Performance Evaluation Of Networks Research Articles

Correction to: Performance Evaluation of FPGA-Based LSTM Neural Networks for Pulse Signal Detection on Real-Time Radar Warning Receivers

Correction to: Performance Evaluation of FPGA-Based LSTM Neural Networks for Pulse Signal Detection on Real-Time Radar Warning Receivers

Reliable Internet of Things for Health Care Technologies

Background Several at-home monitoring devices are being introduced in the market, which can help individuals, fitness enthusiasts, etc, monitor their health anytime they want. This allows individuals to monitor and collect their health data, reflect upon it, and take necessary action. Such technologies can help enhance the user’s quality of life by motivating and empowering them to improve their health actively. Unfortunately, there are still several challenges to making this transition from in-hospital monitoring to home monitoring smoother. Some of these challenges may include technology readiness and acceptance by patients and their family members, lack of proper privacy measures, security, and lack of reliable internet and communication technology infrastructure. Objective The objective of this study is to use wireless communication networks to remotely transfer data from various body sensors measuring different vital parameters. Wireless sensors (electrocardiogram monitors, sleep sensors, etc) and Internet of Things devices can allow real-time and relatively cheap at-home health monitoring to provide critical health updates over the internet. Methods The study will be conducted by means of designing multiple experiments in which data from different sensors will be collected, packaged, and sent to a remote server using the internet. Along with the patient data, different network performance parameters such as delay, information loss, etc, will be calculated to understand and evaluate network performance. Results The results from the experiment will focus on evaluating network performance parameters such as latency, delay, packet drop, etc, in various indoor as well as outdoor environments. Conclusions We hope the results obtained from these experiment can be used for making various technological design choices and serve as a good starting point while building Internet of Things health care technologies. Conflicts of Interest None declared.

Synthesis and Nanostructure Investigation of Hybrid β-Ga2 O3 /ZnGa2 O4 Nanocomposite Networks with Narrow-Band Green Luminescence and High Initial Electrochemical Capacity.

The material design of functional "aero"-networks offers a facile approach to optical, catalytical, or and electrochemical applications based on multiscale morphologies, high large reactive area, and prominent material diversity. Here in this paper, the synthesis and structural characterization of a hybrid β-Ga2 O3 /ZnGa2 O4 nanocomposite aero-network are presented. The nanocomposite networks are studied on multiscale with respect to their micro- and nanostructure by X-ray diffraction (XRD) and transmission electron microscopy (TEM) and are characterized for their photoluminescent response to UV light excitation and their electrochemical performance with Li-ion conversion reaction. The structural investigations reveal the simultaneous transformation of the precursor aero-GaN(ZnO) network into hollow architectures composed of β-Ga2 O3 and ZnGa2 O4 nanocrystals with a phase ratio of ≈1:2. The photoluminescence of hybrid aero-β-Ga2 O3 /ZnGa2 O4 nanocomposite networks demonstrates narrow band (λem = 504nm) green light emission of ZnGa2 O4 under UV light excitation (λex = 300nm). The evaluation of the metal-oxide network performance for electrochemical application for Li-ion batteries shows high initial capacities of ≈714 mAh g-1 at 100mA g-1 paired with exceptional rate performance even at high current densities of 4 A g-1 with 347 mAh g-1 . This study provides is an exciting showcase example of novel networked materials and demonstrates the opportunities of tailored micro-/nanostructures for diverse applications a diversity of possible applications.

Peer-to-Peer User Identity Verification Time Optimization in IoT Blockchain Network.

Blockchain introduces challenges related to the reliability of user identity and identity management systems; this includes detecting unfalsified identities linked to IoT applications. This study focuses on optimizing user identity verification time by employing an efficient encryption algorithm for the user signature in a peer-to-peer decentralized IoT blockchain network. To achieve this, a user signature-based identity management framework is examined by using various encryption techniques and contrasting various hash functions built on top of the Modified Merkle Hash Tree (MMHT) data structure algorithm. The paper presents the execution of varying dataset sizes based on transactions between nodes to test the scalability of the proposed design for secure blockchain communication. The results show that the MMHT data structure algorithm using SHA3 and AES-128 encryption algorithm gives the lowest execution time, offering a minimum of 36% gain in time optimization compared to other algorithms. This work shows that using the AES-128 encryption algorithm with the MMHT algorithm and SHA3 hash function not only identifies malicious codes but also improves user integrity check performance in a blockchain network, while ensuring network scalability. Therefore, this study presents the performance evaluation of a blockchain network considering its distinct types, properties, components, and algorithms' taxonomy.

Research on key technologies of multi-core DSP in smart substation redundancy network testing

Aiming at the network characteristics of a smart substation redundant network with complex communication messages, a large amount of interactive data, and high real-time performance, the test model of multiple submachines in a High-Availability Seamless Redundancy (HSR) loop network is analyzed. The requirements of packet types, size, and transmission speed are evaluated, and a hardware solution based on a multi-core Digital Signal Processor (DSP) and Parallel architecture FPGA is designed. The multi-core interaction strategy of DSP software and the multi-port high-speed communication method of FPGA are proposed, and the inter-core communication (IPC) interrupts and shared memory to achieve fast data migration between cores, based on the Serial RapidIO (SRIO) to complete high-speed message transmission. The throughput, delay, packet loss rate, and error frame detection functions are realized, tested, and verified, providing theoretical and practical support for the evaluation of redundant network performance of smart substations.

Coverage Performance Evaluation for Cellular Networks With Correlated Blockage Model

Analyzing correlation effects caused by the realistic geometry of blockage is crucial to evaluate network performance in wireless networks. However, the correlation generally complicates analysis procedure even makes it intractable. Based on the cascade blockage model, this paper proposes a functional framework for performance evaluation from a vision of typical user. We first derive a functional equation to obtain the association probability that can be solved efficiently by an association iterative algorithm. Then, they are leveraged to design a coverage iterative algorithm to analyze the coverage probability in the form of joint distribution. This analytical framework gains insight into the effect of blockage correlation and penetration loss on network performance. In addition, it is applied to inter-cell interference coordination (ICIC) context to demonstrate its feasibility and flexibility.

QB-II for evaluating the reliability of binary-state networks

Reliability is one of the most popular tools for evaluating network performance. A new algorithm called QB-II (quick binary-addition tree algorithm II) is proposed to efficiently assess the reliability of the binary-state network which is the fundamental of current real-life networks. The QB-II implements the shortest minimum cuts (MCs) to separate the entire network into smaller sub-networks, and the source-target matrix convolution products to connect these sub-networks intelligently, and the aggregated probability to improve the efficiency in calculating the network reliability. Twenty benchmark problems were used to validate the performance of the QB-II. • Propose a method to separate networks into smaller sub-networks. • Propose a new convoution product to combine vectors in smaller sub-networks. • Propose an aggregated method to calculate the vector probaility efficiently. • Propose a first node-based algorithm for the problem. • Propose an improved quick BAT for the problem.

Performance Assessment of a Wireless Mesh Network for Post-harvest Food Quality Traceability of Fruit Products: A Case Study

This paper presents the performance evaluation of a wireless sensor mesh network, for monitoring temperature and humidity in horticultural products when transported in truck galleys. For this purpose a software solution was proposed using ESP8266 devices powered by batteries. The mesh network was managed by the painlessMesh library. The proposed solution aims to minimize the energy consumption of the sensor nodes. The validation of the solution was performed in an area simulating a galley of a truck, where five sensor nodes and a root node were distributed. The tests were developed considering four different models involving variations in messages delivery confirmation, number of attempts until successful delivery and duty cycle duration of the nodes. The performance evaluation of the solution aimed to determine, connectivity rate, sending rate after connection and delivery rates of the first and second attempts. The results obtained show that the message delivery confirmation does not bring added value to the solution, contributing only to increase energy consumption. The use of synchronous duty cycles also showed worse results than the asynchronous use. These results allow the creation of a knowledge base for the use of this solution in a real context.

One-dimensional VGGNet for high-dimensional data

We consider a deep learning model for classifying high-dimensional data and seek to achieve optimal evaluation accuracy and robustness based on multicriteria decision-making (MCDM) for high-dimensional data analysis applications during comprehensive evaluation (CE) activities. We propose a novel one-dimensional visual geometry group network (1D_VGGNet) to overcome the problem that high-dimensional data are too complicated and unstable to be feasibly applied. Then, to effectively handle one-dimensional MCDM, we present a 1D_VGGNet classifier to replace the two-dimensional convolution operation applied to image data with a one-dimensional convolution operation applied to one-dimensional MCDM. Furthermore, to solve the invariance problem of the generated feature maps, the maxpooling kernel size can be flexibly adjusted to effectively meet the requirements of reducing the feature map dimension and speeding up training and prediction on different datasets. The improvement is reasonable for various high-dimensional data application scenarios. Moreover, we propose a novel objective function to accurately evaluate network performance since the objective function includes a variety of representative performance evaluation metrics, and the average value is calculated as one of the CE metrics. The experimental results illustrate that the proposed framework outperforms a one-dimensional convolutional neural network (1D_CNN) for comprehensive classification on the Shaoxing University student achievement dataset and the MIT-BIH Arrhythmia database and achieves average gains of 36.3% and 12.1% in terms of the designated evaluation metric.

Path Planning for UAV Communication Networks: Related Technologies, Solutions, and Opportunities

Path planning has been a hot and challenging field in unmanned aerial vehicles (UAV). With the increasing demand of society and the continuous progress of technologies, UAV communication networks (UAVCN) are also flourishing. The mobility of UAV nodes allows for flexible network deployment, but some challenges are brought, such as power constraints, throughput, cost, and time efficiency. Therefore, path planning is significant for UAVCN. This article presents a review of UAVCN path planning. We first introduce the network structure and performance evaluation of UAVCN. We then investigate the generic UAV path planning algorithms and the path planning algorithms in UAVCN. In this article, the advantages and disadvantages of each path planning algorithm and the functional problems. The challenges faced in path planning for UAVCN, the solutions, state-of-the-art, and representative results are also presented. In addition, we illustrate future research directions for UAVCN path planning as well, which can provide some help to researchers.

Power Grid Structure Performance Evaluation Based on Complex Network Cascade Failure Analysis

A safe and stable operation power system is very important for the maintenance of national industrial security and social economy. However, with the increasing complexity of the power grid topology and its operation, new challenges in estimating and evaluating the grid structure performance have received significant attention. Complex network theory transfers the power grid to a network with nodes and links, which helps evaluate the system conveniently with a global view. In this paper, we employ the complex network method to address the cascade failure process and grid structure performance assessment simultaneously. Firstly, a grid cascade failure model based on network topology and power system characteristics is constructed. Then, a set of performance evaluation indicators, including invulnerability, reliability, and vulnerability, is proposed based on the actual functional properties of the grid by renewing the power-weighted degree, medium, and clustering coefficients according to the network cascade failure. Finally, a comprehensive network performance evaluation index, which combines the invulnerability, reliability, and vulnerability indicators with an entropy-based objective weighting method, is put forward in this study. In order to confirm the approach’s efficacy, an IEEE-30 bus system is employed for a case study. Numerical results show that the weighted integrated index with a functional network could better evaluate the power grid performance than the unweighted index with a topology network, which demonstrates and validates the effectiveness of the method proposed in this paper.

UAV-Correlated MIMO Channels: 3-D Geometrical-Based Polarized Model and Capacity Analysis

The unmanned aerial vehicle (UAV) communication with the multiple-input–multiple-output (MIMO) system has attracted lots of attention to improve spectral efficiency and channel capacity. The large antenna spacing is often required to reduce the spatial correlation between subchannels. However, this requirement cannot be well satisfied in practical scenarios due to the limited dimension of UAV platforms. Therefore, polarization diversity is a promising approach for UAV MIMO systems. In this work, we propose a three-dimensional (3-D) geometrical-based polarized model for UAV-correlated MIMO channels. By utilizing the geometrical theory of polarization, we describe the channel depolarization caused by the terrestrial scattering environment based on the multicylinder geometrical model and then acquire the channel polarization function and polarized channel impulse response (CIR). Furthermore, we investigate the impact of the key factors, such as the UAV tilt rotation, cross-polarization, and limited antenna spacing on the UAV MIMO channel capacity. To validate the proposed UAV channel model, we compare the spatial correlations of the numerical simulation and UAV field measurement results for the co- and cross-polarized channels, and the close agreements between them are observed. This work can provide useful guidance and support for the design and performance evaluation of UAV communication networks.

Системи моніторингу комп’ютерних мереж

Monitoring tools are an important component of network infrastructure and provide automation of network administration processes. The effectiveness of using a monitoring system is a spe-cific indicator of the computer network quality and it depends on the specified settings and sce-narios of its use. The main characteristics and parameters that should be investigated and taken into account when deploying a computer network monitoring system are network scalability, the anticipation of updating the existing system after the deployment, the required level of net-work monitoring complexity, and service functionality. Network management and monitoring tools should include the following key features: detailed analytics, compatibility, simplified overview mode, notifications, and account and interface settings. Detailed analytics and reports are the foundation of network monitoring. The chosen tool should evaluate network perfor-mance by processing parameters of network speed and latency. Compatibility allows you to in-teract with various components of the IT infrastructure. A wide range of compatibility provides a greater variety of characteristics of elements and greater adaptability of the service to the real conditions of a particular enterprise. Alerts from the monitoring system must be instantaneous and sent whenever a threshold value is exceeded or a device is disabled. Monitoring systems allow you to edit the type and severity level of an alert, as well as create monitoring categories and templates. Accounts are an important aspect, particularly for remote work. The user inter-face is important for organizing the process of working with a computer network monitoring system from the point of view of efficiency. A well-organized interface speeds up the interac-tion of an administrator or an operator with information about the current state of the system. An important characteristic of the interface is the adaptability of display on different types of devices: smartphones, tablets, computers, etc.

Joint Beamforming Optimization Design and Performance Evaluation of RIS-Aided Wireless Networks: A Comprehensive State-of-the-Art Review

Joint Beamforming Optimization Design and Performance Evaluation of RIS-Aided Wireless Networks: A Comprehensive State-of-the-Art Review

Performance Evaluation of Deep Convolutional Neural Networks and Transfer Learning on Classification of Diseases from Chest Radiograph Images

Performance Evaluation of Deep Convolutional Neural Networks and Transfer Learning on Classification of Diseases from Chest Radiograph Images

Utilizing Deep Reinforcement Learning to Control UAV Movement for Environmental Monitoring

Unmanned aerial vehicles (UAVs) are increasingly used in various applications, including infrastructure inspection, traffic monitoring, remote sensing, mapping, and rescue. However, many applications have required UAVs to function autonomously, without human intervention to improve system performance. In this study, we propose a new approach to environmental monitoring using a group of UAVs equipped with sensors under the support of reinforcement learning. Regarding the communication system model, we assume that UAVs can cooperate with each other to learn and share information about the environment, and then relocate to an optimal position while managing connectivity and coverage. After that, we exploit reinforcement learning with a deep deterministic policy gradient (DDPG) algorithm to optimize environmental monitoring with the proposed algorithm. Specifically, the proposed algorithm aims to simulate an environmental monitoring system using UAVs with basic parameters. We further apply the proposed algorithm to evaluate network performance under different parameter settings. Numerical results validate the effectiveness of the proposed learning-based framework in monitoring and sensing data.

Performance Evaluation of UDSM Network to Deliver Multimedia eLearning Contents: Case of CoICT

The use of technology in education in Sub-Saharan Africa has been reported to be hindered by many factors including inadequate Information and Communication Technology (ICT) infrastructure, low internet speed, and lack of skills to use educational technologies. This paper aim at evaluating how the available ICT infrastructure at the College of Information and Communication Technologies (CoICT) can support, with acceptable Quality of Service (QoS), the delivery of multimedia applications for eLearning services. The evaluation was performed over a network testbed implemented in a computer laboratory for wired and wireless connections. The testbed consisted of five personal computers (PCs) connected via a switch and a router to the internet for the wired connection. The wireless connection consisted of three PCs and a laptop connected through a switch and a router to the internet via a wireless access point. Four different multimedia applications were run through the PCs, and traffic data was mirrored and captured at one of the PC using Wireshark network analyzer. The traffic included streamed and conversational video and audio using YouTube and Zoom applications, respectively; interactive web browsing using web browsers; bulk file transfer, and a mixture of all applications running on the computers. Traffic data captured during the running of these applications were packet loss, delay, jitter, and throughput. The QoS performance parameters obtained were within the acceptable threshold values as per International Telecommunication Union, Telecommunication Standardization Sector (ITU-T) values. Results revealed that ICT infrastructure at CoICT can support delivery of multimedia content for eLearning purposes for both wired and wireless connections. Having the infrastructure capable of supporting multimedia applications with the acceptable QoS implies that instructors are equipped with a conducive environment that supports multimedia application. Hence, they are expected to use and continue using educational technologies to facilitate teaching and learning processes.

Asymptotics of a time bounded cylinder model

AbstractOne way to model telecommunication networks are static Boolean models. However, dynamics such as node mobility have a significant impact on the performance evaluation of such networks. Consider a Boolean model in $\mathbb {R}^d$ and a random direction movement scheme. Given a fixed time horizon $T>0$, we model these movements via cylinders in $\mathbb {R}^d \times [0,T]$. In this work, we derive central limit theorems for functionals of the union of these cylinders. The volume and the number of isolated cylinders and the Euler characteristic of the random set are considered and give an answer to the achievable throughput, the availability of nodes, and the topological structure of the network.

Enhanced system for ns2 trace file analysis with network performance evaluation

One of the critical challenges facing operators around the world is how to ensure that everything is running smoothly as well as how to analyze the performance of the network. Nonetheless, the analytic system must be precise, user-friendly, and quick enough to depict network performance in real time. Network performance is essential for ensuring service quality. In this light, the Network Simulator NS-2 is generally employed for network research on the widely-used UNIX and Windows systems. Next, the network scenarios are generated using network simulation scripts, and upon completion of the simulation, trace files that record network events are generated. Information that might be utilized in a performance study could be recorded in trace files. This could include the total number of packets sent, their arrival times, any delays or losses, and so on. This trace file analysis problem arises mostly because NS-2 does not provide any visualization options for analyzing simulation results (trace files). A system is proposed to analyze the NS2 trace file and facilitate data extraction and evaluation. The main feature of this system is the Java language for network performance evaluation. And to prove the validity of the system, the system was compared with the Trace graph, where the speed of the new system was very high.

Performance Evaluation of FPGA-Based LSTM Neural Networks for Pulse Signal Detection on Real-Time Radar Warning Receivers

Abstract Radar warning receivers are real-time systems used to detect emitted signals by the enemy targets. The conventional method of detecting the signal is to determine the noise floor and differentiate the signals above the noise floor by setting a threshold value. The common methodology for detecting signals in noisy environment is Constant False Alarm Rate (CFAR) detection. In CFAR methodology, threshold level is determined for a specified probability of false alarm. CFAR dictates the signal power to be detected is higher than the noise floor, i.e. signal-to-noise ratio (SNR) should be positive. To detect radar signals for negative SNR values machine learning techniques can be used. It is possible to detect radar signals for negative SNR values by Long Short-Term Memory (LSTM) Artificial Neural Network (ANN). In this study, we evaluated whether LSTM ANN can replace the CFAR algorithm for signal detection in real-time radar receiver systems. We implemented a Field Programmable Gate Array (FPGA) based LSTM ANN architecture, where pulse signal detection could be performed with 94% success rate at -5 dB SNR level. To the best of our knowledge our study is the first where LSTM ANN is implemented on FPGA for radar warning receiver signal detection.