THE OMNET++ DISCRETE EVENT SIMULATION SYSTEM

By András Varga. 2003

2,533 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0586f39a5280d49e62b49838c229dcb37d105994

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Citations in the corpus

Cross-Level Sensor Network Simulation with COOJA (2006, background): “Other network level simulators used for the simulation of wireless sensor networks include GloMoSim [12] and OMNet++ [10].”
Do LoRa Low-Power Wide-Area Networks Scale? (2016, background): “A vast number of generic wireless simulation tools such as ns-3 [10] or OMNet++ [17] exist.”
RouteNet: Leveraging Graph Neural Networks for Network Modeling and Optimization in SDN (2019, methodology): “Weevaluated the accuracy of our GNN model with a dataset generated using a packet-level simulator (Omnet++ [14]), and this resulted in high estimation accuracy of delay, jitter, and loss when testing it against topologies, routing and trafﬁc not seen during training.”
An adaptive energy-efficient MAC protocol for wireless sensor networks (2003, methodology): “In the OMNeT++ discrete event simulation package [11], we have built a realistic model of the EYES wireless sensor nodes [13].”
An overview of the OMNeT++ simulation environment (2008, background): “OMNeT++[1][2] is a C++-based discrete event simulator for modeling communication networks, multiprocessors and other distributed or parallel systems.”
Distributed localization in wireless sensor networks: a quantitative compariso (2003, unknown): “The underlying OMNeT++ discrete event simulator [17] takes care of the semiconcurrent execution of the specific localization algorithm.”
ns-3 project goals (2006, methodology): “A larger number of open source tools have been developed, including GloMoSim [7], NCTUns [14], GTNets[20], OMNET++ [25], SSFNet [3], and JiST [10].”
The Georgia Tech Network Simulator (2003, methodology): “A similar object oriented approach was used in the OMNet[16, 17] simulator, which is a general purpose simulation environment that can be adapted to create network simulations.”
Distributed algorithms for transmission power control in wireless sensor networks (2003, methodology): “To find the minimum transmission power, the following algorithm is used:”
GeOpps: Geographical Opportunistic Routing for Vehicular Networks (2007, methodology): “To evaluate GeOpps, we used OMNet++[15, 19], a discrete event simulation environment and the mobility framework plug-in [5], which supports node mobility, dynamic connection management and a wireless channel model.”
Exploiting Virtual Coordinates for Improved Routing Performance in Sensor Networks (2011, methodology): “For analyzing the performance of the virtual address-based routing protocols VCP and VRR, we implemented these models in OMNeT++ [37], a discrete event simulator free for academic use.”
AVE: Autonomous Vehicular Edge Computing Framework with ACO-Based Scheduling (2017, methodology): “We simulated IEEE 802.11p DSRC using the Omnet++ [32] simulator, together with Veins [33], a network framework for 802.11p.”
Economic-based Distributed Resource Management and Scheduling for Grid Computing (2002, background): “ude simulation languages (e.g., Simscript [15]), simulation environments (e.g., Parsec [90]), simulation libraries (SimJava [29]), and application specific simulators (e.g., OMNet++ network simulator [5]). While there exists a large body of knowledge and tools, there are very few tools available for”
Lightweight time synchronization for sensor networks (2003, methodology): “Omnet++ provides built-in support for modeling wireless channels and a notion of time for distributed scheduling of events at nodes.”
Bidirectionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis (2011, methodology): “In particular, we employ OMNeT++ [9], a simulation environment free for academic use, to model realistic communication patterns of VANET nodes.”
CAR: Context-Aware Adaptive Routing for Delay-Tolerant Mobile Networks (2009, methodology): “We implemented our simulation scenario using OMNet++ [22] .”
Versatile, scalable, and accurate simulation of distributed applications and platforms (2014, unknown): “OverSim [5] relies on the OMNeT ++ [81] discrete-event simulation kernel for implementing more realistic packet-level network simulation.”
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].”