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Open AccessArticle

Modeling and Solution of the Routing Problem in Vehicular Delay-Tolerant Networks: A Dual, Deep Learning Perspective

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Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Av. Lago de Guadalupe KM 3.5, Estado de México 52926, Mexico
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Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Av. General Ramon Corona 2514, Jalisco 45138, Mexico
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Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Av. Eugenio Garza Sada 2501, Nuevo León 64849, Mexico
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Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(23), 5254; https://doi.org/10.3390/app9235254
Received: 7 November 2019 / Revised: 26 November 2019 / Accepted: 26 November 2019 / Published: 3 December 2019
The exponential growth of cities has brought important challenges such as waste management, pollution and overpopulation, and the administration of transportation. To mitigate these problems, the idea of the smart city was born, seeking to provide robust solutions integrating sensors and electronics, information technologies, and communication networks. More particularly, to face transportation challenges, intelligent transportation systems are a vital component in this quest, helped by vehicular communication networks, which offer a communication framework for vehicles, road infrastructure, and pedestrians. The extreme conditions of vehicular environments, nonetheless, make communication between nodes that may be moving at very high speeds very difficult to achieve, so non-deterministic approaches are necessary to maximize the chances of packet delivery. In this paper, we address this problem using artificial intelligence from a hybrid perspective, focusing on both the best next message to replicate and the best next hop in its path. Furthermore, we propose a deep learning–based router (DLR+), a router with a prioritized type of message scheduler and a routing algorithm based on deep learning. Simulations done to assess the router performance show important gains in terms of network overhead and hop count, while maintaining an acceptable packet delivery ratio and delivery delays, with respect to other popular routing protocols in vehicular networks. View Full-Text
Keywords: vehicular networks; VDTN; routing; message scheduling; deep learning vehicular networks; VDTN; routing; message scheduling; deep learning
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Hernández-Jiménez, R.; Cardenas, C.; Muñoz Rodríguez, D. Modeling and Solution of the Routing Problem in Vehicular Delay-Tolerant Networks: A Dual, Deep Learning Perspective. Appl. Sci. 2019, 9, 5254.

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