Deep Reinforcement Learning for Optimal Resource Allocation in Blockchain-based IoV Secure Systems

Abstract

Driven by the advanced technologies of vehicular communications and networking, the Internet of Vehicles (IoV) has become an emerging paradigm in smart world. However, privacy and security are still quite critical issues for the current IoV system because of various sensitive information and the centralized interaction architecture. To address these challenges, a decentralized architecture is proposed to develop a blockchain-supported IoV (BS-IoV) system. In the BS-IoV system, the Roadside Units (RSUs) are redesigned for Mobile Edge Computing (MEC). Except for information collection and communication, the RSUs also need to audit the data uploaded by vehicles, packing data as block transactions to guarantee high-quality data sharing. However, since block generating is critical resource-consuming, the distributed database will cost high computing power. Additionally, due to the dynamical variation environment of traffic system, the computing resource is quite difficult to be allocated. In this paper, to solve the above problems, we propose a Deep Reinforcement Learning (DRL) based algorithm for resource optimization in the BS-IoV system. Specifically, to maximize the satisfaction of the system and users, we formulate a resource optimization problem and exploit the DRL-based algorithm to determine the allocation scheme. The evaluation of the proposed learning scheme is performed in the SUMO with Flow, which is a professional simulation tool for traffic simulation with reinforcement learning functions interfaces. Evaluation results have demonstrated good effectiveness of the proposed scheme.