A Novel Deep Reinforcement Learning Based Relay Selection for Broadcasting in Vehicular Ad Hoc Networks

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%.