Performance Analysis Of Networks Research Articles

A DASH server-side delay-based representation switching solution to improve the quality of experience for low-latency live video streaming

This work addresses the integration of real-time transmission systems, including IP cameras and production systems (like OBS or vMix), that use protocols such as RTSP (Real Time Streaming Protocol) or SRT (Secure Reliable Transport), with content distribution technology based on LL-DASH (Low Latency DASH -Dynamic Adaptive Streaming over HTTP-), taking advantage of the fact that DASH offers significant well-known advantages for content distribution over the Internet and via CDNs (Content Delivery Networks). Considering the limitations of the LL-DASH standard regarding the adaptation to network conditions, this paper proposes a new solution called Server-Side Representation Switching (SSRS). SSRS uses an approach based on the server measuring the delay in the requests made by clients, whose variation may be due to a decrease in bandwidth, as occurs in Wi-Fi networks with a high number of clients. To evaluate the effectiveness of the proposed solution, a testbed has been developed that allows the performance evaluation of both the LL-DASH system and the solution based on server-side decision-making. In addition, the developed solution has been compared with known algorithms (L2A and LoL+) integrated into the Dash.js player. The results show that the Server-Side Representation Switching solution offers a good trade-off between the transmitted quality and the final delay measured at the client, compared to the other algorithms evaluated. Moreover, it holds the advantage of being straightforward to implement and does not require any modifications to the players used.• Networks • Network performance evaluation • Network performance analysis.

Performance Analysis of Large-scale Networks in Ns-2

Wired communication refers to data transmission over a wire-based communication technology. Also, in computer engineering terms, a network is defined as a group of computers connected to share information or services (print services, multitasking, etc.). In this paper, the performance of a wired network that consists of more than 300 nodes which implement as an example of a big organization (i.e., Sakarya University), is investigated and analyzed by simulations using a network simulator tool (Ns2). The analysis depends on many performance metrics such as throughput, delay, Jitter, etc. Results show that modeled network has enough performance in different scenarios.

Performance analysis of overlay-based cognitive radio networks with MRC/SC and DT/AF/DF

Performance analysis of overlay-based cognitive radio networks with MRC/SC and DT/AF/DF

Secure transmission performance analysis of multi-hop cognitive relay radio networks with energy harvesting and artificial noise aided

Secure transmission performance analysis of multi-hop cognitive relay radio networks with energy harvesting and artificial noise aided

Performance Analysis of NOMA-Based Hybrid Satellite-Terrestrial Relay Networks With CCI

Hybrid satellite-terrestrial relay networks (HSTRNs) which exploit relay cooperative technology in the integrated system of satellites and terrestrial base stations (BSs) are expected to offer enhanced transmission performance benefited from the spatial gain of independent fading paths. To enable multi-user accessing and resource sharing among users, non-orthogonal multiple access (NOMA) technique can be applied in HSTRNs. In this paper, we consider a NOMA-based HSTRN where a satellite transmits signals to a relay base station (RBS) and a destination ground station (DGS), and RBS also serves a cellular user (CU) and DGS simultaneously by leveraging NOMA scheme. We assume that the satellite shares spectrum with a number of cellular small BSs (SBSs), hence SBSs will cause co-channel interference (CCI) to DGS, RBS and CU. We examine the transmission characteristics of the satellite and terrestrial links, and derive the closed-form expressions of the outage probability (OP) and average symbol error rate (ASER) of DGS and CU, respectively. Specially, we consider the effect of perfect and imperfect successive interference cancellation (SIC) on the ASER performance of CU. Then, considering the stochastic characteristics of satellite links and CCI, we analyze the mean energy efficiency (MEE) of the satellite and propose an optimal power allocation scheme which maximizes the MEE of the satellite and achieves system performance optimization. Simulation results demonstrate the effectiveness of the theoretical analysis and the proposed power allocation scheme.

Grid parameters analysis of 11 kV radial feeder on residential areas based on forecasted emerging load: A case study of Tarkeshwor Municipality, Nepal

The study focuses on the technical performance analysis of a radial distribution network in a rapidly urbanising area under the current, future, and optimised scenarios. The control area in this research is Tarkeshwor Municipality, Nepal, which lies in the outer stretch of Kathmandu, representing the urbanising loads. In this paper, the electric cooking load has been added to the long term forecasted load up to ten years from now, representing emerging technology and load that adds to the peak load in the daily load curve of the control area. The placement of reactive power suppliers (i.e. Capacitor placement) in optimal network buses is also proposed as an optimisation tool that exhibits improved power quality and reliability with the forecasted load. The peak load is found to be increased to 21.93 MVA at the tenth year's end including targeted emerging load due to which the voltage profile also goes out of the limits. Optimal placement of capacitor on optimal buses results in improved voltage profile and also reduces active power loss of the distribution network in the control area. The analysis and discussion in this research will be helpful to the policymakers as well as the electric utility for planning the supply and utilisation pattern of electricity in the future.

Efficient Generators of the Generalized Fractional Gaussian Noise and Cauchy Processes

In the last years of the past century, complex correlation structures were empirically observed, both in aggregated and individual traffic traces, including long-range dependence, large-timescale self-similarity and multi-fractality. The use of stochastic processes consistent with these properties has opened new research fields in network performance analysis and in simulation studies, where the efficient synthetic generation of samples is one of the main topics. Nowadays, networks have to support data services for traffic sources that are poorly understood or still insufficiently observed, for which simple, reproducible, and good traffic models are yet to be identified, and it is reasonable to expect that previous generators could be useful. For this reason, as a continuation of our previous work, in this paper, we describe efficient and online generators of the correlation structures of the generalized fractional noise process (gfGn) and the generalized Cauchy (gC) process, proposed recently. Moreover, we explain how we can use the Whittle estimator in order to choose the parameters of each process that give rise to a better adjustment of the empirical traces.

Performance Analysis of IRS-Assisted NOMA Networks With Randomly Deployed Users

In this article, we investigate the intelligent reflecting surface (IRS)-assisted downlink nonorthogonal multiple access (NOMA) networks, where the users are randomly deployed in a disk. The randomly deployed users are divided into two groups, named center group and edge group, and then two distance-dependent user selection schemes are proposed. The developed closed-form expressions of outage probability and average rate for the proposed two user selection schemes in IRS-assisted NOMA networks are derived, as well as the asymptotic results at high signal-to-noise ratio (SNR) and the number of the reflecting number of the IRS trends to infinity, respectively. The asymptotic analysis results of outage probability for cell-edge user decay as <inline-formula><tex-math notation="LaTeX">$\frac{\ln \mathrm{SNR}}{\mathrm{SNR} ^{K+1}}$</tex-math></inline-formula>, and the achieved diversity gain is <inline-formula><tex-math notation="LaTeX">$K+1$</tex-math></inline-formula>, at high SNR region, where <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> denotes the number of elements of the IRS. Furthermore, as <inline-formula><tex-math notation="LaTeX">$K \rightarrow \infty$</tex-math></inline-formula>, the asymptotic average rate of cell-edge user only depends on the power allocation factor of cell-center user. In addition, compared to IRS-assisted orthogonal multiple access networks, the IRS-assisted NOMA networks can always realize lower outage performance and higher average rate. Finally, Monte&#x2013;Carlo simulation results are provided to verify the accuracy of the developed analytical results for the proposed two user selection schemes.

Performance Analysis and Deep Learning Assessment of Full-Duplex Overlay Cognitive Radio NOMA Networks Under Non-Ideal System Imperfections

In this paper, we investigate the effectiveness of an overlay cognitive radio (OCR) coupled with non-orthogonal multiple access (NOMA) system using a full-duplex (FD) cooperative spectrum access with a maximal ratio combining (MRC) scheme under the various non-ideal system imperfections. In view of practical realization, we ponder the impact of loop self-interference, transceiver hardware impairments, imperfect successive interference cancellation, and channel estimation errors on the system performance. We investigate the performance of the proposed system by obtaining closed-form expressions for outage probability and ergodic rate for primary as well as secondary users using Nakagami-m fading channels. As a result, we reveal some notable ceiling effects and present efficacious power allocation strategy for cooperative spectrum access. We further evaluate the system throughput and ergodic sum-rate (ESR) to assess the system’s overall performance. Our findings manifest that the FD-based OCR-NOMA can comply with the non-ideal system imperfections and outperform the competing half-duplex (HD) and orthogonal multiple access (OMA) counterparts. Due to the massive complexity of the suggested system model, direct derivation of the closed-form formula for the ESR becomes cumbersome. To address this problem, we develop a deep neural network (DNN) framework for ESR prediction in real-time situations.

MDC Enhanced IoT Networks: Network Modeling and Performance Analysis

As a promising architecture, Mobile Data Collector (MDC) enhanced Internet of Things (IoT) exhibits broad prospects in efficient data collection and data aggregation especially for sparse deployment scenarios. Combining the tools from queueing theory and stochastic geometry, we propose an analytical framework to study the network performance of an MDC enhanced IoT network, in terms of coverage probability, end-to-end delay and energy consumption. We derive the closed-form expressions for average contact and inter-contact time between a sensor and its associated MDC. By modeling the data collection system between a sensor and its associated MDCs as an M/G/1 queue system with vacations and general limited (G-limited) service, we first derive the queueing delay at the tagged sensor, and further obtain the end-to-end delay. The proposed analytical framework enables us to quantify the effect on network performance of key system parameters, such as MDC velocity, packet arrival rate, densities of sensors and MDCs, and contact radius. This study reveals that the MDC velocity has little impact on the coverage probability, and provides guidelines to minimize the end-to-end delay by optimizing the density and contact radius of sensors, and the velocity and density of MDCs.

Network consensus analysis and optimization of distributed FANETs based on multi-agent consensus theory

Distributed flying ad hoc networks (FANETs) have been widely used in collaborative reconnaissance, situation construction, and other scenarios. In distributed FANETs with multi-hop and intermittent links, nodes only maintain neighbors’ information and cannot obtain the whole network messages. There may be contradicting information collected across nodes, resulting in inconsistency problems. However, existing research on collaborative consensus focuses mainly on the control domain using multi-agent consensus theory. The study on distributed network consensus does not consider the effect of the multi-hop forwarding order, hence limiting the optimization of distributed FANETs. Based on this, we establish a network consensus model utilizing the multi-agent consensus theory and analyze the impact of the outage probability of links and untimely forwarding on the distributed consensus probability, considering the node density, link outage probability, and network maintenance times. Besides, using the election mechanism as an example, we establish distributed network performance analysis models considering consensus error to enhance the service delay and resource efficiency performance analysis of distributed FANETs. Finally, we construct a protocol-level simulation platform based on Visual Studio and extensive experiments to determine the optimal mechanism parameters under different network and channel parameters. The simulation results show that the optimal network maintenance times increase with the increasing outage probability of links. Moreover, distributed FANETs can achieve optimal resource efficiency without achieving complete consensus, that is, there is a tradeoff between network maintenance cost and network performance.

A resilience-based decision framework for post-earthquake restoration of bridge networks under uncertainty

As key components of the transportation infrastructure system, bridges are vulnerable to earthquake hazards during their service life. Restoration of damaged bridges is of great importance to resume the operation of the transportation networks in support of post-earthquake city reconstruction and rescue. This paper proposes a resilience-based framework to identify the near-optimal restoration scheduling of bridge networks after an earthquake. In the framework, an average travel time-based metric is introduced for the network performance analysis, and a probabilistic recovery model is adopted to describe the restoration characteristics of damaged bridge. Furthermore, the post-earthquake restoration scheduling problem is formulated as a mathematical model with the aim of maximizing network resilience, for which a modified GA-based optimization methodology is developed to obtain the near-optimal solution to this problem. Finally, the applicability of this framework in the scenario of bridge network recovery is demonstrated through two numerical examples. The results show that the proposed framework is effective in guiding the network restoration scheduling under resources and functionality constraints; the dynamic changes in traffic demand after an earthquake can significantly affect the optimal restoration sequence of damaged bridges, the evolution of network functionality and network resilience.

Saliency Map and Deep Learning in Binary Classification of Brain Tumours.

The paper was devoted to the application of saliency analysis methods in the performance analysis of deep neural networks used for the binary classification of brain tumours. We have presented the basic issues related to deep learning techniques. A significant challenge in using deep learning methods is the ability to explain the decision-making process of the network. To ensure accurate results, the deep network being used must undergo extensive training to produce high-quality predictions. There are various network architectures that differ in their properties and number of parameters. Consequently, an intriguing question is how these different networks arrive at similar or distinct decisions based on the same set of prerequisites. Therefore, three widely used deep convolutional networks have been discussed, such as VGG16, ResNet50 and EfficientNetB7, which were used as backbone models. We have customized the output layer of these pre-trained models with a softmax layer. In addition, an additional network has been described that was used to assess the saliency areas obtained. For each of the above networks, many tests have been performed using key metrics, including statistical evaluation of the impact of class activation mapping (CAM) and gradient-weighted class activation mapping (Grad-CAM) on network performance on a publicly available dataset of brain tumour X-ray images.

System Performance Analysis of Sensor Networks for RF Energy Harvesting and Information Transmission

This paper investigates the problem of RF energy harvesting in wireless sensor networks, with the aim of finding a suitable communication protocol by comparing the performance of the system under different protocols. The network is made up of two parts: first, at the beginning of each timeslot, the sensor nodes harvest energy from the base station (BS) and then send packets to the BS using the harvested energy. For the energy-harvesting part of the wireless sensor network, we consider two methods: point-to-point and multi-point-to-point energy harvesting. For each method, we use two independent control protocols, namely head harvesting energy of each timeslot (HHT) and head harvesting energy of dedicated timeslot (HDT). Additionally, for complex channel states, we derive the cumulative distribution function (CDF) of packet transmission time using selective combining (SC) and maximum ratio combining (MRC) techniques. Analytical expressions for system reliability and packet timeout probability are obtained. At the same time, we also utilize the Monte Carlo simulation method to simulate our system and have analyzed both the numerical and simulation solutions. Results show that the performance of the HHT protocol is better than that of the HDT protocol, and the MRC technology outperforms the SC technology for the HHT protocol in terms of the energy-harvesting efficiency coefficient, sensor positions, transmit signal-to-noise ratio (SNR), and length of energy harvesting time.

GRaNIE and GRaNPA: inference and evaluation of enhancer‐mediated gene regulatory networks

Enhancers play a vital role in gene regulation and are critical in mediating the impact of noncoding genetic variants associated with complex traits. Enhancer activity is a cell‐type‐specific process regulated by transcription factors (TFs), epigenetic mechanisms and genetic variants. Despite the strong mechanistic link between TFs and enhancers, we currently lack a framework for jointly analysing them in cell‐type‐specific gene regulatory networks (GRN). Equally important, we lack an unbiased way of assessing the biological significance of inferred GRNs since no complete ground truth exists. To address these gaps, we present GRaNIE (Gene Regulatory Network Inference including Enhancers) and GRaNPA (Gene Regulatory Network Performance Analysis). GRaNIE (https://git.embl.de/grp‐zaugg/GRaNIE) builds enhancer‐mediated GRNs based on covariation of chromatin accessibility and RNA‐seq across samples (e.g. individuals), while GRaNPA (https://git.embl.de/grp‐zaugg/GRaNPA) assesses the performance of GRNs for predicting cell‐type‐specific differential expression. We demonstrate their power by investigating gene regulatory mechanisms underlying the response of macrophages to infection, cancer and common genetic traits including autoimmune diseases. Finally, our methods identify the TF PURA as a putative regulator of pro‐inflammatory macrophage polarisation.

Performance analysis of dense low earth orbit satellite communication networks with stochastic geometry

The low Earth orbit (LEO) satellite communication systems have drawn much attention as a promising solution for providing global wireless connectivity. This paper studies the downlink performances of LEO satellite communication systems performing a directional beamforming with a stochastic geometry. As the beamwidth of the satellite increases, the beam coverage increases but the interference from other satellites also increases. This is vice versa for the smaller beamwidth. Accordingly, the optimal beamwidth control is necessary to balance the beam coverage and the network interference. To address such issue, we first derive the conditional distance distribution to serving line-of-sight (LoS) and non-line-of-sight (NLoS) satellites, conditioned on that there exists at least one satellite in the satellite-visible region and the user is located within the beam coverage of the satellite. Then, we derive the exact and approximated formulas for the coverage probability and the ergodic rate as a function of the beamdwidth. With some numerical examples, we investigate how various system parameters such as altitude and satellite density affect on the optimal beamwidth and demonstrate that the optimal control of the beamwidth of the satellites can maximize the performances by efficiently controlling the amount of interference and beam coverage.

COMPARATIVE PERFORMANCE ANALYSIS OF ANFIS &amp; ANN ALGORITHMS BASED MPPT ENERGY HARVESTING IN SOLAR PV SYSTEM

This paper presents the development and performance analysis of Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller for a DC to DC converter. The proposed system consists of 2.0 kW PV array, DC to DC boost converter and load. In this research work presents the development and performance analysis of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) based Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms are deployed in maximum power point tracking (MPPT) energy harvesting in solar photovoltaic (PV) system to forge a comparative performance analysis of the four different algorithms. A comparative analysis among the algorithms in terms of the performance of handling the trained dataset is presented. The MATLAB/Simulink environment is used to design the maximum power point tracking energy harvesting system and the artificial neural network toolbox is utilized to analyze the developed model. However, considering the dataset training, the correlation between input-output and error, the Levenberg-Marquardt ANFIS algorithm performs better.

A comprehensive survey on authentication and secure key management in internet of things: Challenges, countermeasures, and future directions

Critical infrastructures such as healthcare, intelligent transportation, and home automation adapt billions of smart IoT devices to handle real-time data effectively in the digital era. The latest development in sensory and communication technologies collaborate directly with modern applications to bridge networking services without human intervention. Most information systems enable the extension of digital technology to demonstrate the potential use of next-generation network services and management. However, recent advancements open various opportunities, including repudiation, data tampering, and security threats in digital security and privacy. Thus, this survey article chooses a systematic literature review to present a comprehensive survey on authentication and key management in IoT. To discuss the major issues involved in IoT environments, this survey categorizes the work progress into four modules: 1. Discuss the broad aspects of convergence technologies, including healthcare, smart farming, intelligent transportation, etc., to address challenges such as security and privacy in IoT; 2. Conduct a comprehensive review of state-of-the-art technologies to identify security requirements, services, and challenges associated with modern IoT applications; 3. Present a systematic approach to review various key agreement schemes based on network models and performance analysis to examine potential vulnerabilities. 4. Show a thematic analysis to determine suitable corrective measurements dealing with various security and privacy issues. Finally, this paper rationalizes significant progress, including research challenges and future directions in IoT to promote new insights.

Performance analysis of LSTM network in diagnosis of atrial fibrillation class of Arrhythmia

The abnormality in the rhythm of the heartbeat is known as Arrhythmia. This abnormal rhythm can be fast or slow from the normal rhythm of the heartbeat. Certain abnormalities in the heart can be cured if the diagnosis is done at the earliest. The conventional method of recording and analyzing these rhythms are done using Electrocardiogram. This analysis is done manually which needs the expertise to identify the kind of heart disorder. An alternate solution for this problem is to implement the computer-assisted analysis. Machine learning methods are becoming more popular in various domains which makes a milestone every day through research and development. This paper discusses the results achieved with the implementation of a machine learning algorithm for the diagnosis of atrial fibrillation. The methodology used for the study is Long Short-Term Memory (LSTM) network to classify the signals used for the study into a normal and abnormal rhythm.

Impact of the Interplay Between Mode Dispersion and Kerr Effect in SDM Optical Networks

Space division multiplexing (SDM) – such as based on multi-mode fibers – is under investigation to support the growing capacity demands. In optical networks, the estimation of quality of transmission (QoT) is a fundamental control task to identify the most suitable transmission parameters (e.g., modulation format) to meet proper spectral efficiency while satisfying the required optical reach. The Gaussian Noise (GN) model is widely adopted for QoT estimation. However, the basic version of the GN model does not account for the interplay between mode dispersion and the Kerr effect. In this paper, we investigate the impact of mode dispersion on SDM networks exploiting strongly coupled modes, through an extended GN model. Network performance analysis is evaluated. First, an analysis of the achievable information rate (AIR) with Gaussian data statistics is presented. Then, a network capacity analysis is shown constrained to a set of supported modulation formats and a fixed symbol rate. Finally, blocking probability is studied with the same set of supported modulation formats with variable symbol rates. The presented network analysis shows that accounting for mode dispersion increases the throughput considering: i) Gaussian data statistics, ii) different modulation formats. Moreover, the blocking probability analysis shows a blocking reduction when mode dispersion is considered in the physical layer modeling.