Simulating Mobility and DTNs with the ONE (Invited Paper)

Keränen, Ari; Kärkkäinen, Teemu; Ott, Jörg

doi:10.4304/jcm.5.2.92-105

Cited by 129 publications

(62 citation statements)

References 27 publications

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“…The simulation is carried out through the Opportunistic Network Environment (ONE) Simulator [29]. We investigate how good are the metrics incorporated in INRIA the GrAnt protocol (e.g., quality of nodes and pheromone evaporation), how GrAnt performs as a forwarding protocol, and how its performance compares with other protocols.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The community-based scenario is divided into five communities or Points of Interest (PoIs) that simulates a group of people in their community that will eventually meet each other and will exchange data. It uses the "Shortest Path Map-Based Movement" movement model [29] that employs Dijkstra's algorithm for finding the shortest path between two random PoIs. There are four groups of nodes, each one with different destination selection probabilities and with thirty nodes that are placed in a random PoI.…”

Section: Mobility Scenariosmentioning

confidence: 99%

“…Working Day Movement Model. The Working Day (WD) movement model [29] [30] represents an activity-based scenario that simulates the daily lives of people who go to work in the morning, spend the day working, at the end of the day may go to a public place for leisure activities with friends and at night go to their houses. The scenario is divided into meeting points, bus, houses, offices, and roads.…”

Section: Mobility Scenariosmentioning

confidence: 99%

See 2 more Smart Citations

GrAnt: Inferring best forwarders from complex networks’ dynamics through a greedy Ant Colony Optimization

et al. 2012

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This paper presents a new prediction-based forwarding protocol for the complex and dynamic Delay Tolerant Networks (DTN). The proposed protocol is called GrAnt (Greedy Ant) as it uses a greedy transition rule for the Ant Colony Optimization (ACO) metaheuristic to select the most promising forwarder nodes or to provide the exploitation of good paths previously found. The main motivation for the use of ACO is to take advantage of its population-based search and of the rapid adaptation of its learning framework. Considering data from heuristic functions and pheromone concentration, the GrAnt protocol includes three modules: routing, scheduling, and buffer management. To the best of our knowledge, this is the first unicast protocol that employs a greedy ACO which: (1) infers best promising forwarders from nodes' social connectivity, (2) determines the best paths to be followed to a message reach its destination, while limiting the message replications and droppings, (3) performs message transmission scheduling and buffer space management. GrAnt is compared to Epidemic and PROPHET protocols in two different scenarios: a working day and a community mobility model. Simulation results obtained by ONE simulator show that in both environments, GrAnt achieves higher delivery ratio, lower messages redundancy, and fewer dropped messages than Epidemic and PROPHET.

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Section: Performance Evaluationmentioning

confidence: 99%

Section: Mobility Scenariosmentioning

confidence: 99%

Section: Mobility Scenariosmentioning

confidence: 99%

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GrAnt: Inferring best forwarders from complex networks’ dynamics through a greedy Ant Colony Optimization

et al. 2012

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“…Simulation studies were conducted using the VDTNsim tool [22]. This tool is an extension of the ONE simulator [23,24] and allows simulating the VDTN architectural approach, which comprehends the store-carry-and-forward overlay network below the network layer. This section is divided into three subsections.…”

Section: Simulation Setupmentioning

confidence: 99%

Performance evaluation of a cooperative reputation system for vehicular delay-tolerant networks

Dias

Rodrigues

Shu

et al. 2014

J Wireless Com Network

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In the last decade, both scientific community and automotive industry enabled communications among vehicles in different kinds of scenarios proposing different vehicular architectures. Vehicular delay-tolerant networks (VDTNs) were proposed as a solution to overcome some of the issues found in other vehicular architectures, namely, in dispersed regions and emergency scenarios. Most of these issues arise from the unique characteristics of vehicular networks. Contrary to delay-tolerant networks (DTNs), VDTNs place the bundle layer under the network layer in order to simplify the layered architecture and enable communications in sparse regions characterized by long propagation delays, high error rates, and short contact durations. However, such characteristics turn contacts very important in order to exchange as much information as possible between nodes at every contact opportunity. One way to accomplish this goal is to enforce cooperation between network nodes. To promote cooperation among nodes, it is important that nodes share their own resources to deliver messages from others. This can be a very difficult task, if selfish nodes affect the performance of cooperative nodes. This paper studies the performance of a cooperative reputation system that detects, identify, and avoid communications with selfish nodes. Two scenarios were considered across all the experiments enforcing three different routing protocols (First Contact, Spray and Wait, and GeoSpray). For both scenarios, it was shown that reputation mechanisms that punish aggressively selfish nodes contribute to increase the overall network performance.

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“…The Opportunistic Networking Environment (ONE) simulator [34][35] is a Java-based simulator specifically designed for evaluating DTN routing and application protocols. The authors demonstrate the tremendous capabilities supported by the simulator critical to implementing various scenarios in DTN, including mobility and event generation, message exchange, a basic notion of energy consumption, visualization and analysis, interfaces for importing and exporting mobility traces, events and entire messages.…”

Section: E Implementations and Simulatorsmentioning

confidence: 99%

From Delay-Tolerant Networks to Vehicular Delay-Tolerant Networks

Pereira

Casaca

Rodrigues

et al. 2012

IEEE Commun. Surv. Tutorials

208

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Abstract-This paper provides an introductory overview of Vehicular Delay-Tolerant Networks. First, an introduction to Delay-Tolerant Networks and Vehicular Delay-Tolerant Networks is given. Delay-Tolerant schemes and protocols can help in situations where network connectivity is sparse or with large variations in density, or even when there is no end-to-end connectivity by providing a communications solution for non realtime applications. Some special issues like routing are addressed in the paper and an introductory description of applications and the most important projects is given. Finally, some research challenges are discussed and conclusions are detailed.

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Simulating Mobility and DTNs with the ONE (Invited Paper)

Cited by 129 publications

References 27 publications

GrAnt: Inferring best forwarders from complex networks’ dynamics through a greedy Ant Colony Optimization

GrAnt: Inferring best forwarders from complex networks’ dynamics through a greedy Ant Colony Optimization

Performance evaluation of a cooperative reputation system for vehicular delay-tolerant networks

From Delay-Tolerant Networks to Vehicular Delay-Tolerant Networks

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