IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

OMNeT++ Framework for Simulation of Centralized and Distributed Algorithms in Multi-Hop Networks

OMNeT++ Framework for Simulation of Centralized and Distributed Algorithms in Multi-Hop Networks
View Sample PDF
Author(s): Sercan Demirci (Ondokuz Mayıs University, Turkey), Serhat Celil Ileri (Ondokuz Mayıs University, Turkey)and Sadat Duraki (Ondokuz Mayıs University, Turkey)
Copyright: 2022
Pages: 33
Source title: Handbook of Research on Advances in Data Analytics and Complex Communication Networks
Source Author(s)/Editor(s): P. Venkata Krishna (Sri Padmavati Mahila University, India)
DOI: 10.4018/978-1-7998-7685-4.ch001

Purchase

View OMNeT++ Framework for Simulation of Centralized and Distributed Algorithms in Multi-Hop Networks on the publisher's website for pricing and purchasing information.

Abstract

Theoretical applications and practical network algorithms are not very cost-effective, and most of the algorithms in the commercial market are implemented in the cutting-edge devices. Open-source network simulators have gained importance in recent years due to the necessity to implement network algorithms in more realistic scenarios with reasonable costs, especially for educational purposes and scientific researches. Although there have been various simulation tools, NS2 and NS3, OMNeT++ is more suitable to demonstrate network algorithms because it is convenient for the model establishment, modularization, expandability, etc. OMNeT++ network simulator is selected as a testbed in order to verify the correctness of the network algorithms. The study focuses on the algorithms based on centralized and distributed approaches for multi-hop networks in OMNeT++. Two network algorithms, the shortest path algorithm and flooding-based asynchronous spanning tree algorithm, were examined in OMNeT++. The implementation, analysis, and visualization of these algorithms have also been addressed.

Related Content

J. Mangaiyarkkarasi, J. Shanthalakshmi Revathy. © 2024. 34 pages.
Gummadi Surya Prakash, W. Chandra, Shilpa Mehta, Rupesh Kumar. © 2024. 22 pages.
Duygu Nazan Gençoğlan. © 2024. 35 pages.
Smrity Dwivedi. © 2024. 20 pages.
Pallavi Sapkale, Shilpa Mehta. © 2024. 21 pages.
Pardhu Thottempudi, Vijay Kumar. © 2024. 43 pages.
Sathish Kumar Danasegaran, Elizabeth Caroline Britto, S. Dhanasekaran, G. Rajalakshmi, S. Lalithakumari, A. Sivasangari, G. Sathish Kumar. © 2024. 18 pages.
Body Bottom