The objective of the OMNet++ Simulation Training program is to equip participants with the skills and knowledge to effectively use OMNet++ for simulating and analyzing network systems.

The program covers key concepts of network modeling, simulation setup, and performance evaluation for various network types, including wireless, wired, and IoT.

Participants will gain hands-on experience in creating and customizing simulation scenarios, interpreting results, and applying these insights to optimize network designs and operations. By the end of the training, attendees will be proficient in leveraging OMNet++ for academic research and professional projects.

OMNet++ Research Project Support - We provide exclusive research project development support for Masters & PHD scholars. You can discuss your requirements with our experts to get project development support quote.

+91-8826900551

Training Code: NES_SK_286

Duration: 40 Hrs/4 weeks/ Customized

Mode: Corporate (Instructor Led - Online / Offline / Onsite), Individual (Instructor Led - Online)

Module 1 - OMNet++ Programming

• Programming the Algorithms
• The NED Language
• NED Overview
• The Network
• Introducing a Channel
• The App, Routing, and Queue Simple Modules
• The Node Compound Module
• Putting It Together
• Simple Modules
• Compound Modules
• Channels
• Parameters

Module 2 - OMNet++ Programming (Contd)

• Gates
• SubModules
• Connections
• Multiple Connections
• Parametric Submodule and Connection Types
• Metadata Annotations (Properties)
• Inheritance
• Packages

Module 3 - Simple Modules

• Simulation Concepts
• Components, Simple Modules, Channels
• Defining Simple Module Types
• Adding Functionality to cSimpleModule
• Accessing Module Parameters
• Accessing Gates and Connections
• Sending and Receiving Messages
• Channels
• Stopping the Simulation
• Finite State Machines

Module 4 - Simple Modules

• Navigating the Module Hierarchy
• Direct Method Calls Between Modules
• Dynamic Module Creation
• Signals
• Signal-Based Statistics Recording

Module 5 - Messages and Packets

• Overview
• The cMessage Class
• Self-Messages
• Signals
• The cPacket Class
• Attaching Parameters and Objects

Module 6 - Message Definitions

• Introduction
• Messages and Packets
• Classes
• Structs
• Literal C++ Blocks
• Using C++ Types
• Customizing the Generated Class
• Using Standard Container Classes for Fields
• Namespaces
• Descriptor Classes

Module 7 - The Simulation Library

• Fundamentals
• Logging from Modules
• Random Number Generators
• Generating Random Variates
• Container Classes
• Routing Support: cTopology
• Pattern Matching
• Statistics and Distribution Estimation
• Recording Simulation Results
• Watches and Snapshots
• Defining New NED Functions
• Deriving New Classes
• Object Ownership Management

Module 8 - Visualization

• Overview
• Placement of Visualization Code
• Display Strings
• Bubbles
• The Canvas
• 3D Visualization

Module 9 - Building Simulation Programs

• Overview
• Using opp_makemake and Makefiles
• Project Features

Module 10 - Configuring Simulations

• The Configuration File
• Sections
• Assigning Module Parameters
• Parameter Studies
• Configuring the Random Number Generators
• Logging

Module 11 - Running Simulations

• Introduction
• Cmdenv: the Command-Line Interface
• The Tkenv Graphical User Interface
• The Qtenv Graphical User Interface
• Batch Execution
• Akaroa Support: Multiple Replications in Parallel
• Troubleshooting

Module 12 - Result Recording and Analysis

• Result Recording
• Configuring Result Collection
• Overview of the Result File Formats
• The Analysis Tool in the Simulation IDE
• Scave Tool
• Alternative Statistical Analysis and Plotting Tools

Module 13 - Eventlog

• Introduction
• Configuration
• Eventlog Tool

Module 14- Testing

• Overview
• The opp_test Tool
• Smoke Tests
• Fingerprint Tests
• Unit Tests
• Module Tests
• Statistical Tests

Module 15 - Sample Project

• Models (TCP/IP – IP, TPC, UDP, PPP),
• Models (Network Protocols – Ethernet
• 802.11, FDDI, Token Ring)
• Models – Peer to peer networks
• Models – Sensor Networks