A Reinforcement Learning-Based Data Storage Scheme for Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) have been attracting interest for their potential roles in intelligent transport systems (ITS). In order to enable distributed ITS, there is a need to maintain some information in the vehicular networks without the support of any infrastructure such as road side units. In this paper, we propose a protocol that can store the data in VANETs by transferring data to a new carrier (vehicle) before the current data carrier is moving out of a specified region. For the next data carrier node selection, the protocol employs fuzzy logic to evaluate instant reward by taking into account multiple metrics, specifically throughput, vehicle velocity, and bandwidth efficiency. In addition, a reinforcement learning-based algorithm is used to consider the future reward of a decision. For the data collection, the protocol uses a cluster-based forwarding approach to improve the efficiency of wireless resource utilization. We use theoretical analysis and computer simulations to evaluate the proposed protocol.