Journal of Guangdong University of Technology ›› 2021, Vol. 38 ›› Issue (06): 62-69.doi: 10.12052/gdutxb.210112

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

Li Ming-lei1, Zhang Yang1,2, Kang Jia-wen3, Xu Min-rui4, Dusit Niyato4   

  1. 1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430000, China;
    2. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
    3. School of Automation Guangdong University of Technology, Guangzhou 510006, China;
    4. School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
  • Received:2021-07-12 Online:2021-11-10 Published:2021-11-09

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Abstract: 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.

Key words: block verification, delegated Proof-of-Stake, game theory, multi-agent reinforcement learning

CLC Number: 

  • TP393
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