Abstract
VANETs (Vehicular Ad hoc NETworks) are considered among the world’s largest networks. These networks are providing multiple services like infotainment applications, safety services, driver assistance, and even video on demand. On one hand, VANETs are characterized by their random topology and dynamic behavior that varies in urban context, and which highly changes in highways. On the other hand, diffusing information is a fundamental task to deliver multiple services. Thus, the broadcasting task is a challenging problem and need more investigation. In fact, to achieve this task, artificial intelligence and learning based computing seem to be one of the most appropriate options that best fits the dynamic behavior of VANETs. Accordingly, in this paper we propose a novel hybrid relay selection technique to perform the broadcasting task based on a reinforcement learning method. Our proposition is initially to combine an artificial neural network-based classification applied to select forwarding nodes, and in the second phase, we apply the Viterbi algorithm as a reinforcement tool to refine the first classification. To measure the performance of our contribution, we adopt a grid map scenario with varied traffic densities. Afterwards, we analyze and compare the simulation results with other methods in the literature based on different parameters such as the success rate, the data loss, the saved rebroadcasts, and the delay. We conclude by proving that the proposed technique combining deep learning along with reinforcement learning outperforms other recently proposed broadcasting schemes based on the results which show that the new solution increased the success rate by 16%, the saved rebroadcasts by 20%, and reduced the delay by 23%.
Highlights
IntroductionVANETs (vehicular ad hoc networks) enable a wide range of intelligent transportation system services
VANETs enable a wide range of intelligent transportation system services
We introduce a novel hybrid relay selection technique for broadcasting in vehicular networks based on a reinforcement learning method using the Viterbi algorithm, and we apply artificial neural network for the classification of nodes to forward the message
Summary
VANETs (vehicular ad hoc networks) enable a wide range of intelligent transportation system services. Vehicle-to-vehicle and vehicle-to-infrastructure communications are used to provide services varying from road safety to infotainment and traffic management [1]. VANET creates self-organizing networks composed of nodes with dedicated short-range communication (DSRC) implanted in automobiles. Due to their varied movement speeds and bounded covered area, automobiles constitute a mobile ad hoc network along the roads with very dynamic topology [2]. Data delivery is one of the fundamental tasks in VANET applications deployment [4], [5]. In these kinds of networks with nonpredefined infrastructure, every vehicle can send, forward or receive a disseminated packet of data. Designing an efficient broadcasting model is considered as a challenge in order to guarantee a maximum accessibility with optimal performance
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