An overview of the OMNeT++ simulation environment

By András Varga, Rudolf Hornig. International ICST Conference on Simulation Tools and Techniques, 2008

1,981 citations recorded by the source corpus.

The OMNeT++ discrete event simulation environment has been publicly available since 1997. It has been created with the simulation of communication networks, multiprocessors and other distributed systems in mind as application area, but instead of building a specialized simulator, OMNeT++ was designed to be as general as possible. Since then, the idea has proven to work, and OMNeT++ has been used in numerous domains from queuing network simulations to wireless and ad-hoc network simulations, from business process simulation to peer-to-peer network, optical switch and storage area network simulations. This paper presents an overview of the OMNeT++ framework, recent challenges brought about by the growing amount and complexity of third party simulation models, and the solutions we introduce in the next major revision of the simulation framework.

View Paper: https://www.semanticscholar.org/paper/012e174c148901bedf28ae161518f3fa5c2eee4c

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Citations in the corpus

Sionna: An Open-Source Library for Next-Generation Physical Layer Research (2022, background): “There is also a growing body of well-maintained OSS projects serving different purposes, such as channel simulation [20], [21], link-level simulations [22]–[24], system-level simulations [25], network simulations [26], [27], and hardware experimentation [28], [29].”
Mobile fog: a programming model for large-scale applications on the internet of things (2013, methodology): “EVALUATION To show the bene.ts of Mobile Fog over a cloud based approach, in terms of communication cost, we conducted a network simulation using OMNeT++ [8] with realistic traf .c patterns generated by SUMO [1].”
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey (2018, methodology): “To test the performance, they used the OMNeT++ discrete event simulator [76], [77] to collect transmission delay with given traffic and routing parameters [78].”
A compilation of UAV applications for precision agriculture (2020, unknown): “The proposed algorithm was implemented on the OMNeT++ [118,119] software and speciﬁcally on the Mixim framework [120] .”
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, methodology): “The routing solution is then evaluated by OMNet+ discrete event simulator [143].”
Optimizing smart energy control strategies for plug-in hybrid electric vehicle charging (2010, methodology): “To evaluate the PHEV charging control strategies, we developed a simulator framework based on OMNeT++ [5], where we model both the electrical distribution network and communication network in detail.”
A Performance Comparison of Recent Network Simulators (2009, methodology): “The main contribution of this paper is a performance comparison study incorporating five different open source simulation tools, namely ns2 [13], ns-3 [6], OMNeT++ [19], JiST [3] and SimPy [11].”
A Survey on Recent Advances in Vehicular Network Security, Trust, and Privacy (2019, methodology): “Various network simulators such as NS-2 [112], NS-3 [113], GlomoSim [114] and OMNeT++ [115] have been used to evaluate the performance and security of routing protocols in VANETs.”