Predicting journeys for DTN routing in a public transportation system

Segundo, Fabio Rafael; Silva, Eraldo Silveira e; Farines, Jean-Marie

doi:10.1109/wimob.2014.6962216

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Works on using mobility characteristics of mobile nodes for DTN have been carried out significantly [25][26][27][28][29]. In [25], mobility traces taken from UMass DieselNet, which consist of buses with WiFi interfaces that travel their routes, are used to forward messages, and the performance of the DieselNet routing is analyzed.…”

Section: Introductionmentioning

confidence: 99%

“…Then, large scale mobility traces are produced using micro-mobility simulator and the performance of various DTN routing protocols are compared with the proposed algorithm. In [27], the authors propose a journey predictor for DTN based on mobility traces of public transportation system, where the journey predictor is based on a graph of predicted journeys and the best journey is selected to a specific destination. Artificial neural networks are used to predict a journey.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Opportunistic Routing Protocol Based on Context Information of Mobile Nodes

2018

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Featured Application: The proposed routing protocol can be applied to enable communication in an extreme environment, where network infrastructure is not available.Abstract: Delay tolerant network (DTN) protocol was proposed for a network where connectivity is not available. In DTN, a message is delivered to a destination node via store-carry-forward approach while using opportunistic contacts. Probabilistic routing protocol for intermittently connected networks (PRoPHET) is one of the widely studied DTN protocols. In PRoPHET, a message is forwarded to a contact node, if the contact node has a higher delivery predictability to the destination node of the message. In this paper, we propose an improved opportunistic routing protocol, where context information of average distance travelled and average time elapsed from the reception of a message to the delivery of the message to the destination node is used. In the proposed protocol, the average distance and average time are updated whenever a message is delivered to a destination node. Then, both average distance and average time as well as delivery predictability of PRoPHET protocol are used to decide a message forwarding. The performance of the proposed protocol is analyzed and compared with that of PRoPHET and reachable probability centrality (RPC) protocol, which is one of the latest protocols using the contact history information of a mobile node. Simulation results show that the proposed protocol has better performance than both PRoPHET and RPC, from the aspect of delivery ratio, overhead ratio, and delivery latency for varying buffer size, message generation interval, and the number of nodes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Opportunistic Routing Protocol Based on Context Information of Mobile Nodes

2018

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“…To select efficient relay nodes, several research groups have proposed prediction routing models like PRoPHET (Probabilistic Routing Protocol using History of Encounters and Transitivity) and PER (Predict and Relay). [3][4][5][6][7][8][9] The existing prediction models require additional information such as a node's schedule and delivery predictability. However, network reliability is lowered when additional information is unknown.…”

Section: Introductionmentioning

confidence: 99%

A dynamic Markov chain prediction model for delay-tolerant networks

Jeon

Lee

2016

International Journal of Distributed Sensor Networks

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In this study, prediction routing algorithms are proposed to select efficient relay nodes. While most prediction algorithms assume that nodes need additional information such as node's schedule and connectivity between nodes, the network reliability is lowered when additional information is unknown. To solve this problem, this study proposes a context-aware Markov chain prediction based on the Markov chain that uses the node's movement history information without requiring additional information. The evaluation results show that the proposed scheme has competitive delivery ratio but with less average latency.Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License

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