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

• Published in: IEEE transactions on vehicular technology Vol. 66; no. 7; pp. 6336 - 6348
• Main Authors: Celimuge Wu, Yoshinaga, Tsutomu, Yusheng Ji, Murase, Tutomu, Yan Zhang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-07-2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Carrier mobility; Cloud computing; Computer simulation; Current carriers; Data acquisition; Data storage; Data storage scheme; Fuzzy logic; Intelligent transportation systems; Machine learning; Memory; Mobile ad hoc networks; reinforcement learning; Routing; Routing protocols; Throughput; Vehicles; vehicular ad hoc networks (VANETs)
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2016.2643665

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.