Today's Networks Research Articles

NetGSR: Towards Efficient and Reliable Network Monitoring with Generative Super Resolution

Network monitoring systems are a key building block in today's networks. They all follow a common framework where measurement data from network elements is aggregated at a central collector for network-wide visibility. When designing network monitoring systems, two key properties have to be taken into account: (1) efficiency, to minimize the communication overhead from network elements to the collector; (2) high-fidelity, to faithfully represent the network status. However, in presence of network dynamics, tracking the right operating point to ensure both high fidelity and efficiency is hard and we observe that prior monitoring approaches trade off one for the other. In this paper, we show that it is possible to satisfy both these properties with NetGSR, a new deep learning based solution we introduce that reconstructs the fine-grained behavior of network status at the collector while requiring low resolution measurement data from network elements. This is achieved through a combination of a new custom-tailored conditional deep generative model (DistilGAN), and a new feedback mechanism (Xaminer) based on model uncertainty estimation and denoising that allows the collector to adjust the sampling rate for measurement data from network elements, at run-time. We extensively evaluate NetGSR using three different network scenarios with corresponding real-world network monitoring datasets as well as two downstream use cases. We show that NetGSR can faithfully reconstruct fine-grained network status with 25x greater measurement efficiency than prior approaches while requiring only few ms of inference time at the collector.

Optimal sizing and placement of STATCOM, TCSC and UPFC using a novel hybrid genetic algorithm-improved particle swarm optimization

The increase in global power demand has caused most of today's power networks to become overloaded especially in Sub-Saharan Africa. The increased load demand can be met through expansion of existing generation and transmission system. However, construction of new power infrastructure is limited by financing and technical constraints. Thus, power networks have been left to operate at overload conditions with high power losses and many power quality (PQ) problems. Flexible AC Transmission System (FACTS) devices can improve the power transfer capability of the existing transmission networks without the need of constructing new power infrastructure. In this paper, a multi-objective function comprising of minimization of power loss (PL), voltage deviation (VD) and operational cost (OC) was formulated and solved using a novel algorithm. A novel Genetic Algorithm-Improved Particle Swarm Optimization (GA-IPSO) technique is proposed in this paper for optimization of size and location of FACTS devices. Static Synchronous Compensator (STATCOM), Thyristor Controlled Series Capacitor (TCSC) and Unified Power Flow Controller (UPFC) are the three FACTS devices considered. The proposed technique was validated using IEEE-33 Bus Test System, which is a popular benchmark Radial Distribution System (RDS). The three FACTS devices were optimized separately and also in a combined manner. Under the separate optimization, the size and location of individual FACTS devices were optimized. For combined optimization, the sizes and locations of more than one device were optimized in the same test system. For separate optimization, UPFC produced the best results by reducing the active power losses by 38.44 % and OC from $1.59×105 to $ 1.15×105. Under the combined optimization, combination of TCSC, STATCOM and UPFC gave better results by achieving active power loss reduction of 56.09 % and reducing OC from $1.59×105 to $ 1.03×105. Comparison of GA-IPSO technique with other algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Improved Grey Wolf Optimization (IGWO) and Differential Evolution Algorithm (DEA) showed that the proposed hybrid technique was superior and more efficient in solving the FACTS optimization problem.

Managing financial investments in supply chain networks: The roles of network power and control

The financialization of firms refers to the phenomenon of non-financial firms increasingly engaging in financial activities rather than traditional production and operational investments. In today's competitive and complex supply chain networks (SCNs), firms do not operate in isolation but heavily rely on their partners. This interdependence may shape their investment behaviours. However, the mechanism of how the network structure affects the financialization of firms in SCNs is yet unclear. Therefore, this study draws on the resource dependence theory to investigate the role of network structure—encompassing network power and control—in shaping corporate financialization in SCNs. We construct SCNs for the focal firms in a sample of 2307 Chinese listed enterprises from 2009 to 2021 and find significantly negative relationships between corporate financialization and both network power and control. This may be because network power and control diminish enterprises' precautionary saving and speculative motivations through financing and information channels. Moreover, their effect on corporate financialization was found to be more pronounced for non-state-owned enterprises (non-SOEs) and firms with low market share, high customer concentration, and distant supply chain geographic proximity. Furthermore, our results indicate that enterprises exhibiting greater network power and control in SCNs tend to reduce their financial investments, which benefits their innovation and total factor productivity. By exploring the factors that influence corporate financialization through a network lens, this study offers valuable insight into the issues regarding financialization, thus promoting the sustainable development of firms and the development of countries' real economies.

Innovative Approaches to Beam Forming Antenna Array Systems with Adaptive Partial Update NLMS Algorithms

Improvements in interference cancellation, energy economy, spectrum efficiency, and system security are among the most pressing needs for today's wireless networks. One effective technique for this is the use of a Beamforming Array Antennas (BAA). However, the complexity of the Beamforming (BF) network, the long convergence time, and the large number of adjustable weight coefficients, all work against full band BAA. The key innovation and contribution of this research was to use Partial Update (PU) instead of full band adaptive algorithms, as no previous attempt had been made to do so. A subset of the array's elements, rather than all of them, will be connected to by PU methods. This allows the system to reduce the number of active antennas across all cells while maintaining high efficiency, and low cost. In this research, a new architectural model was proposed that makes use of PU adaptive algorithms, to reduce the required number of phase shifters (PSs), and hence base station antennas. For the most part, we will be discussing PU Normalized Least Mean Square (PU NLMS) algorithms like M-max NLMS, Periodic-NLMS, and Stochastic-NLMS. Using a Uniform Linear Array (ULA) Antennas in a simulation environment, we find that the, in terms of Mean Square Error (MSE), convergence rate, and steady-state error, it is evident that all PU NLMS algorithms (with the exception of Periodic-NLMS) had performed close, and approximately equivalent performance to the full band NLMS algorithms. In other hands, these PU algorithms maintain the radiation pattern with as little change from the original (distortion-free) as possible and symmetrical of the array, while. fewer elements are needed to produce the same amount of radiation. Reducing the number of required coefficients N to M (M = 5; M: Number of coefficients to be update per iteration) compared to the full update method (N = 8), to obtain the Reduction ratio 38%.

CC-OLIA: A Dynamic Congestion Control Algorithm for Multipath QUIC in Mobile Networks

High-quality services in today's mobile networks require stable delivery of bandwidth-intensive network content. Multipath QUIC (MPQUIC), as a multipath protocol that extends QUIC, can utilize multiple paths to support stable and efficient transmission. The standard coupled congestion control algorithm in MPQUIC synchronizes these paths to manage congestion, meeting fairness requirements and improving transmission efficiency. However, current algorithms' Congestion Window (CWND) reduction approach significantly decreases CWND upon packet loss, which lowers effective throughput, regardless of the congestion origin. Furthermore, the uncoupled Slow-Start (SS) in MPQUIC leads to independent exponential CWND growth on each path, potentially causing buffer overflow. To address these issues, we propose the CC-OLIA, which incorporates Packet Loss Classifcation (PLC) and Coupled Slow-Start (CSS). The PLC distinguishes between congestion-induced and random packet losses, adjusting CWND reduction accordingly to maintain throughput. Concurrently, the CSS module coordinates CWND growth during the SS, preventing abrupt increases. Implementation on MININET shows that CC-OLIA not only maintains fair performance but also enhances transmission efficiency across diverse network conditions.

Deep Learning and Recurrent Signature Based Classification for Sensor-Based HAR: Addressing Explainability and Complexity in 5G Networks

When it comes to clinical applications, sensor-based human activity recognition (HAR) is invaluable, and numerous machine learning algorithms have effectively used to obtain excellent presentation. Using a variety of on-body sensors, these systems attempt to ascertain the subject's status relative to their immediate surroundings. There was a time when feature extraction was done by hand, but now more and more people are using Artificial Neural Networks (ANNs). A number of innovative approaches to HAR have surfaced since the advent of deep learning. Problems arise, however, for sensor-based HAR classification algorithms in today's communication networks. Among these, you can find solutions to problems like deal with complicated and large-scale data signals, extract characteristics from complicated datasets, and meet explainability standards. For complicated 5G networks, these difficulties become even more apparent. In particular, explainability is now critical for the broad use of sensor-based HAR in 5G networks and beyond. The research suggests a classification approach based on path signatures, recurrent signature (ReS), to address these issues. This cutting-edge model employs deep-learning (DL) approaches to circumvent the tedious feature selection challenge. Furthermore, the study investigates how to improve the ReS model's classification accuracy by using graph-based optimisation methods. To test how well the suggested framework worked, to dug deep into the publicly available dataset, which included a separate set of tasks. The paper's empirical results on AReM datasets achieved an average accuracy of 96%.

Enhancing Network Security in Mobile Applications with Role-Based Access Control

In today's dynamic networking environment, securing access to resources has become increasingly challenging due to the growth and progress of connected devices. This study explores the integration of Role-Based Access Control (RBAC) and OAuth 2.0 protocols to enhance network access management and security enforcement in an Android mobile application. The study adopts a waterfall methodology to implement access control mechanisms that govern authentication and authorization. OAuth 2.0, a widely adopted open-standard authorization framework, was implemented to secure user authentication by allowing third-party access without exposing user credentials. Meanwhile, RBAC was leveraged to streamline access permissions based on predefined user roles, ensuring that access privileges are granted according to hierarchical role structures. The main outcomes of this study show significance towards the improvements in security enforcement and user access management. Specifically, the implementation of multi-factor authentication, session timeout mechanisms, and user role-based authorization ensured robust protection of sensitive data while maintaining system usability. RBAC proved effective in controlling access to various system resources, such as database operations which was presented in scenario of physical access to doors, while OAuth 2.0 provided a secure communication channel for authentication events. These protocols, working in tandem, addressed key issues like unauthorized access, data integrity, and scalability in network security policy enforcement. This research deduces that combining RBAC and OAuth 2.0 protocols in mobile applications enhances security posture, simplifies access management, and mitigates evolving threats.

Enhancing Network Fault Detection with Precision Predictive AI

Traditional methods for managing and predicting faults must be revised in today's complex network landscape. Predictive Artificial Intelligence (AI) offers a proactive solution, using advanced algorithms and machine learning to analyze vast data, detect patterns, and prevent issues before they escalate. This approach significantly enhances network reliability, reduces downtime, improves operational efficiency, and has transformative potential in network management. This white paper explores this potential, providing real-world examples and integration strategies. We also discuss its benefits and challenges, highlighting its promise for ensuring stable and resilient network operations.

Performance Analysis of Cooperative Spectrum Sensing using Empirical Mode Decomposition and Artificial Neural Network in Wireless Regional Area Network

Background: Radio spectrum is natural and the most precious means in wireless communication systems. Optimal spectrum utilization is a key concern for today's cutting-edge wireless communication networks. The impending problem of the lack of available spectrum has prompted the development of a new idea called “Cognitive Radio” (CR). Cooperative spectrum sensing (CSS) is utilized to improve the detection performance of the system. Several fusion algorithms of decision-making are proposed for sensing the licensed user, but they do not work well under low signal-to-noise ratio (SNR). Objectives: To address the issue of poor detection performance under low SNR, Empirical mode decomposition (EMD) and artificial neural network (ANN) based CSS under Rayleigh multipath fading channel in IEEE 802.22 wireless regional area network (WRAN) is proposed in this paper. Method: In this work, we propose the use of ANN as a fusion center. First, the received signal's energy is calculated using EMD. The computed energy, SNR, and false alarm probability are combined to form a data set of 2048 samples. They are utilized to train Levenberg- Marquardt back propagation training algorithm-based feed-forward neural network (FFNN). Using this trained network, CSS in WRAN is simulated under Rayleigh multipath fading. Results: Simulation results show that the proposed CSS method based on EMD-ANN outperforms the standard fast Fourier transform (FFT) and EMD detection-based cooperative spectrum sensing with a hard "OR" fusion at low SNR. With Pf =0.01, 100% detection accuracy with proposed techniques is obtained at SNR= -22dB. Conclusion: The findings show that the suggested approach outperforms EMD and FFT based energy detection scheme-based traditional CSS in low SNR environments.

Honey Badger Algorithm Based DVR Controllers for Improved Power Quality‏ in a Microgrid Combined of (Wind System\ Grid\Nonlinear Load)

Power quality (PQ) is crucial in today's energy supply networks, where even little voltage fluctuations can have a big impact on how well household appliances and technologies operate. The suggested dynamic voltage regulator (DVR) approach helps to create a new generation power grid that is more dependable and effective. In this study, the honey badger optimizer (HBO) is used to optimize the controllers of the DVR for improving PQ via voltage control. The efficacy of the optimized DVR is further increased by its integration with a microgrid (MG) wind supply. The suggested technique makes use of a low-complexity control approach for voltage regulation to adjust for harmonic distortion, swells, and voltage dips in the addressed system. The technique accomplishes voltage improvement, bus stabilization, energy-efficient utilization, and harmonic distortion reduction by using the DVR in conjunction with an MG wind supply. Various voltage disturbances, such as balanced and unbalanced swell and sag, voltage imbalance, notching, various fault states, and power system harmonic distortion, are taken into consideration to show the approach's usefulness. The findings indicate PQ enhancement, demonstrating that the load voltage roughly matches the nominal value.

A novel multi-modal incremental tensor decomposition for anomaly detection in large-scale networks

Network traffic anomaly detection is a crucial task for today's network monitoring and maintenance. However, with the rapid growth of network data volume, the data structure has become more and more complex, showing multi-modal characteristics, which makes traffic anomaly detection face a great challenge. The earlier proposed anomaly detection methods have the following deficiencies, i) Most of them are static or dynamic detection methods that only grow along the temporal modality. ii) Lower detection rate or higher computational cost. To address these deficiencies, this article proposes a traffic anomaly detection framework based on multi-modal incremental tensor decomposition, which has the following three highlights, i) Constructing traffic data as a tensor model to fully mine the correlation between data, and the proposed framework is applicable to the situation where traffic data grows dynamically along multiple modes. ii) Using the multi-modal incremental tensor decomposition method to process dynamically growing data without decomposing all the data, greatly reducing computational cost and improving data quality. iii) Using the XGBoost classification algorithm for anomaly detection to improve detection accuracy. Finally, the results of experiments on two real network traffic datasets NSL-KDD and CICDDOS 2019 show that the proposed framework can achieve a high detection rate of 99.21%, and has the characteristics of good scalability and fast detection speed.

VIRTUAL PRIVATE NETWORKS (VPN): A CONCEPTUAL REVIEW OF SECURITY PROTOCOLS AND THEIR APPLICATION IN MODERN NETWORKS

Virtual Private Networks (VPNs) play a crucial role in ensuring secure communication over public networks. This concept paper provides a comprehensive review of security protocols used in VPNs and their application in modern networks. The paper begins by defining VPNs and explaining their importance in securing data transmission over untrusted networks. It then discusses the evolution of VPN technology, highlighting the transition from traditional IPSec and SSL/TLS protocols to more modern approaches such as WireGuard and QUIC. The paper also examines the security protocols used in VPNs, including IPSec, SSL/TLS, and the newer protocols like WireGuard and QUIC. Each protocol is analyzed in terms of its security features, performance, and suitability for different use cases. Furthermore, the paper explores the application of VPNs in modern networks, including their use in remote access, site-to-site connectivity, and securing cloud-based services. It also discusses the challenges and considerations involved in deploying and managing VPNs in today's complex network environments. In conclusion, the paper emphasizes the importance of VPNs in ensuring secure communication in modern networks. It highlights the need for organizations to carefully evaluate their VPN requirements and choose the appropriate security protocols and technologies to protect their data and networks. Keywords: VPN, Security, Applications, Network.

Self-stabilizing synchronous unison in directed networks

Self-stabilization is a general paradigm that characterizes the ability of a distributed system to recover from transient faults. Since its introduction by Dijkstra in 1974, self-stabilization has been successfully applied to efficiently solve many networking tasks. However, most of the literature focuses on bidirectional networks. Now, in today's networks such as WSNs, some communication channels may be one-way only. Considering such network topologies, a.k.a. directed graphs, makes self-stabilization more complicated, and sometimes even impossible. In this paper, we investigate the gap in terms of requirements and efficiency when considering a directed graph instead of an undirected one as network topology for a self-stabilizing algorithm. Our case study is a variant of a synchronous unison algorithm proposed by Arora et al.; the synchronous unison being a clock synchronization problem.

Learning Audio and Video Bitrate Selection Strategies via Explicit Requirements

Mobile video streaming dominates today's network traffic, and adaptive bitrate (ABR) algorithms have been routinely adopted for transmitting media content across dynamic mobile networks. State-of-the-art ABR algorithms mainly alter video bitrate without considering audio bitrate as they consider the impact on the video negligible due to their small size. However, to bring users an immersive experience, recent content providers have applied high-quality audio with large sizes, like stereophonic sound. Therefore, improper audio bitrate selection will adversely affect video bitrate selection, leading to undesirable audio/video combinations (the highest video quality with the lowest audio quality, and vice versa) and frequent playback interruptions. To address these inefficiencies, we propose a Self-Play reinforcement learning-based Audio-aware ABR algorithm named SPA to learn strategies for audio and video bitrate selections. By learning from explicit goals, SPA can match the actual requirements and attain good performance. By conducting trace-driven and testbed-based experiments, we observe SPA's considerable superiority compared to existing approaches, including reducing the undesirable combinations by up to 34.17× and achieving zero stall time across 88.57% of traces. We also invite 35 volunteers to join a subjective test, and the result shows that 33/35 people consider SPA provides them with a satisfactory viewing experience.

Metadisciplinarity at the intersection of modern philosophical and philological practices

The article explores the relationship between inter-, trans-, and polydisciplinarity in contemporary humanities. It highlights philosophy's role in fostering critical thinking and methodological reflection. The phenomenon of intersubjectivity/interculturality is discussed in today's network society, emphasizing the ongoing exchange between individual consciousness and interpersonal perception within metadisciplinary philosophical studies. The article illustrates the practical application of metadisciplinarity through a PhD program at Kyiv National Linguistics University, advocating for the reintroduction of philosophy into modern education. It proposes an interdisciplinary segment, "Philosophy of Science and Methodology of Humanitarian Knowledge," integrating philological and professional teaching with philosophy to cultivate reflective and critical skills. The program aims to equip postgraduate students with knowledge of logical, methodological, and epistemological shifts, applicable in teaching. Ultimately, it asserts that combining specific scientific knowledge with philosophical principles in education shapes a flexible and deep conceptual framework, enhancing modern scientific and philosophical discourse. This multimodal approach to metadisciplinary philosophy fosters innovative paradigmatic trends.

Addressing the challenges of using autonomous robots for last-mile delivery

The importance of last-mile delivery (LMD) in today's logistics networks has increased recently due to the rising popularity of e-commerce and home delivery. Various LMD modes, such as drones and autonomous vehicles, have emerged to solve the problems encountered during LMD activities and to reduce human intervention in the process. The purpose of this paper is to determine the challenges that arise when using autonomous delivery robots (ADRs) in LMD. Throughout the research, the challenges have been revealed, categorized into six titles and their significance has been calculated via the intuitionistic fuzzy (IF) pivot pairwise relative criteria importance assessment (PIPRECIA) method in conjunction with PESTEL Analysis. IF-PIPRECIA is employed for the first time in the literature with PESTEL Analysis to evaluate the challenges for the implementation of ADRs in LMD. According to the proposed methodology results, challenges related to “Energy consumption,” “Battery life and energy management,” “Cybersecurity,” “Air pollution,” and “Uncertain return on investment” are the most critical factors to consider when implementing ADRs in LMD.

Group Key Management Protocols for Non-Network: A Survey

The phenomenal rise of the Internet in recent years, as well as the expansion of capacity in today's networks, have provided both inspiration and incentive for the development of new services that combine phone, video, and text "over IP." Although unicast communications have been prevalent in the past, there is an increasing demand for multicast communications from both Internet Service Providers (ISPs) and content or media providers and distributors. Indeed, multicasting is increasingly being used as a green verbal exchange mechanism for institution-oriented programmers on the Internet, such as video conferencing, interactive college games, video on demand (VoD), TV over the Internet, e-learning, software programme updates, database replication, and broadcasting inventory charges. However, the lack of security within the multicast verbal exchange model prevents the effective and large-scale adoption of such important company multi-celebration activities. This situation prompted a slew of research projects that addressed a variety of issues related to multicast security, including confidentiality, authentication, watermarking, and access control. These issues should be viewed within the context of the safety regulations that work in the specific conditions. For example, in a public inventory charge broadcast, while identification is a vital necessity, secrecy is not. In contrast, video-convention programme requires both identification and confidentiality. This study gives a complete examination and comparison of the issues of group key management. Both network-dependent and network-independent approaches are used. The study also addresses the advantages, disadvantages, and security problems of various protocols.

Design and analysis of optimal AGC regulator for multi‐area power systems with TCPS and energy storage unit in deregulated environment

AbstractIn today's electric power network, fast changes are the norm, and auxiliary services such as automatic generation control (AGC) play a crucial role in maintaining the quality of the power supply. AGC ensures a balance between power generation, demand, and losses to sustain frequency stability and variation in tie‐line power within a set limit, even when the load changes. To accomplish this, it is always beneficial to consider new approaches to controlling the situation. This paper introduces the design of new AGC regulators that consider deviation in DC tie‐line power as an extra control variable for the turbine controller. In this study, three different deregulated power systems (containing two control areas), namely three thermal generations, the combination of one thermal and two hydro generations, and three hydro generations, including two power distribution companies (DISCOs) in each control area. These optimal AGC regulators are implemented in the proposed thermal–thermal–thermal system to carry out the various power contracts. The results have shown that the dynamic outcomes meet the AGC standards. To improve further dynamic results and the proposed systems' stability margins by incorporating redox flow batteries (RFB). In actual operating conditions, the system parameters do not remain constant due to the aging effect, assumptions made in simplifying the mathematical model, etc. Thus, ±50% deviations in the nominal value of system parameters to assess how well the optimal AGC regulators perform in the system under investigation. The suggested realistic AGC system incorporates many parameter fluctuations and works well with the optimal AGC regulators developed for the proposed plans. This study expanded to include a two‐area, thermal‐hydro‐hydro (THH) system under a deregulated framework connected via an asynchronous transmission link with or without a thyristor‐controlled phase shifter (TCPS) and RFB. The genetic algorithm (GA) can solve many issues and has global search, flexibility to varied problem types, intrinsic parallelism, and the ability to handle massive, complex search spaces. Thus, a two‐area deregulated hydro‐hydro‐hydro system with parallel tie‐lines utilizes GA‐PID, GA‐FOPID, and GA‐(1 + PI)‐FOPID controllers. Moreover, a random load disturbance (RLD) is employed in each section of the offered approach to show the resilience and elite performance of the proposed control strategy. To produce various dynamic reactions in the plans, MATLAB software version R2013a is employed.

Simulation and Implementation of Intelligent Network Technology System Based on Big Data

In today's society, big data and intelligent networks are important development trends in science and technology. With the rapid development of big data, higher requirements have been put forward for data processing and analysis, especially in the design and implementation of network technology systems. In response to the above issues, this article intends to simulate and implement the intelligent networking technology system in the big data environment. Based on big data, the article can identify and analyze the laws and trends contained in it by collecting and processing a large amount of Internet data and using data mining methods. This article can adopt a series of machine learning methods and apply them to network traffic prediction and network security improvement. This method can monitor the real-time operation status of the network and predict possible network congestion and attack behaviors, in order to achieve rapid and automated management of the network. The system throughput fluctuates with slight changes in response time, but typically remains between 495 and 530 requests per second. The experiment shows that the method used in this article can effectively improve data processing speed, error detection rate, and system stability.

Dynamic QoS: Automatically Modifying QoS Queue's Maximum Bandwidth Rate-Limit of Network Devices for Network Improvement

The heterogeneous data traffic of today's network is a huge challenge to existing best-effort network technology, particularly in the context of large Ethernet, which handles hundreds to thousands of users. The existing conventional best-effort network technology is no longer efficient to handle the diversity of traffic types in the network and requires network management equipment such as Quality of Service (QOS). Usually, QOS is implemented on the gateway router. However, for better network performance and management, to guarantee high priority for sensitive traffic like video conferencing, Voice over Internet Protocol (VoIP), and streaming media within an internal network, it is nice to have QoS implemented on each router in the LAN network, starting from the access router to the gateway router. This paper is to demonstrate the effectiveness of the proposed dynamic QoS that has been developed and deployed in the LAN, purposely to provide adequate bandwidth for sensitive traffic when the network utilization is high and congested, by automatically modifying the QoS Queue's Maximum Bandwidth Rate-Limit of the best-effort traffic queue of the related router. The performance of the proposed developed dynamic QoS was evaluated via a comparison study before and after the dynamic QoS was presented in the network simulation environment that was built using Mininet. Results from the testing show that the developed dynamic QoS can improve the network's performance by automatically giving the appropriate bandwidth for sensitive traffic on the fly while needed/on demand.