A Deep Learning-based Algorithm for the Routing Problem in Vehicular Delay-Tolerant Networks

Abstract

The exponential growth of cities across the world has brought along important challenges such as waste management, pollution and overpopulation, and transportation administration. To mitigate these problems, the idea of 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. Intelligent Transportation Systems are intelligent systems that aim at providing the best solution to transportation-related matters, with the aid of information technologies, electrical and electronics and communication networks. In this context, communication networks are called Vehicular Networks, and they offer a communication framework for moving vehicles, road infrastructure and pedestrians. The extreme conditions of vehicular environments, nonetheless, make communication between high-speed moving nodes very difficult, so non-deterministic approaches are necessary to maximize the chances of packet delivery. In this work, this problem is addressed using Artificial Intelligence from a hybrid perspective, focusing on both the best next message to replicate and the best next hop in its path in the network. Furthermore, DLR+ is proposed, 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.