Performance Analysis Of Networks Research Articles

A Study on Catastrophic Retrial Feedback Queueing Network Model with Three Nodes in Steady State

Queueing networks, which are effectively used for performance analysis of various systems, including computer, communications, transportation networks, and manufacturing, are discussed in this article. Under the catastrophic condition, we evaluate a queueing network with feedback and retrial for single server. In the third node the customer can decide whether feedback or leave the system with probabilities t, u and 1-t-u. Also using Little's formula, we are determining the likelihood of having idle customers and determining n number of customers in the system. We are deriving for the above model. The purpose of the numerical example gives the richness of our result and the feasibility of this concept.

Performance Analysis of IRS-Assisted Networks With Product-Distance

Performance Analysis of IRS-Assisted Networks With Product-Distance

Real-Time Co-Simulation Implementation for Voltage and Frequency Regulation in Standalone AC Microgrid with Communication Network Performance Analysis across Traffic Variations

Effective communication networks are crucial for ensuring reliable and stable operation and control in smart microgrids (MGs). This paper proposes a comprehensive analysis of the interdependence between power and communication networks in the real-time control of a standalone AC microgrid to address this vital need. Thus, the role of communication network design is emphasized in facilitating an effective centralized secondary control to regulate the voltage and frequency of an MG. Consequently, voltage and frequency deviations from the droop-based primary control should be eliminated. This study employs a real-time co-simulation testbed setup that integrates OPAL-RT and network simulator (ns-3), supporting a rigorous evaluation of the interplay between the communication networks and control within the MG. Experiments have been conducted to demonstrate the effectiveness of the designed communication infrastructure in seamlessly enabling real-time data exchange among the primary and secondary control layers. Testing scenarios have been implemented, encompassing low-traffic patterns with minimal load variations and high traffic characterized by more frequent and severe load changes. The experimental results highlight the significant impact of traffic variations on communication network performance. Despite the increase in traffic, the effectiveness and reliability of the designed communication network have been validated, underscoring the vital role of communication in ensuring the resilient and stable operation of cyber–physical standalone AC microgrids.

IOS-based mobile application for network performance analysis

In today's world, when technology is developing faster and faster, many people face network malfunctions and threats. Ensuring the security and performance of your own network is a critical task for many IT professionals and ordinary users. There are many tools and ways to monitor, analyse and secure your network, but a mobile app is an extremely convenient option. A mobile application that combines several network functions at once saves time and is always at hand. Networking tools in a smartphone can replace many different desktop applications and websites (web APIs), provide command line tools, etc. The iOS operating system has high security standards and its ecosystem is quite closed. This protects users from a lot of malware and makes the device extremely resistant to hacking. However, it also imposes some restrictions on the network and system for the average user. That's why iOS apps that include a variety of networking tools are a really useful tool for users. To keep the network in an efficient and secure state, iOS has specific ways to monitor and detect network problems: Wi-Fi analysers, checking network routes and interfaces, checking Internet speed, ping, traceroute, etc. The use of these technologies in the application under consideration will enable efficient analysis of network productivity.

Performance analysis of underwater wireless sensor networks using Reinforcement Learning

This article presents a novel routing protocol named DROR, specifically tailored for underwater wireless sensor networks (UWSNs) to tackle the challenge of void regions. DROR integrates Reinforcement Learning (RL) and Opportunistic Routing (OR) in a recipient-oriented approach, considering the energy limitations and the unique underwater setting. It incorporates a mechanism for void rehabilitation, allowing packets to circumvent void nodes and maintain continuous moving for dependable transmission. Furthermore, a dynamic scheduling strategy based on relative Q-values ensures proficient packet forwarding along the most efficient routing path. Simulation outcomes illustrate the efficacy of the suggested protocol concerning delay, PDR, and energy tax in UWSNs with varying Range, Depths, Packet sizes, and moving radius.

A Network Performance Analysis of MQTT Security Protocols with Constrained Hardware in the Dark Net for DMS

In the context of the internet of things, and particularly within distributed measurement systems that are subject to high privacy risks, it is essential to emphasize the need for increasingly effective privacy protections. The idea presented in this work involves managing critical traffic through an architectural proposal aimed at solving the problem of communications between nodes by optimizing both the confidentiality to be guaranteed to the payload and the transmission speed. Specifically, data such as a typical sensor on/off signal could be sent via a standard encrypted channel, while a sensitive aggregate could be transmitted through a dedicated private channel. Additionally, this work emphasizes the critical importance of optimizing message sizes to 5 k-bytes (small payload messages) for transmission over the reserve channel, enhancing both privacy and system responsiveness, a mandatory requirement in distributed measurement systems. By focusing on small, encrypted payloads, the study facilitates secure, timely updates and summaries of network conditions, maintaining the integrity and privacy of communications in even the most challenging and privacy-sensitive environments. This study provides a comprehensive performance analysis of IoT networks using Dark Net technologies and MQTT protocols, with a focus on privacy and anonymity. It highlights the trade-offs between enhanced security and performance, noting increased latency, reduced bandwidth, and network instability when using TOR, particularly with cipher suites like AES256-GCM-SHA384 and DHE-RSA-CHACHA20-POLY1305. The research emphasizes the need for further exploration of alternative protocols like LWM2M in secure IoT environments and calls for optimization to balance privacy with performance in Dark-Net-based IoT deployments.

Efficient resource management in direct and indirect transmission in V2X communications

Autonomous driving systems consist of vehicles that are able to communicate not only with other vehicles but also with entities in their environment, forming vehicle-to-everything (V2X) communication. However, the V2X applications have intense requirements posing a significant challenge to the telecommunication infrastructure. In this work, we consider two types of transmission, i.e. direct and indirect, and we utilize analytical traffic-engineering models with a view to conduct a performance analysis of a vehicular network that enables V2X communication. We additionally propose two resource management strategies in order to decrease the request rejection probability and consequently ensure enhanced communication conditions. The results reveal that the proposed resource management strategies constitute a strong asset for improving the system’s service provisioning capability.

Performance Analysis of NOMA-Enabled Active RIS-Aided MIMO Heterogeneous IoT Networks With Integrated Sensing and Communication

Performance Analysis of NOMA-Enabled Active RIS-Aided MIMO Heterogeneous IoT Networks With Integrated Sensing and Communication

Performance Analysis of an OpenFlow-Enabled Network with POX, Ryu, and ODL Controllers

With the ongoing digital transformation, the requirement for seamless & agile network connectivity has increased. This has necessitated the transformation from traditional networking to Software-Defined Network. With SDN, the network resources/infrastructure can be made more robust and programmable to accommodate the varying demand. Software-Defined Network (SDN) is now being used by data centers, cloud computing environments, and service providers for efficient handling of network requests. The objective of SDN can be achieved through the performance of the controllers. We have considered POX, Ryu, and ODL controllers to evaluate the performance using the Mininet tool and D-ITG (Distributed Internet Traffic Generator) script. Performance is evaluated using custom topology i.e. Fat tree topology. Several performance metrics (such as Delay, Jitter, Bitrate, Packet rate, Loss-burst size, and Percentage of Packets dropped) are considered and the behaviors of the controllers are monitored during experimentation by flooding the switches with several data packets. It has been observed that the POX controller performs better compared to Ryu and ODL for the Fat tree topology. From the experimental results, it is observed that the average delay of ODL is approximately 50% more than the POX controller and approximately 80% more delay occurred in the Ryu controller in comparison to the POX controller. Similarly average packet dropped percentage of POX is 20% less than Ryu and ODL controllers. Also, it has been observed that the average jitter in POX is almost 50% less in comparison to ODL and 90% less in comparison to the Ryu controller.

Enhancing Work Zone Safety: Evaluating Static Merge Strategies Through Microscopic Traffic Simulation

Background The utilization of merging control has been proposed as a strategy to maximize the capacity of roads in work zone areas. The static Early Merge (EM) and static Late Merge (LM) controls are extensively implemented among these strategies. Although many studies have investigated the efficacy of these controls through the analysis of field data or microscopic traffic simulations, such comparisons are frequently conducted under different work zone conditions, which can result in inconsistent, contradictory conclusions. Materials and Methods A simulation study was carried out using the VISSIM microscopic traffic simulator within a self-developed work zone featuring a 2-to-1 lane closure setup to comprehensively assess and contrast the traffic efficiency of the EM and LM controls. Furthermore, through a comprehensive analysis and comparison of network performance within the designed work zone across various scenarios, including queue length, vehicle delays, and travel time, the significant VISSIM parameters influencing the system's performance were identified. Results The analysis revealed that the parameters CC1 (headway time) and CC2 (longitudinal following threshold) from the car-following model exert more significant influence in the simulated work zone throughput than the Safety Distance Reduction Factor (SDRF) parameter from the lane-changing model. Discussion According to the simulation findings, implementing the EM control is preferable when drivers display aggressive behavior and maintain relatively short safety distances (i.e., low CC1 values). Conversely, opting for the LM control is more advisable in work zone areas where drivers demonstrate cautious driving tendencies and maintain longer safety distances (i.e., high CC1 values). Conclusion The efficacy of static EM and LM was analyzed in a 2-to-1 lane closure work zone on a freeway using the microscopic traffic simulator VISSIM. Simulation results were compared to identify the most relevant VISSIM parameters that influence work zone throughput. Our results indicate that the parameters CC1 and CC2 from the car-following model have a more substantial impact than the SDRF parameter from the lane-changing model. In particular, our comparison results suggest that the work zone throughput decreases in both the EM and LM scenarios as the values of CC1 and CC2 increase. Additionally, SDRF has a relatively negligible effect on the network performance of both merge strategies.

Modeling and Performance Analysis of LBT-Based RF-Powered NR-U Network for IoT.

Energy harvesting combined with spectrum sharing offers a promising solution to the growing demand for spectrum while keeping energy costs low. New Radio Unlicensed (NR-U) technology enables telecom operators to utilize unlicensed spectrum in addition to the licensed spectrum already in use. Along with this, the energy demands for the Internet of Things (IoT) can be met through energy harvesting. In this regard, the ubiquity and ease of implementation make the RF-powered NR-U network a sustainable solution for cellular IoT. Using a Markov chain, we model the NR-U network with nodes powered by the base station (BS). We derive closed-form expressions for the normalized saturated throughput of nodes and the BS, along with the mean packet delay at the node. Additionally, we compute the transmit outage probability of the node. These quality of service (QoS) parameters are analyzed for different values of congestion window size, TXOP parameter, maximum energy level, and energy threshold of the node. Additionally, the effect of network density on collision, transmission, and energy harvesting probabilities is observed. We validate our model through simulations.

5G enabled smart cities: A real-world evaluation and analysis of 5G using a pilot smart city application

Ubiquitous sensing in smart cities is expected to be one of the key beneficiaries of the high bandwidth and low latency capabilities of 5G. However, current 5G deployments still have low population coverage, and are unlikely to reach global coverage of above 80% before 2028. This means that new smart city applications are likely to experience a combination of 4G and 5G, limiting their data-intensive capabilities. Thus, it is necessary to assess the ability of current 5G deployments to support emerging smart city applications, and how they perform in 5G environments. Existing performance evaluations focus either on the 5G core or use instantaneous speed tests, which do not effectively assess the suitability of 5G deployments for smart city applications. In this paper, we present a comprehensive evaluation and analysis of real-world 5G network performance observed through the outcomes of a pilot smart city application, an innovative mobile 5G Internet of Things (IoT) solution to automatically identify and report road assets requiring maintenance. The pilot smart city application was deployed on 11 waste collection service trucks over a 6 month period (June 2022–Dec 2022). We undertook both application-specific and application independent network performance evaluation to assess the ability of the 5G deployment to support uninterrupted smart city services. Our analysis shows that while 5G is capable of supporting mobile video streaming applications, there are significant variations in network performance, which may make it unsuitable for applications that require higher data intensive or near real-time responsiveness.

Performance Analysis of a Sound-Based Steganography Wireless Sensor Network to Provide Covert Communications

Performance Analysis of a Sound-Based Steganography Wireless Sensor Network to Provide Covert Communications

Enhancing wireless communication systems through large intelligent surfaces: a performance analysis of LIS-assisted NOMA networks

The integration of large intelligent surfaces (LIS) with non-orthogonal multiple access (NOMA) networks has emerged as a promising solution to enhance the capacity and coverage of wireless communication systems. In this study, we analyse the performance of a NOMA network with the assistance of LIS. We propose a system model where a base station (BS) equipped with a LIS serves multiple users. The LIS consists of many passive elements that can influence the wireless channel by adjusting the reflection coefficients. We consider a downlink scenario where the BS transmits to multiple users simultaneously using NOMA, and the LIS helps to improve the signal quality and coverage. We additionally evaluate the efficiency of the suggested LIS-assisted NOMA network. In addition, we evaluate the efficiency of the LIS-assisted NOMA network in comparison to conventional NOMA systems that do not utilize LISs. The findings indicate that the LIS has a notable impact on enhancing the system's performance in terms of diversity gain, probability of error, and pairwise error probability (PEP). Moreover, the suggested LIS-assisted NOMA network is shown to be superior to conventional NOMA systems through comparison. These findings offer useful insights into the performance analysis of LIS-assisted NOMA networks. They also serve as inspiration and motivation for future research and development in this new subject, with the potential to revolutionize wireless communication systems.

Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models

The rapid growth of the automotive industry has exacerbated the conflict between the complex traffic environment, increasing communication demands, and limited resources. Given the imperative to mitigate traffic and network congestion, analyzing the performance of Internet of Vehicles (IoV) mesh networks is of great practical significance. Most studies focus solely on individual performance metrics and influencing factors, and the adopted simulation tools, such as OPNET, cannot achieve the dynamic link generation of IoV mesh networks. To address these problems, a network performance analysis model based on actual switches is proposed. First, a typical IoV mesh network architecture is constructed and abstracted into a mathematical model that describes how the link and topology changes over time. Then, the task generation model and the task forwarding model based on actual switches are proposed to obtain the real traffic distribution of the network. Finally, a scientific network performance indicator system is constructed. Simulation results demonstrate that, with rising task traffic and decreasing node caching capacity, the packet loss rate increases, and the task arrival rate decreases in the network. The proposed model can effectively evaluate the network performance across various traffic states and provide valuable insights for network construction and enhancement.

Design and performance analysis of asynchronous network on chip for streaming data transmission on FPGA

The majority of the system on chip (SoC) uses the network on chip (NoC) as routing ports for data transfer from node-to-node with minimal power consumption and low latency and high throughput. This paper concentrates on the ability to model the asynchronous NoCs on the asynchronous circuits on field programmable gate arrays (FPGAs). A 3×3 NoC and its universal asynchronous receiver transmitter (UART) protocol is designed and its simulation of the Verilog hardware description language (VHDL) code is done and tested on the Artix-7 FPGA kit, the testing processes in done using the Chipscope tool. In order to meet target requirements in terms of power consumption and latency, the label switching (LS) technique is used as routing. The proposed LS-NoC with level-encoded dual-rail (LEDR) encoding technique provides throughput by registering the packet between the different routers and it helps to improve throughput and speed. The effectiveness of the data transfer is measured and analyzed through a synthesis summary in terms of lookup table’s (LUT’s), slice registers, flip flops’s (FF’s), latency, and packet delivery ratio (PDR) for the traffic pattern generator. The proposed NoC is designed for 8×8 and each port size is 21 bits including ID’s of source and destination routers. The results can be justified by following results: improvement of LUTs is about 12%, flip-flops are 7%, improvement of throughput is 23% and delay is reduced by 26%.

Modeling and Performance Analysis of Vertical Heterogeneous Networks Under 3D Blockage Effects and Multiuser MIMO Systems

Modeling and Performance Analysis of Vertical Heterogeneous Networks Under 3D Blockage Effects and Multiuser MIMO Systems

The Performance Analysis of Graph Neural Network (GNN) and Convolutional Neural Network (CNN) Algorithms for Cyberbullying Detection in Twitter Comments

Cyberbullying incidents have surged due to the expansion of social media network and advancements in internet technology, presenting a substantial challenge in online communities. Previous studies employing Support Vector Machine (SVM) techniques have exhibited promising outcomes, achieving a superior accuracy of 71.25%. However, recognizing the dynamic nature of cyberbullying behaviors and the necessity for more robust detection methodologies, this research explores cyberbullying detection on Twitter utilizing Convolutional Neural Network (CNN) and Graph Neural Network (GNN). The selection of CNN and GNN is motivated by the deficiencies observed in prior SVM-based approaches and the capacity of neural network to capture intricate patterns in textual and network data. The GNN consistently outperforms CNN in terms of F1 score, accuracy, precision, and recall. With only 20 epochs, GNN achieves an accuracy of 80.25%, surpassing CNN's 68.43%. Through GNN optimization, its accuracy reaches 89.04% after 100 epochs, underscoring its efficacy in Twitter cyberbullying detection.

Modeling and performance analysis of UAV-aided millimeter wave cellular networks with stochastic geometry

Modeling and performance analysis of UAV-aided millimeter wave cellular networks with stochastic geometry

Performance Analysis of SWIPT Relay Networks Over Arbitrary Dependent Fading Channels

Performance Analysis of SWIPT Relay Networks Over Arbitrary Dependent Fading Channels