Network Designers Research Articles

Rethinking max-min planning on energy-efficient software-defined networking for 5G networks.

Energy efficiency plays an important role in intelligent networking for 5G networks, which concerns environmental, financial, and performance aspects of intelligent networking for 5G networks. To this end, network designers propose energy-efficient approaches to reduce energy consumption of networks and to raise network performance by switching off the links/nodes with low loads or at idle status. The existing energy-efficient approaches can be formulated as a max-min optimal problem, namely maximizing network/node/port throughput via minimum energy consumption. The max-min planning investigates energy efficiency only from the links/nodes perspective. The max-min planning for energy-efficient networking, if not carefully designed from the network-wide standpoint, can lead to lower energy efficiency for the whole network due to lack of global planning, which in turn results in the degraded performance due to network un-connectivity after closing the nodes/links. In this paper we rethink the max-min planning framework on energy-efficient software-defined networking for intelligent networking of 5G networks, which takes in account combining network connectivity and maximum network flow with minimum energy consumption. Our framework aims at how to deliver dynamic end-to-end traffic demands with the appropriate network topology by building data forwarding plane with maximum network flow and control plane with network connectivity. We discuss the associated challenges and implementation issues. A dynamic max-min planning framework depending on dynamic end-to-end traffic demands is presented to achieve network-wide energy efficiency. Numerical results show the improved energy efficiency performance for the whole network.

Network Functions in Cloud: Kubernetes Deployment Challenges

The rapid evolution of cloud computing has paved the way for deploying network functions (NFs) in cloud environments, significantly enhancing the flexibility, scalability, and efficiency of modern network infrastructures. Kubernetes, an open-source container orchestration platform, has emerged as a leading tool for deploying and managing these cloud-based network functions. However, despite its widespread adoption, Kubernetes presents several deployment challenges specific to network functions, stemming from its design, scalability, and operational intricacies. This paper delves into the core challenges faced during the deployment of network functions on Kubernetes, focusing on issues related to network performance, security, service orchestration, and resource management. The abstract aims to provide an overview of the technical hurdles and propose potential strategies to overcome them, thus contributing to the optimization of Kubernetes-based NF deployments in cloud environments. By analyzing existing literature and case studies, the paper identifies key areas where improvements are needed and discusses the implications of these challenges for the future of cloud-based network functions. Ultimately, the paper seeks to guide network architects and cloud engineers in better understanding the complexities of Kubernetes deployments for network functions and in developing more effective strategies for successful implementation.

Simulative analysis of carrier suppressed return to zero based symmetrical compensated optical link

Abstract Optical communication systems provide high data rates to fulfil users’ demands. In addition, the modulation format is essential to the system’s transmission quality and spectral efficiency. Furthermore, the effects of chromatic dispersion, which impair the performance and quality of transmitted signals, are mitigated using dispersion compensation techniques. In optical communication systems, dispersion compensation techniques are essential for preserving signal quality, increasing transmission distances, improving system performance, and offering network designers flexibility. These methods seek to improve signal quality and enable transmission at longer distances by reducing or eliminating the distortion brought on by dispersion. This paper uses a symmetrical compensation technique to analyze the performance of the CSRZ modulation format-based optical communication system. The research is conducted within a communication range of 150–350 km. The system is evaluated in terms of input power, filter order, and filter depth. The results indicate that the proposed system performed better at an input power of 4 dBm using a third-order filter and 60 dB filter depth.

Enhanced resource optimization with the unevenness coefficients use in regular and irregular graph-based networks

The paper presents a simulation-based study of the impact of transmission resource reallocation on the performance of a communication network. The study considers the unevenness coefficients use to control network resources allocation. The results show that adjusting the transmission resource allocation based on the above-mentioned parameter can significantly improve network performance, leading to a equalization of network characteristics, and all of this without increasing the resources pool. The study’s findings provide valuable insights for network designers and operators seeking to optimize network performance in communication systems.

A Low-Computational Burden Closed-Form Approximated Expression for MSE Applicable for PTP with gfGn Environment

The Precision Time Protocol (PTP) plays a pivotal role in achieving precise frequency and time synchronization in computer networks. However, network delays and jitter in real systems introduce uncertainties that can compromise synchronization accuracy. Three clock skew estimators designed for the PTP scenario were obtained in our earlier work, complemented by closed-form approximations for the Mean Squared Error (MSE) under the generalized fractional Gaussian noise (gfGn) model, incorporating the Hurst exponent parameter (H) and the a parameter. These expressions offer crucial insights for network designers, aiding in the strategic selection and implementation of clock skew estimators. However, substantial computational resources are required to fit each expression to the gfGn model parameters (H and a) from the MSE perspective requirement. This paper introduces new closed-form estimates that approximate the MSE tailored to match gfGn scenarios that have a lower computational burden compared to the literature-known expressions and that are easily adaptable from the computational burden point of view to different pairs of H and a parameters. Thus, the system requires less substantial computational resources and might be more cost-effective.

Analysis of WiFi Reliability at 2.4 GHz and 5 GHz Frequencies in the Environment of STMIK Sinar Nusantara Surakarta

This research is expected to provide solutions to improve the performance and reliability of the WiFi network at STIMIK Sinar Nusantara, thus supporting teaching and learning activities and enhancing the quality of education. By comparing the quality of WiFi networks between 2.4 GHz and 5 GHz, it aims to shed light on the differences and advantages of each frequency band. Furthermore, the findings of this research can be beneficial for the design of future networks, helping network designers to better understand the importance of frequency management in network infrastructure planning. This research employs an experimental method by testing QoS parameters (throughput, delay, and packet loss) as well as reliability (connection and uptime) at both frequencies. The results of the research indicate that the 5 GHz frequency provides better QoS performance and reliability compared to 2.4 GHz. This research concludes that the 5 GHz frequency could be a solution to improve the WiFi network quality at STIMIK Sinar Nusantara

An exact method for trilevel hub location problem with interdiction

In this paper, we study the problem of designing a hub network that is robust against deliberate attacks (interdictions). The problem is modeled as a three-level, two-player Stackelberg game, in which the network designer (defender) acts first to locate hubs to route a set of flows through the network. The attacker (interdictor) acts next to interdict a subset of the located hubs in the designer’s network, followed again by the defender who routes the flows through the remaining hubs in the network. We model the defender’s problem as a trilevel optimization problem, wherein the attacker’s response is modeled as a bilevel hub interdiction problem. We study such a trilevel problem on three variants of hub location problems studied in the literature namely: p-hub median problem, p-hub center, and p-hub maximal covering problems. We present a cutting plane based exact method to solve the problem. The cutting plane method uses supervalid inequalities, which is obtained from the solution of the lower level interdiction problem. To solve the lower level hub interdiction problem efficiently, we propose a penalty-based reformulation of the problem. Using the reformulation, we present a branch-and-cut based exact approach to solve the problem efficiently. We conduct experiments to show the computational advantages of the above algorithm. To the best of our knowledge, the cutting plane approach proposed in this paper is among the first exact method to solve trilevel location–interdiction problems. Our computational results show interesting implications of incorporating interdiction risks in the hub location problem.

Analyzing Data Transmission Reliability in Mobile Ad-Hoc Networks under Dynamic Scenarios

Mobile ad hoc networks (MANETs) face inherent challenges in maintaining reliable data transmission because of their dynamic and unpredictable nature. This research conducts a comprehensive reliability analysis of data transmission in MANETs, emphasizing the influence of dynamic conditions such as node mobility, changing network topologies, and fluctuating channel conditions. Through the utilization of mathematical models and simulations, the study assesses the overall reliability of data transmission, considering scenarios with diverse node densities, mobility patterns, and network sizes. Existing routing protocols, error correction mechanisms, and adaptive transmission strategies are examined for their effectiveness in reducing the impact of dynamic conditions on reliability. The research not only examines the current state of protocols but also explores potential enhancements and introduces novel approaches to enhance data transmission reliability under dynamic conditions. The findings offer a nuanced understanding of the challenges and opportunities related to reliable communication in MANETs. This research provides crucial insights for designing resilient communication systems in dynamic and mobile network scenarios, offering valuable guidance to researchers, network designers, and practitioners involved in optimizing the performance of MANETs in real-world applications.

LoRaWAN: The dilemma of optimal transmission parameter

Low-Power Wide-Area Network (LPWAN) technologies, particularly LoRaWAN, have gained significant attention for their ability to facilitate long-range communication with low power consumption, making them ideal for Internet of Things (IoT) applications. However, achieving optimal performance in LoRaWAN systems requires careful selection of transmission parameters such as spreading factor, bandwidth, and coding rate. This paper addresses the dilemma faced by network designers and operators in determining the optimal transmission parameters for LoRaWAN deployments. We provide an overview of the key parameters and their impact on network performance, considering factors such as latency, packet delivery rate, and energy efficiency. Furthermore, we discuss the trade-offs involved in parameter selection. Through simulations, we evaluate the performance of different parameter configurations under various network conditions and provide insights into achieving the best balance between coverage, capacity, and energy consumption in LoRaWAN deployments. Findings from the study established that using high coding rate and bandwidth only provides minimal improvement in packet delivery rate particularly at large distances but at the cost energy consumption while spreading factor remains the most important transmission parameter in LoRa networks.

“Clustering Protocol Based on Territorial Energy for Random Deployment of Wireless Sensor Networks"

Wireless Sensor Networks (WSNs) have become increasingly essential for monitoring and gathering data in diverse environments. The efficient utilization of energy resources is crucial for prolonging the network lifetime in WSNs. This paper presents a comparative analysis of three prominent clustering protocols: Low Energy Adaptive Clustering Hierarchy (LEACH), Residual Energy Aware Clustering (REAC), and Territorial Energy-Driven Clustering Protocol (TEDC). TEDC introduces a novel approach where cluster heads are elected based on territorial dominance and energy levels, aiming to enhance energy efficiency and prolong network lifetime. The performance of LEACH, REAC, and TEDC is evaluated under various scenarios, including node density, network size, and communication range. Key performance metrics such as energy consumption, network lifetime, and packet delivery ratio are analyzed to assess the effectiveness of each protocol. The results provide valuable insights into the comparative strengths and weaknesses of these protocols, aiding network designers and researchers in selecting the most suitable protocol for specific WSN deployments. Additionally, the paper discusses potential optimizations and future research directions to further enhance the performance of clustering protocols in WSNs.

Exploring the processes and mechanisms by which nonprofit organizations orchestrate global innovation networks: A case study of the COVAX program

In cocreating value with other organizations, nonprofit organizations may face multiple management challenges, posed by multistakeholder global innovation networks. Since these have not yet been systematically studied by academics, this study explores how nonprofit organizations can promote the cocreation of value in multistakeholder global innovation networks. Adopting a longitudinal single-case study approach from the perspective of network orchestration theory, this work deeply analyzes how nonprofit organizations can promote the evolution of the global innovation network of the COVID-19 vaccine under the COVAX program. The results show that nonprofits need to successively address the dilemmas of legitimacy, direction, and heterogeneity in constructing global innovation networks and that to solve these stage dilemmas, orchestrators must successively function as network architects, liaisons, and leaders to direct the implementation of network actions using trusted, leveraged, and adapted orchestration logics. This paper further proposes a model of the orchestration process and mechanisms by which nonprofit organizations facilitate multistakeholder global innovation networks. Theoretically, this study therefore extends network orchestration theory by summarizing the mechanisms and orchestration logics by which NPOs construct and develop networks when they act as orchestrators. From a practical perspective, this study also provides guidance for future unexpected global public health crises, improving the global community's ability to combat them.

Review of the OSI Model and TCP/IP Protocol Suite on Modern Network Communication

Layered approaches to networking are possible. Network architects categorize protocols in order to simplify their designs. Each layer has its own protocol for talking to the outside world. Every component of the network implements some of the nth layer. Messages are sent back and forth between these parts. Layer n protocol data units [n-PDU] are the official name for these transmissions. All the steps necessary for efficient conversation are covered and these steps are organized into layers for easy comprehension. Layered architecture describes this kind of planning for a communications system. The OSI model is a framework for developing and implementing network-based software applications. It also serves as a basis for the development of new networking protocol hardware, and architectures. This document compares and contrasts the OSI Reference Model seven layers with those of the TCP/IP Model four. There are distinct roles at each successive level. All Internet communication duties may be traced back to the TCP/IP reference model.

Performance analysis of IoT networks in terrestrial environment utilizing LZW data compression technique

The proliferation of Internet of Things (IoT) devices in terrestrial environments necessitates efficient data transmission and management protocols to optimize network performance. This research paper presents a comprehensive study on the implementation of IoT networks in a terrestrial setting, focusing on the integration of LZW (Lempel-Ziv-Welch) compression with three prominent IoT protocols: CoAP (Constrained Application Protocol), M2M (machine-to-machine), and MQTT (Message Queuing Telemetry Transport). We did a thorough test of these protocols using the NetSim simulator. We looked at how well they worked in terms of throughput, en-to-end delay, and routing overhead, all of which are important for loT network efficiency. The point of our study is to find out what the pros and cons of these protocols are when they are combined with LZW compression in a terrestrial loT setting. The results of our analysis reveal significant variations in the performance of CoAP, M2M, and MQTT when subjected to data compression. Throughput efficiency, which directly impacts data transmission rates, is scrutinized alongside end-to-end delay, a critical factor in ensuring timely data delivery. Additionally, we explore the implications of protocol choice on routing overhead, a crucial metric for network resource utilization. This research contributes to the ongoing efforts to optimize IoT networks in terrestrial settings, offering valuable guidance to network architects and developers seeking to strike the right balance between protocol selection and data compression for improved IoT performance.

Dynamic Data Enhancing Battery Efficiency Through Collection Scheduling in IQRF Wireless Sensor Networks

Abstract In this study, we explore innovative strategies for enhancing energy efficiency in Wireless Sensor Networks (WSNs), with a focus on the IQRF network. Our approach integrates dynamic sleep scheduling and data collection methods to optimize battery usage and extend the network’s operational lifespan. We introduce a battery life estimation model, taking into account various factors such as data collection frequency and network size. This model is instrumental in predicting battery longevity under different operational scenarios. Additionally, we develop a practical tool in the form of an API and an online calculator, aimed at assisting network designers in planning and maintaining energy-efficient WSNs. Our results, derived from a case study involving a CO2 sensor network, demonstrate the effectiveness of our methodologies in real-world applications. The study concludes that implementing dynamic data collection and sleep scheduling significantly enhances battery life, offering a valuable contribution to the sustainability and reliability of WSNs.

Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

Artificial Intelligence (AI) techniques are considered the most advanced approaches for diagnosing faults in power transformers. Dissolved Gas Analysis (DGA) is the conventional approach widely adopted for diagnosing incipient faults in power transformers. The IEC-599 standard Ratio Method is an accurate method that evaluates the DGA. All the classical approaches have limitations because they cannot diagnose all faults accurately. Precisely diagnosing defects in power transformers is a significant challenge due to their extensive quantity and dispersed placement within the power network. To deal with this concern and to improve the reliability and precision of fault diagnosis, different Artificial Intelligence techniques are presented. In this manuscript, an artificial neural network (ANN) is implemented to enhance the accuracy of the Rogers Ratio Method. On the other hand, it should be noted that the complexity of an ANN demands a large amount of storage and computing power. In order to address this issue, an optimization technique is implemented with the objective of maximizing the accuracy and minimizing the architectural complexity of an ANN. All the procedures are simulated using the MATLAB R2023a software. Firstly, the authors choose the most effective classification model by automatically training five classifiers in the Classification Learner app (CLA). After selecting the artificial neural network (ANN) as the sufficient classification model, we trained 30 ANNs with different parameters and determined the 5 models with the best accuracy. We then tested these five ANNs using the Experiment Manager app and ultimately selected the ANN with the best performance. The network structure is determined to consist of three layers, taking into consideration both diagnostic accuracy and computing efficiency. Ultimately, a (100-50-5) layered ANN was selected to optimize its hyperparameters. As a result, following the implementation of the optimization techniques, the suggested ANN exhibited a high level of accuracy, up to 90.7%. The conclusion of the proposed model indicates that the optimization of hyperparameters and the increase in the number of data samples enhance the accuracy while minimizing the complexity of the ANN. The optimized ANN is simulated and tested in MATLAB R2023a—Deep Network Designer, resulting in an accuracy of almost 90%. Moreover, compared to the Rogers Ratio Method, which exhibits an accuracy rate of just 63.3%, this approach successfully addresses the constraints associated with the conventional Rogers Ratio Method. So, the ANN has evolved a supremacy diagnostic method in the realm of power transformer fault diagnosis.

Optimization of deep neural networks for multiclassification of dental X-rays using transfer learning

ABSTRACT In this work, the segmented dental X-ray images obtained by dentists have been classified into ideal/minimally compromised edentulous area (no clinical treatment needed immediately), partially/moderately compromised edentulous area (require bridges or cast partial denture) and substantially compromised edentulous area (require complete denture prosthesis). A total of 116 image dental X-ray dataset is used, of which 70% of the image dataset is used for training the convolutional neural network (CNN) while 30% is used sfor testing and validation. Three pretrained deep neural networks (DNNs; SqueezeNet, ResNet-50 and EfficientNet-b0) have been implemented using Deep Network Designer module of Matlab 2022. Each of these CNNs were trained, tested and optimised for the best possible accuracy and validation of dental images, which require an appropriate clinical treatment. The highest classification accuracy of 98% was obtained for EfficientNet-b0. This novel research enables the implementation of DNN parameters for automated identification and labelling of edentulous area, which would require clinical treatment. Also, the performance metrics, accuracy, recall, precision and F1 score have been calculated for the best DNN using confusion matrix.

Нейрорегулятор комплексной технологической системы переработки рудных отходов

The study is devoted to improving the management system of a complex technological system for processing ore waste. Such waste accumulates in large volumes in the territories adjacent to the mining and processing plants, posing a great environmental threat to both the population and the environment due to dust formation and the penetration of harmful compounds into the soil and groundwater. Therefore, the task of improving the management systems for the processing of ore waste, as one of the priorities, is on the current agenda of the management of mining and processing plants. The complexity of the technological system is manifested in the presence of two processing lines that differ in the set of units, and the choice of line depends on the granulometric composition of ore waste. The scientific novelty of the research results is the proposed structure of the neural network controller based on the reference model for the technological system, which is used as deep recurrent neural networks. The general structure of the neuroregulator includes several local neurocontrollers for each of the units of the technological system. Recurrent neural networks make it possible to create high-precision digital copies of individual units of two processing lines and use them to simulate the response of control objects when setting up controllers. Approbation of the proposed structure of the neuroregulator was carried out in the MatLab-Simulik environment, neural networks were designed using the Deep Network Designer tool. The results of testing showed that the speed of the control system is increased compared to other architectures of neuroregulators available in the Simulik environment, which can positively affect the operation of the entire technological system in transient conditions, in particular, reduce technological losses.

Performance Analysis and Optimization of Worst Case User in CoMP Ultra Dense Networks

In the cellular system, the Worst Case User (WCU), whose distances to three nearest BSs are the similar, usually achieves the lowest performance. Improving user performance, especially the WCU, is a big problem for both network designers and operators. This paper works on the WCU in terms of coverage probability analysis by the stochastic geometry tool and data rate optimization with the transmission power constraint by the reinforcement learning technique under the Stretched Pathloss Model (SPLM). In analysis, only fast fading from the WCU to the serving Base Stations (BSs) is taken into the analysis to derive the lower bound coverage probability. Furthermore, the paper assumes that the Coordinated Multi-Point (CoMP) technique is only employed for the WCU to enhance its downlink signal and avoid the explosion of Intercell Interference (ICI). Through the analysis and simulation, the paper states that to improve the WCU performance under bad wireless environments, an increase in transmission power can be a possible solution. However, in good environments, the deployment of advanced techniques such as Joint Transmission (JT), Joint Scheduling (JS), and reinforcement learning is an suitable solution.

A reinforcement neural architecture search convolutional neural network for rolling bearing fault diagnosis

The complexity of machinery makes accurate identification of rolling bearing fault signals difficult. Convolutional neural networks (CNNs) have made some progress, but they rely on the expertise of the network designer and the iterative process of optimizing numerous parameters. Therefore, there is an urgent need to develop a method that reduces the threshold for designing CNNs for a given task. In this article, we propose a reinforcement neural architecture search CNN to address this problem. Firstly, we design a neural architecture search algorithm that can generate different types of sub-networks specifically for fault diagnosis tasks. Secondly, we execute a reinforcement learning-based search strategy to discover promising sub-networks. Furthermore, we enhance the performance of the sub-network by improving the optimizer and training parameters. We conduct extensive experiments using two different types of datasets and verify that the proposed method’s fault classification capability is superior to existing methods.

Nearby connections strategies: Features, usage, and empirical performance evaluation

The Device-to-Device (D2D) communications involve a direct, peer-to-peer link between devices that operates independently of fixed network infrastructures. It can either complement existing network infrastructures or be used as a standalone network to provide services, such as distributing content, supporting emergency services during natural disasters, and enabling smartphone applications like AirDrop (iOS) and Nearby Share (Android). However, the behavior of D2D communication is not always predictable, and there are many challenges to implementing and deploying D2D communication systems in real-world situations. In particular, it is hard to know what throughput one can obtain, as nominal numbers provided by the documentation are seldom observed in practice. This paper focuses on Nearby Connections Application Programming Interface (API) provided by Google. This API offers distinct strategies in function of the network’s topology, and choosing the right one for an application is a challenging task as they perform differently. In this paper, we contribute to the research community in two ways. Firstly, we investigate the real-time performance of throughput of two strategies (STAR and POINT-TO-POINT, as they are the only ones to use Bluetooth and Wi-Fi Direct to transfer data). Our study shows that the POINT-TO-POINT strategy generally achieves high throughput performance and is suitable for use cases involving high network traffic. In contrast, the STAR performs poorly in throughput, which can result in link instability and slow transfers in some cases. Secondly, we disclose a tool to help network designers evaluate their networks and fine-tune their protocols and algorithms according to their specificities.