Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks (2011.08449v2)

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.

• The paper proposes a novel framework using deep reinforcement learning (DRL) and permissioned blockchain to optimize content caching in vehicular edge computing (VEC) networks.
• It integrates a permissioned blockchain with a novel Proof-of-Utility (PoU) consensus mechanism to enhance data privacy and security for content sharing in VEC.
• Numerical results using real-world data show the DRL-inspired caching scheme outperforms baselines by reducing latency and improving cache hit rates.

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

The paper "Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks" presents a novel framework that leverages deep reinforcement learning (DRL) alongside permissioned blockchain technology to enhance content caching strategies in vehicular edge computing environments. This approach is particularly contextualized within Vehicular Edge Computing (VEC) networks, which are characterized by high mobility and dynamic wireless conditions, posing challenges to establishing optimal caching strategies that ensure low latency requirements.

Key Contributions

The authors propose integrating DRL into vehicular edge networks to optimize content caching policies considering vehicular dynamics. The DRL framework is utilized to discern environmental factors such as available caching resources and dynamic wireless channels, facilitating proactive cache management decisions. This is particularly beneficial in scenarios where vehicles experience rapid topology changes due to mobility.

Moreover, the paper introduces a permissioned blockchain strategy, which seeks to alleviate concerns relating to data privacy and security—critical aspects when vehicles are required to store potentially sensitive personal information. The paper details a blockchain-enabled distributed framework wherein base stations manage blockchain networks to maintain secure transactions for content caching purposes. The authors argue that permissioned blockchains are suited to VEC due to their lower energy and computational requirements compared to public blockchains.

Furthermore, the introduction of a novel block verification approach, termed Proof-of-Utility (PoU), marks a distinctive contribution. PoU prioritizes verification efficiency and security, indicating a practical insight into optimizing consensus mechanisms for vehicular networks. This methodology underlines vehicular capacities and requirements, enhancing the feasibility of blockchain technology in real-time vehicular environments.

Numerical Results & Implications

The authors employ a real-world dataset from Uber to validate the proposed DRL-inspired caching scheme. The results substantially demonstrate the outperformance of the DRL-inspired caching strategy against baseline methods, namely greedy and random caching policies. The framework not only reduces latency but improves cache hit rates—a metric critical to VEC application scenarios.

The paper concludes by emphasizing the practical implications of DRL and blockchain integration in VEC, outlining that such a fusion enables more efficient and secure vehicular data sharing. This is particularly advantageous for scenarios demanding rapid data access and stringent privacy safeguards, such as autonomous driving and infotainment systems in connected vehicles.

Future Directions

The authors speculate on future advances that could emerge from their research, including:

• Enhanced DRL algorithms capable of predicting more detailed mobility patterns and cache requirements in vehicular networks.
• Further exploration of alternative blockchain consensus mechanisms specifically tailored for resource-constrained vehicular environments.
• Development of more comprehensive incentive mechanisms encouraging vehicle engagement in decentralized networks.

The integration of AI-driven approaches like DRL and blockchain into vehicular networks marks a significant stride toward realizing intelligent, secure, and autonomous vehicular systems. As vehicular technology continues to progress, the insights garnered from this paper may inform the next generation of data-centric vehicular network applications.