Paper Info
Title
Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks
Abstract
Vehicular Edge Computing (VEC) is a promising paradigm to enable huge amount of data and multimedia content to be cached in proximity to vehicles. However, high mobility of vehicles and dynamic wireless channel condition make it challenge to design an optimal content caching policy. Further, with much sensitive personal information, vehicles may be not willing to caching their contents to an untrusted caching provider. Deep Reinforcement Learning (DRL) is an emerging technique to solve the problem with high-dimensional and time-varying features. Permission blockchain is able to establish a secure and decentralized peer-to-peer transaction environment. In this paper, we integrate DRL and permissioned blockchain into vehicular networks for intelligent and secure content caching. We first propose a blockchain empowered distributed content caching framework where vehicles perform content caching and base stations maintain the permissioned blockchain. Then, we exploit the advanced DRL approach to design an optimal content caching scheme with taking mobility into account. Finally, we propose a new block verifier selection method, Proof-of-Utility (PoU), to accelerate block verification process. Security analysis shows that our proposed blockchain empowered content caching can achieve security and privacy protection. Numerical results based on a real dataset from Uber indicate that the DRL-inspired content caching scheme significantly outperforms two benchmark policies.
Year
DOI
Venue
2020
10.1109/TVT.2020.2973705
IEEE Transactions on Vehicular Technology
Keywords
DocType
Volume
Edge computing,Servers,Vehicle dynamics,Wireless communication,Base stations
Journal
69
Issue
ISSN
Citations 
4
0018-9545
13
PageRank 
References 
Authors
0.46
0
5
Name
Order
Citations
PageRank
Yueyue Dai12127.69
Xu Du23715.92
Ke Zhang354032.46
Sabita Maharjan4107852.89
Yan Zhang55818354.13