# Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks

arXiv: https://arxiv.org/abs/2011.08449 · HTML: https://mlsanity.com/paper/2011.08449/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning](https://mlsanity.com/paper/2208.01219/) (2022)
- [Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT](https://mlsanity.com/paper/2202.03575/) (2022)
- [Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching](https://mlsanity.com/paper/2110.10349/) (2021)
- [Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks](https://mlsanity.com/paper/2207.00755/) (2022)
- [A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networks](https://mlsanity.com/paper/2105.05564/) (2021)

## Abstract (arXiv)

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.