NS3 Simulator Projects

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NS-3 (Network Simulator) is an open-source, discrete-event network simulation tool. For constructing a prototype simulation in NS-3, which could be adjusted on the basis of your particular project necessities, we provide a common guideline:

Step 1: Install NS-3

Initially, on our system, NS-3 must be installed in an effective manner. The process of assuring this is examined as significant. For the configuration, packages such as Mercurial, Python, and gcc are needed by NS-3. On different Unix-based platforms such as macOS, Linux, it can be utilized. From its official website, we plan to download the advanced version of NS-3. The installation guidelines offered in the documents ought to be adhered to.

Step 2: Explain the Simulation Setting

In this segment, what we intend to simulate has to be explained in an explicit manner, prior to engaging in coding:

  • Network Topology: The physical or consistent structure, kinds and number of nodes such as switches, clients, routers, servers, and their links should be defined.
  • Protocols and Models: The communication protocols like HTTP, TCP, UDP, etc must be determined. For mobility, channel, or traffic generation, it is significant to indicate any particular models that we intend to employ.
  • Performance Metrics: As a means to assess the effectiveness of our simulation like packet loss, throughput, delay, etc., the parameters that we plan to assess ought to be recognized.

Step 3: Create the Simulation Script

Generally, in Python or C++, NS-3 simulations are written. The languages that we are most familiar with have to be selected. By employing C++ for demonstration, a simple summary regarding the aspects that are encompassed in our script is provided here:

  1. Encompass Essential Headers: Essential for our simulation, we focus on involving the NS-3 modules.

#include “ns3/core-module.h”

#include “ns3/network-module.h”

  1. Configure Nodes: In order to depict devices in our network, it is advisable to develop node objects.

ns3::NodeContainer nodes;

nodes.Create(2);

  1. Set Up the Network Devices and Channels: Typically, network interfaces and channels linking our nodes must be configured in an appropriate manner.

ns3::PointToPointHelper pointToPoint;

pointToPoint.SetDeviceAttribute(“DataRate”, StringValue(“5Mbps”));

pointToPoint.SetChannelAttribute(“Delay”, StringValue(“2ms”));

auto devices = pointToPoint.Install(nodes);

  1. Install Protocols and Applications: To our devices, we aim to allocate network protocols. Any traffic generators or applications should be configured.

ns3::InternetStackHelper stack;

stack.Install(nodes);

ns3::UdpEchoServerHelper echoServer(9);

  1. Set Up Simulation Parameters: For our simulation and any other related metrics, focus on configuring the beginning and end times.

ns3::Simulator::Run();

ns3::Simulator::Destroy();

Step 4: Execute the Simulation

Our simulation script has to be compiled and executed. Regarding how to compile our project on the basis of its build system, NS-3 offers elaborate documentation. Generally, we could employ the waf build system encompassed with NS-3 for a C++ script.

Step 5: Examine Outcomes

From basic text files to highly complicated pcap traces or even visualizations with tools such as NetAnim, NS-3 is capable of producing different ways of output. Based on the effectiveness and activities of our network setting, obtain eloquent data by examining these outputs extensively.

Step 6: Iterate and Improve

As a means to effectively attain our research objectives, we ought to prolong the simulation, adapt our setting, or modify metrics on the basis of the outcomes of our preliminary simulations. A crucial segment of the simulation procedure is considered as iteration.

What are the SDN simulators currently available Or any Idea

There exist several SDN simulators, but some are available right now. Together with specific abilities and characteristics, we recommend few of the prominent SDN simulators and emulation tools that are recently accessible:

  1. Mininet
  • Explanation: One of the most prominent SDN simulation tools is examined as Mininet. In order to execute kernel, switch, and application code, it offers a virtual network platform. As a result of its easy utilization and capability to simulate complicated topologies on a single machine, it is extremely acknowledged.
  • Appropriate For: Specifically, for learning objectives, fast modeling of SDN applications, and assessing network topologies, Mininet is highly applicable.
  1. GNS3 (Graphical Network Simulator-3)
  • Explanation: The process of visualizing complicated networks which encompass conventional as well as SDN elements and incorporating with SDN controllers are enabled by GNS3, even though it is not particularly an SDN simulator. As a means to simulate different network settings, users are enabled to incorporate actual and virtual devices appropriately.
  • Appropriate For: Hybrid networks which involve SDN as well as conventional network components are simulated with the aid of this simulator.
  1. EstiNet
  • Explanation: In order to assist SDN simulation, EstiNeT is considered as a viable network simulator and emulator. By enabling extensive exploration and assessment of SDN platforms, it contains the capability to shift among simulation and emulation modes in a dynamic manner.
  • Appropriate For: Thorough performance analysis are required by the study and advancement committees. For those committees, EstiNet is more suitable. A broad scope of network protocols and SDN controllers could be assisted.
  1. ONOS (Open Network Operating System)
  • Explanation: Appropriate for high scalability and effectiveness, ONOS is an openly available SDN controller. As a means to enable the simulation and assessment of SDN applications and network arrangements, effective tools and elements are encompassed in ONOS, even though it is mainly an SDN controller.
  • Appropriate For: Specifically, in high-performance networks, ONOS is extremely ideal for constructing and assessing extensive SDN implementations.
  1. OpenDaylight
  • Explanation: For developing network management and applications, a flexible environment is offered by OpenDaylight which is considered as an openly available, extensively employed SDN controller model. For a diversity of SDN simulation and evaluation requirements, it is extremely applicable, since it assists a broad scope of services and protocols.
  • Appropriate For: To construct and assess network management applications, an adaptable as well as extensive SDN environment is explored by developers.
  1. ns-3 (Network Simulator 3)
  • Explanation: For Internet models, ns-3 is examined as a discrete-event network simulator. In the research committee, it is highly prominent. The SDN platforms once set with the suitable scripts and modules could be simulated by ns-3, even though it is not mainly modelled for SDN.
  • Appropriate For: Extensive designing of networking protocols and activities such as SDN settings are needed by educational study. Generally, ns-3 plays a crucial role in academic research.

To construct a prototype simulation in NS-3 which could be adjusted according to your certain project necessities, we have offered a usual instruction. As well as, numerous popular SDN simulators and emulation tools presently accessible along with its specific abilities and characteristics are recommended by us in this article.

NS3 Simulator Project Topics

NS3 Simulator Project Topics List that can be approachable for students are listed below, we have Doctorates who guide you in solving the complex task. Get your work done along with source code or else if you are struck on any area then we assure you with complete guidance. 

  1. Visual sensor network stimulation model identification via Gaussian mixture model and deep embedded features
  2. Classification And Regression Tree (CART) based resource allocation scheme for Wireless Sensor Networks
  3. Service Attack Improvement in Wireless Sensor Network Based on Machine Learning
  4. UAV-assisted connectivity enhancement algorithms for multiple isolated sensor networks in agricultural Internet of Things
  5. Routing failure prediction and repairing for AUV-assisted underwater acoustic sensor networks in uncertain ocean environments
  6. A Network Observability Framework for Sensor Placement in Flood Control Networks to Improve Flood Situational Awareness and Risk Management
  7. Calibration and field deployment of low-cost sensor network to monitor underground pipeline leakage
  8. Photovoltaic energy generation systems monitoring and performance optimization using wireless sensors network and metaheuristics
  9. An energy-balanced unequal clustering approach for circular wireless sensor networks
  10. Extending lifetime of Wireless Nano-Sensor Networks: An energy efficient distributed routing algorithm for Internet of Nano-Things
  11. Consensus cubature filtering based on Gaussian process for distributed sensor network with model uncertainty
  12. Robust sensor placement for sustainable leakage management in water distribution networks of developing economies: A hybrid decision support framework
  13. An extended evaluation on machine learning techniques for Denial-of-Service detection in Wireless Sensor Networks
  14. Application-specific clustering in wireless sensor networks using combined fuzzy firefly algorithm and random forest
  15. Secure routing with multi-watchdog construction using deep particle convolutional model for IoT based 5G wireless sensor networks
  16. An optimized back propagation neural network for automated evaluation of health condition using sensor data
  17. Aldehyde modified cellulose-based dual stimuli responsive multiple cross-linked network ionic hydrogel toward ionic skin and aquatic environment communication sensors
  18. Dynamic sensor activation and decision-level fusion in wireless acoustic sensor networks for classification of domestic activities
  19. CMML: Combined metaheuristic-machine learning for adaptable routing in clustered wireless sensor networks
  20. An adaptive hierarchical data dissemination mechanism for mobile data collector enabled dynamic wireless sensor network