Network Simulation Tool Research Articles

Kiphynet: an online network simulation tool connecting cellular kinetics and physiological transport.

Human metabolism is sustained by functional networks that operate at diverse scales. Capturing local and global dynamics in the human body by hierarchically bridging multi-scale functional networks is a major challenge in physiological modeling. To develop an interactive, user-friendly web application that facilitates the simulation and visualization of advection-dispersion transport in three-dimensional (3D) microvascular networks, biochemical exchange, and metabolic reactions in the tissue layer surrounding the vasculature. To help modelers combine and simulate biochemical processes occurring at multiple scales, KiPhyNet deploys our discrete graph-based modeling framework that bridges functional networks existing at diverse scales. KiPhyNet is implemented in Python based on Apache web server using MATLAB as the simulator engine. KiPhyNet provides the functionality to assimilate multi-omics data from clinical and experimental studies as well as vascular data from imaging studies to investigate the role of structural changes in vascular topology on the functional response of the tissue. With the network topology, its biophysical attributes, values of initial and boundary conditions, parameterized kinetic constants, biochemical species-specific transport properties such as diffusivity as inputs, a user can use our application to simulate and view the simulation results. The results of steady-state velocity and pressure fields and dynamic concentration fields can be interactively examined. KiPhyNet provides barrier-free access to perform time-course simulation experiments by building multi-scale models of microvascular networks in physiology, using a discrete modeling framework. KiPhyNet is freely accessible at http://pallab.cds.iisc.ac.in/kiphynet/ and the documentation is available at https://deepamahm.github.io/kiphynet_docs/ .

Connecting quantum cities: simulation of a satellite-based quantum network

We present and analyze an architecture for a European-scale quantum network using satellite links to connect Quantum Cities, which are metropolitan quantum networks with minimal hardware requirements for the end users. Using NetSquid, a quantum network simulation tool based on discrete events, we assess and benchmark the performance of such a network linking distant locations in Europe in terms of quantum key distribution rates, considering realistic parameters for currently available or near-term technology. Our results highlight the key parameters and the limits of current satellite quantum communication links and can be used to assist the design of future missions. We also discuss the possibility of using high-altitude balloons as an alternative to satellites.

Optimizing OSPF Cost Value for Best Path Selection Using Metric-Type Variation

Open Shortest Path First (OSPF) is a multi-vendor routing standard. Any routing protocol's goal is to discover the optimum path between source and destination addresses inside a computer network. Other routing protocols include Routing Information Protocol (RIP), Border Gateway Protocol (BGP), Enhanced Interior Gateway Routing Protocol (EIGRP), and IS-IS. which are also used in computer networking. Each routing protocol has different set of rules through which they calculate the best path selection. OSPF is often used with the combination of these routing protocol because of its flexible design. This research focuses on the implementation of OSPF network cost (metric) value for best path calculation using network simulation tool like GNS3 for getting correct outcome just like a real networking device working in a live production environment. This research would use the different combinations of OSPF cost calculation using metric-types for external routes learned from different routing protocols like RIP. Being able to accurately identify cost (metric) values obtain by testing for OSPF when used with other routing protocols, just like a real-world environment.

Design and simulation of district heating networks: A review of modeling approaches and tools

District heating (DH) systems have been identified as an important component in efforts to mitigate greenhouse gas emissions and achieve climate targets in the near future. Software tools for analyzing these systems use different modeling approaches, making it challenging for practitioners to choose the most suitable tool based on specific use case requirements. This paper systematically reviews available DH network modeling and simulation tools, comparing their modeling approaches, application scope, and functional capabilities. An online survey was conducted among DH tool developers and experts to assess the technical evaluation criteria for these tools. First, the basic concepts and principles of DH networks modeling are discussed. Moreover, it provides an overview of DH network mathematical models, and discusses various numerical methods used for simulation and analysis, while highlighting potential areas for further research and tools development. The aim of this paper is to support academic and industrial users in selecting appropriate modeling approaches, tools, and libraries.

Network simulation tools for unmanned aerial vehicle communications: A survey

SummaryUnmanned aerial vehicle (UAV) communication has been proposed as an effective solution in both military and civilian scenarios, with low cost, high efficiency, high flexibility, and on‐demand deployment. Network simulation is an economically efficient method for validating new ideas in UAV communication. While some simulation tools have been proposed for UAV communications, there is a lack of a state‐of‐the‐art review to guide newcomers in this research field. In existing surveys, the discussion of network simulators is not comprehensive and the content discussed is outdated. There has also been no discussion of open‐source UAV simulation tools. To fill these research gaps, this survey presents an updated and comprehensive survey of UAV network simulation tools, including a unique collection of open‐source tools. Research challenges and opportunities have been proposed to inspire follow‐up studies.

Enhanced Whale Optimization Algorithm for Multi-Objective Node Disjoint Routing

Objectives: The research goal is to identify node-disjoint pathways maximizing path lifetime, delivery ratio, etc. while establishing several node-disjoint paths to optimize routing and enhance interference reduction and PDR efficiency. Methods: In the MANET, the Enhanced Whale Optimization Algorithm (EWOA) is utilized to discover the most secure routing path. The dataset used in this research comprises simulated scenarios representing various network conditions and configurations, allowing for a comprehensive evaluation of the proposed work. The software employed for simulation and analysis includes an NS-3 network simulation tool, allowing the model to evaluate the performance of the proposed routing protocol in diverse MANET scenarios. The parameters considered during the evaluation include throughput, end-to-end delay, packet delivery ratio (PDR), and routing overhead. The modifications involve enhancing the routing algorithm to prioritize node-disjoint pathways, thus improving interference reduction, energy efficiency, and overall network performance. Findings: The EWOA for Multi-Objective Node Disjoint Routing demonstrates superior performance compared to existing routing protocols such as SRABC and OLSR across various key metrics. Specifically, in terms of end-to-end delay, EWOA consistently outperforms SRABC and OLSR by reducing delay by 1.3% and 4.7%, respectively. Additionally, EWOA achieves a higher packet delivery ratio, surpassing SRABC and OLSR by 4.3% and 0.4%, respectively. Moreover, EWOA exhibits higher throughput, with a throughput increase of 11.3% compared to SRABC and 6.2% compared to OLSR. Furthermore, EWOA demonstrates lower routing overhead, reducing overhead by 9.6% compared to SRABC and 3.1% compared to OLSR. These findings highlight the efficacy of EWOA in optimizing multi-objective routing in MANETs, offering improved network performance and efficiency compared to existing protocols. Novelty: The Multi-Objective Node Disjoint Routing Protocol with Enhanced Whale Optimization is utilized in MANET to choose the best route, reducing latency resulting from link failure and distributing traffic loads across multiple paths. Keywords: Optimization, Whale Optimization Algorithm (WOA), Optimized Link State Routing (OLSR), Secure Routing Algorithm Blockchain Technology (SRABC)), MANET

Data-Driven Strategies in the Evolutionary Game of Green Port Construction: Analyzing Policy Incentives for Shore Power Adoption

This study addresses the critical challenge of green port construction in China, with a focus on the underutilized potential of shore power technology, through a computational lens. By establishing an evolutionary game model that captures the intricate dynamics among ports, shipping companies, and government agencies, we introduce a computational framework that utilizes advanced data analytics, network simulations, and optimization algorithms to evaluate policy incentives and their efficacy. The heart of our analysis lies in the deployment of network simulation tools and predictive analytics, which facilitate a deep dive into the operational dynamics and strategy adaptations within the tripartite system. Our findings underscore the pivotal role of government subsidies, quantitatively determined through optimization algorithms, in influencing the evolutionary path towards sustainable port operations. The research demonstrates that a carefully calibrated subsidy regime, informed by simulation outcomes and data-driven insights, can significantly boost the adoption of shore power technologies among ports and shipping companies, nurturing a symbiotic relationship that accelerates green construction efforts. Furthermore, the study leverages computational algorithms to simulate initial game scenarios, revealing how the presence of shore power infrastructure and technology-ready ships can catalyze a cooperative push towards environmental goals. Through a comprehensive computational approach, including the use of mathematical modeling and the analysis of vast datasets, the paper highlights the transformative power of policy interventions guided by computational intelligence. The conclusions drawn not only emphasize the necessity for targeted government action but also showcase the potential of computer science methodologies in crafting and implementing effective environmental policies, offering a novel pathway to expedite the green transformation of China’s maritime infrastructure.

Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm‐based channel estimation in OFDM

SummaryDual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm‐based channel estimation in OFDM (DiWGAN‐Hyb AOA‐COA‐MIMO‐OFDM) is proposed in this manuscript. In OFDM, there is a non‐stationary channel physical appearance during channel estimation (CE). Therefore in this work, Hyb AOA‐COA is employed to enhance the DiWGAN weight parameters. The proposed DiWGAN‐Hyb AOA‐COA‐MIMO‐OFDM technique is executed in network simulator (NS2) tool. The proposed technique attains lower computational cost 99.67%, 92.34%, and 97.45%; lesser bit error rate 98.33%, 83.12%, and 88.96%; and lesser mean square error 93.15%, 79.90%, and 92.88% compared with existing methods, like MIMO‐OFDM system using deep neural network and MN‐based improved AMO model (DNN‐IAMO‐MIMO‐OFDM), MIMO‐OFDM systems using the deep learning and optimization (RBFNN‐PSO‐MIMO‐OFDM), and MIMO‐OFDM systems using hybrid neural network (HNN‐CSI‐MIMO‐OFDM) respectively.

Review of common spiking neural network simulation tools

As a recognized third-generation neural network, the spiking neural network has high concurrency and complexity. Although the degree of research is still far from the previous generation of neural networks, spiking neural networks are excellent in performance and energy consumption. In this paper, the common spiking neural network simulation tools are reviewed. The most frequently used and mentioned tools are NEURON, NEST, and BRAIN. NEURON is more suitable for simulation based on biological applications, pays more attention to biological characteristics, and can support large-scale network simulation. Examples used in the official documentation are neural simulations of invertebrates and mammals. Large heterogeneous networks of point neurons or neurons with a few compartments are frequently simulated using NEST. In contrast to models that concentrate on the specific morphological and biophysical characteristics of individual neurons, NEST is appropriate for those that emphasize the dynamics, size, and structure of the nervous system. Brian was originally designed for research and teaching and is well suited as a teaching and presentation tool for simulating and observing the effects of different parameters for classical neural network projects such as picture classification. In addition, CSIM, SPLIT, SPINNAKER, and other tools also have their merits, but due to the low frequency of relevant references and lack of universality, this study will not give a detailed introduction.

A highly robust gas network simulation approach through an inherently solvable problem formulation for network states far from intended design points

Pipeline networks are an efficient and widespread transportation system for supplying natural gas as well as increasingly green gas to Europe. However, they are exposed to risks arising from environmental factors, accidents, crime and political issues. This work contributes to the assessment of gas transmission networks and enables a targeted improvement of their resilience. Therefore, the objective of this work is to develop a simulation tool that enables gas transmission system operators (TSOs) to identify weak spots, e.g. potential bottle necks, in their pipeline networks. To this end, a steady-state gas network simulation approach is developed that provides fast results for gas supply deficiencies for complete sets of multi-event disruption scenarios, enabling the identification of the most important network elements. This paper describes how to implement a computationally fast, numerically robust, and physically accurate gas network simulation and assessment tool. By solving mass and momentum balance equations, it accounts for key network elements such as flow control valves, pressure control valves, compressor stations, gas sources, and gas consumers. The central feature that distinguishes this approach from classical steady state solvers is the robustness of the gas flow calculation through the choice of a problem boundary condition formulation, which results in an inherently solvable system of equations. This feature is key in ensuring mathematical convergence of predictions for gas network states that strongly deviate from their original design point, where boundary conditions must not become mathematically invalid but need to reflect physically reasonable behavior. The capabilities of the system are demonstrated using a fictitious gas network and a representative national gas transmission network. Finally, the limits of applicability are outlined based on the size of the network under consideration and the types of analyses.

Dual-discriminator Conditional Generative Adversarial Network Optimized with Hybrid Momentum Search Algorithm and Giza Pyramids Construction Algorithm for Cluster-based Routing in WSN Assisted IoT

Wireless sensor network (WSN) efficiently sends and receives the data on the internet of things (IoT) environment. As a large-scale WSN's nodes are powered by batteries, it is essential to create an energy-efficient system to decrease energy consumption and increase the network's lifespan. The existing methods not present effectual cluster head (CH) selection and trust node computation. Therefore, dual-discriminator conditional generative adversarial network optimized with a hybrid Momentum search algorithm and Giza Pyramids Construction algorithm for Cluster Based Routing in WSN Assisted IoT is proposed in this manuscript, for securing data transmission by identifying the optimum CH in the network (DDcGAN-MSA-GPCA-CBR-WSN-IoT). Initially, the proposed method is acting routing process via cluster head. Therefore, Dual-Discriminator conditional Generative Adversarial Network (DDcGAN) is considered to select the CH depending on multi-objective fitness function. The multi-objective fitness function, such as energy, delay, throughput, distance among the nodes, cluster density, capacity, collision, traffic rate, and cluster density. Based on fitness function, CH is selected. After cluster head selection, a malicious node depends on three parameters: trust, delay, and distance. These three parameters are optimized by hyb MSA-GPCA for ideal trust path selection. The proposed DDcGAN-MSA-GPCA-WSN-IoT technique is activated in PYTHON and network simulator (NS2) tool. Its effectiveness is analyzed under performance metrics, such as number of alive nodes, dead nodes, delay, energy consumption, packet delivery ratio, a lifetime of sensor nodes, and total residual energy. The simulation outcomes display that the proposed method attains lower delay, higher packet delivery ratio and high network lifetime when comparing to the existing models.

Hybrid trust-based secure routing protocol for detection of routing attacks in environment monitoring over MANETs

Maintaining a healthy environment requires constant vigilance. Keeping constant tabs on environmental factors might be a useful tool in the fight against and recovery from environmental degradation. It is possible to send environmental data from strategically positioned sensors that monitor the surrounding circumstances. Environmentalists are the first to arrive at a disaster site, assess the situation, and take corrective measures. To get information from the sensors, they must coordinate with one another. Remote environmental sensing, the tracking from several physical systems, as well as risk assessment and management, have all seen significant improvements as a result of the use of these networks, which are comprised of many dispersed devices, each of which includes sensing, processing, and wireless technology capabilities. To facilitate communication between sensors and ecologists, the authors of this research suggest the usage of MANETs. To address these issues, a hybrid trust-based secure routing protocol is designed. Firstly, nodes are clustered using the Skill Optimization Technique. The Direct and Indirect (Hybrid) trust is computed using the Deep Q-Learning technique and then Routing is established using Diffusion Convolutional Neural Networks. With these methods, routing attacks such as Black Hole, Gray hole, and Wormhole attacks are identified and compared with the traditional security protocols using network simulation tools.

الباكت تريزر كأداة فعاله في تعلم مادة شبكات الحاسوب لطلبة المرحلة الجامعية في جامعة طبرق

Technologies related to computer networks have been expanding rapidly. Computer networks need to be understood theoretically as well as practically, hence network simulation tools should be recommended to address this challenge. Students may use networking simulation tools to build, tune, and evaluate networks while strengthening their understanding of networking theory.On the market, there are several network simulation programs that enable users to simulate LANs, MANs, and WANs such as Ns3, OPNET and Packet Tracer.The objective of this research is to investigate the effectiveness of utilizing Cisco Packet Tracer as an educational tool. In addition, this paper aims to study what extend Packet tracer tool enhance student's understanding of computer networks theoretically and practically. Respondents of the study were the 41 second and fourth year students from Computer Science department at University of Tobruk who took Network course. The questionnaires were used as the research instruments in this stud. Data collected from questionnaire was analyzed using software program The Statistical Package for the Social Sciences (SPSS). In brief, Cisco Packet Tracer has been provena powerful simulation and visualization tool for aiding in theteaching and learning of Computer Networking courses. Keywords: Learning approaches, computer network, Simulation, Packet tracer tool.

Energy Efficient MAC for M2M Communication Using Dynamic TDMA in IoT Network

In the Internet of Things (IoT) context, the relevance of M2M communication increased, creating the need for practical solutions. MTCDs, or Machine Type Communication Devices, frequently encounter issues such as collisions and delays while transmitting data, ensuring network scalability and maintaining the quality of service (QoS) for all devices participating in the communication. To address these challenges, this paper presents the protocol for switching MAC that optimizes energy usage with Dynamic Time Division Multiple Access (EMAC-DTDMA). TheEMAC-DTDMA protocol involves grouping, dynamic MAC switching, and timeslot allocation. The Harris Hawks Optimization (HHO) algorithm is applied to choose the cluster head of the machine type communication devices (MTCH) while the CSMA/CA and CSMA/CARP protocols with different backoff times are used to minimize collisions based on device density, backlog, and active nodes. The timeslots are allocated based on data size and QoS requirements using Dynamic TDMA. The Markov chain model is employed to overcome synchronization issues with traditional TDMA. The EMAC-DTDMA's performance is evaluated through simulation using a network simulator tool, considering access delay, energy usage, collision probability, and throughput.

Interactive Network Learning: An Assessment of EVE-NG Platform in Educational Settings

Abstract This article compares EVE-NG, a network emulation software, to other commonly used network simulation tools such as Cisco Packet Tracer and GNS3. EVE-NG’s features, benefits, and drawbacks are assessed in terms of system requirements, support platforms, web interface, console access, topology management, and advanced lab functionalities. EVE-NG is tested by the students in the real-world application, and feedback from a group of students is gathered regarding their satisfaction and suggestions for improvement. The results of this study show that EVE-NG provides a robust and versatile tool for simulating complex network scenarios, despite compatibility, reliability, and performance issues. It is concluded that EVE-NG has the potential to improve student learning outcomes in computer networks; however, ongoing development and refinement are necessary to meet user expectations and needs.

Performance Evaluation of 802.11ax OFDMA Through Theoretical Analysis and Simulations

With the introduction of Orthogonal Frequency Division Multiple Access (OFDMA) in 802.11ax, the role of the Access Point (AP) in Wi-Fi networks changes significantly, thanks to the opportunity of implementing more complex scheduling logic to handle Downlink (DL) traffic flows and simultaneously act as coordinator of Multi User (MU) Uplink (UL) transmissions. In this context, it becomes necessary to develop reliable network analysis and simulation tools that allow for an in-depth investigation of the trade-offs involved in the usage of OFDMA, especially considering that the standard leaves much of the actual scheduling algorithmic details to vendor-specific implementation. In this work we present a series of results highlighting how several network settings have an impact on throughput and Head-of-Line Delay, in a network that employs multiple 802.11ax features such as OFDMA and the MU Enhanced Distributed Channel Access (EDCA) Parameter Set, while also containing legacy devices. The results are obtained via both the newly re-designed ns-3 wifi module and an original analytical framework, based on the well-established Bianchi 802.11 model.

The bibliometrics assessment of opportunistic network protocols & simulation tools

Opportunistic Network (Oppnets) is one of the influencing network of wireless ad-hoc Network in today's era of research. Massive publications have already existed in Oppnets, which considers diverse techniques of Opportunistic Routing (OR) to provide an adequate selection of relay nodes. This article contributes to the bibliometric evaluation of the routing protocols and study of network simulation tools. Bibliometric analysis is the statistical analysis of published articles by researchers at particular time and place, with the relation among these articles. The data were pre-processed and analyzed with the VOS viewer and Bibliometrics, the professional mapping tool. 16 years of publication (from 2006 to 2022) were included in this evaluation process. Firstly, persuasive citation evaluation of countries and articles were addressed. Secondly, contribution assessment of documents according to countries and researchers. Thirdly, most effective sources, keywords, and trending topics with co-occurrence of bibliographic coupling were encountered. At last, 23 wireless-tools were analysed and also compared. These tools were also discussed with their unique features, leverages, and detriments. The primary objective is to give researchers enormous knowledge and help them elaborate their work in OR, and select appropriate tools to simulate their work. From this study; Readers, Researchers, and Authors can find easily the most reliable “researcher”, “document”, and “source of journal”. The author can find what work has been already published and illuminates their research work with more outcome.

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.

Simulating Network Paths with Recurrent Buffering Units

Simulating physical network paths (e.g., Internet) is a cornerstone research problem in the emerging sub-field of AI-for-networking. We seek a model that generates end-to-end packet delay values in response to the time-varying load offered by a sender, which is typically a function of the previously output delays. The problem setting is unique, and renders the state-of-the-art text and time-series generative models inapplicable or ineffective. We formulate an ML problem at the intersection of dynamical systems, sequential decision making, and time-series modeling. We propose a novel grey-box approach to network simulation that embeds the semantics of physical network path in a new RNN-style model called Recurrent Buffering Unit, providing the interpretability of standard network simulator tools, the power of neural models, the efficiency of SGD-based techniques for learning, and yielding promising results on synthetic and real-world network traces.

Quad-Interleaving Routing Algorithm for Performance Enhancement of Oil and Gas Pipeline Network Monitoring

The pipeline is one of the main modes oftransportation for the oil and gas industry that could be laid offshore and onshore. Pipeline transportation requires close monitoring as a failure in pipelines causes disruption of supply, loss of precious commodities and not to mention irreversible environmental damage will be an essential topic. The major issue in AODV and DSDV is when the network gets more extensive, which causes high packet traffic resulting in high packet loss, energy consumption and passive nodes. This research aims to establish a routing algorithm with better performance focusing on throughput, fairness index, delivery ratio and passive nodes and energy consumption. The study is confined to wireless sensor network simulation on Network Simulator Tool. A technique of quad-interleaving Ad-hoc On- demand Distance Vector (QAODV) routing algorithm has been developed where predefined Alpha, Beta, Charlie and Delta nodes will communicate with the nodes which belong to their family. The results of the developed routing protocol have been compared with standard AODV, DSDV and OLSR routing protocols. The developed routing algorithm has produced a massive improvement in the delivery ratio (25% more), throughput (44% more), passive nodes (71 less), fairness index (0.06 more) and energy consumption (0.22J less).