Simulating Opportunistic Networks with OMNeT++

Udugama, Asanga; Förster, Anna; Dede, Jens; Kuppusamy, Vishnupriya

doi:10.1007/978-3-030-12842-5_14

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…24 To simulate the Epidemic protocol with TRAILS, traces, and SWIM, we used the OPS framework alongside the discrete simulator OMNeT++. 27…”

Section: Resultsmentioning

confidence: 99%

“…To evaluate OppNet protocols, we can use simulators such as ONE, that is an integrated solution that can simulate delay-tolerant wireless protocols with synthetic and trace-based mobility models; 25 Adyton, that simulates OppNets only with trace-based mobility models; 26 and Opportunistic protocol simulator (OPS), 27 that is an extension of the discrete event simulator OMNeT++. 28 OPS can simulate OppNet’s protocols with synthetic and trace-based mobility models.…”

Section: Related Workmentioning

confidence: 99%

“…24 To simulate the Epidemic protocol with TRAILS, traces, and SWIM, we used the OPS framework alongside the discrete simulator OMNeT++. 27 In Figure 24, we present the delivery ratio, and in Figure 25, we describe the mean end-to-end delay of each simulation. 22 Figures 24 and 25 reveal that the medians of the endto-end delay and the delivery ratio of TRAILS simulations are more similar to traces than SWIM simulations.…”

Section: Network Communicationmentioning

confidence: 99%

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TRAILS mobility model

Sarmiento

Förster

2022

SIMULATION

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According to state-of-the-art research, mobile network simulation is preferred over real testbeds, especially to evaluate communication protocols used in Opportunistic Networks (OppNet) or Mobile Ad hoc NETworks (MANET). The main reason behind it is the difficulty of performing experiments in real scenarios. However, in a simulation, a mobility model is required to define users’ mobility patterns. Trace-based models can be used for this purpose, but they are difficult to obtain, and they are not flexible or scalable. Another option is TRAce-based ProbabILiStic (TRAILS). TRAILS mimics the spatial dependency, geographic restrictions, and temporal dependency from real scenarios. In addition, with TRAILS, it is possible to scale the number of mobile users and simulation time. In this paper, we dive into the algorithms used by TRAILS to generate mobility graphs from real scenarios and simulate human mobility. In addition, we compare mobility metrics of TRAILS simulations, real traces, and another synthetic mobility model such as Small Worlds in Motion (SWIM). Finally, we analyze the performance of an implementation of the TRAILS model in computation time and memory consumption. We observed that TRAILS simulations represent the interaction among users of real scenarios with higher accuracy than SWIM simulations. Furthermore, we found that a simulation with TRAILS requires less computation time than a simulation with real traces and that a TRAILS graph consumes less memory than traces.

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“…24 To simulate the Epidemic protocol with TRAILS, traces, and SWIM, we used the OPS framework alongside the discrete simulator OMNeT++. 27…”

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Network Communicationmentioning

confidence: 99%

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TRAILS mobility model

Sarmiento

Förster

2022

SIMULATION

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“…Adyton [51] (i) C++-based event-driven simulator (ii) Supports numerous routing protocols and real-world contact traces (iii) Also provides several congestion control mechanisms and buffer management policies MobEmu [52] (i) Java-based free simulator (ii) Capable of executing a mobility model or replay a trace, while applying the desired routing or dissemination algorithm Ns-3 [53] (i) Python-based free simulator under the GNU GPLv2 license (ii) Also supports a real-time scheduler that facilitates several "simulation-in-the-loop" use cases for interacting with real systems OMNet++ [54,55] (i) Free only for noncommercial organizations (ii) An extensible, component-based C++ simulation framework (iii) Runs basically on all platforms where a modern C++ compiler is available ONE [56] (i) Java-based free simulator (ii) Offers both keyboard and GUI interface for coding (iii) It allows researchers to design new protocols/architecture/framework in a very easy and defined way 10 Mobile Information Systems participant nodes would be at most 1 or even less than 1 per square km. e ratio of the number of nodes per square km becomes 100-150 when a normal highway situation is considered.…”

Section: Simulator Brief Descriptionmentioning

confidence: 99%

Impact of Node Density on the QoS Parameters of Routing Protocols in Opportunistic Networks for Smart Spaces

Garg

Dixit

Sethi

et al. 2020

Mobile Information Systems

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The need and importance of Smart Spaces have been potentially realized by the researchers due to its applicability in the current lifestyle. Opportunistic network, a successor of mobile ad hoc networks and a budding technology of network, is a best-suited technology for implementing Smart Spaces due to its wide range of applications in real-life scenarios ranging from building smart cities to interplanetary communication. There are numerous routing protocols which are available in opportunistic network, each having their pros and cons; however, no research till the time of listing has been done which can quantitatively demonstrate the maximum performance of these protocols and standardize the comparison of opportunistic routing protocols which has been a major cause of ambiguous performance evaluation studies. The work here presents a categorical view of the opportunistic routing protocol family and thereby compares and contrasts the various simulators suited for their simulation. Thereafter, the most popular protocols (selecting at least one protocol from each category) are compared based on node density on as many as 8 standard performance metrics using ONE simulator to observe their scalability, realism, and comparability. The work concludes by presenting the merits and demerits of each of the protocols discussed as well as specifying the best routing protocol among all the available protocols for Smart Spaces with maximum output. It is believed that the results achieved by the implemented methodology will help future researchers to choose appropriate routing protocol to delve into their research under different scenarios.

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“…To enable research, we have built a framework of models to simulate Opp-Nets in OMNeT++. This model framework, called the Opportunistic Networking Simulation (OPS) framework consist of a set of applications and protocols used to simulate nodes of the IoT [12].…”

Section: Introductionmentioning

confidence: 99%

Simulation Speedup in OMNeT++ Using Contact Traces

Karunathilake

Udugama

Förster

EPiC Series in Computing

Self Cite

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The IoT influence is growing rapidly and it is expected that in the near future the number of connected IoT nodes will be in billions. Mobility of nodes is a key aspect in the IoT and network simulations are used to evaluate the performance of IoT networks with mobility. The increasing number of nodes will affect simulation environments by in- creasing the number of nodes per simulation and these simulations will require a long time to complete. So, techniques to reduce the time consumed in simulations are important. A possible technique is the use of contact traces when simulating mobility. A contact trace differs from a real trace or a synthetic mobility model in that, it deals with contact information instead of coordinate information. In this work, we have compared the per- formance of contact traces against coordinate based real traces and mobility models. For our evaluations we use the OPS framework of models built in OMNeT++. In the contact based approach, the use of real traces or mobility models will be removed and instead, nodes will use contact traces to obtain information about connections with other nodes. But this approach requires an additional step, before a simulation, to generate the contact traces. Even with this step, we show that using contact traces is advantageous in terms of simulation durations. When the scale of simulations increase (i.e., in terms of number of nodes, simulated times, etc.), the results presented show that the advantage increases considerably.

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Simulating Opportunistic Networks with OMNeT++

Cited by 10 publications

References 20 publications

TRAILS mobility model

TRAILS mobility model

Impact of Node Density on the QoS Parameters of Routing Protocols in Opportunistic Networks for Smart Spaces

Simulation Speedup in OMNeT++ Using Contact Traces

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