基于多智能体强化学习的区块链赋能车联网中的安全数据共享

针对基于委托权益证明(Delegated Proof-of-Stake, DPoS) 共识算法的区块链赋能车联网系统中区块验证的安全性与可靠性问题, 矿工通过引入轻节点(如智能手机等边缘节点)共同参与区块验证，提高区块验证的安全性和可靠性。为了激励矿工主动引入轻节点, 采用了斯坦伯格(Stackelberg)博弈模型对区块链用户与矿工进行建模, 实现区块链用户的效用和矿工的个人利润最大化。作为博弈主方的区块链用户设定最优的区块验证的交易费, 而作为博弈从方的矿工决定最优的招募验证者(即轻节点)的数量。为了找到所设计Stackelberg博弈的纳什均衡, 设计了一种基于多智能体强化学习算法来搜索接近最优的策略。最后对本文方案进行验证, 结果表明该方案既能实现区块链用户和矿工效益最大化, 也能保证区块验证的安全性与可靠性。

Multi-Agent Reinforcement Learning for Secure Data Sharing in Blockchain-Empowered Vehicular Networks

To achieve secure and reliable block verification, miner nodes of Delegated Proof-of-Stake (DPoS) consensus algorithm can collaborate with nearby light nodes (e.g., smart phones) to verify new block data for secure blockchain-empowered vehicular networks. In order to encourage miners to actively cooperate with light nodes in block verification, a Stackelberg game model is proposed to formulate the interaction between blockchain users and miners, thus jointly maximizing the utility of blockchain users and the profits of miners. The blockchain user acts as the leader setting the optimal transaction fee for block verification, and the miners as the followers determining the optimal number of verifiers to be recruited for block verification. To find out the Nash equilibrium of the game model, a multi-agent reinforcement learning algorithm is designed to search for a strategy close to the optimal one. The numerical results show that the proposed scheme can jointly maximize the benefits of blockchain users and miners and also ensure the safety and reliability of block verification.