designing the strategy, configuration and coordination of international manufacturing networks is a complex task, influenced by a variety of internal and external factors. Therefore, there is no one-size-fits-all approach for the design and management of international manufacturing networks, especially with the rising volatility of the last years. For this purpose, four different industrial use cases are presented to show topics in international manufacturing that companies are currently dealing with and how they are addressing them.