Incentivizing Consensus Propagation in Proof-of-Stake Based Consortium Blockchain Networks
By Jiawen Kang, Zehui Xiong, Dusist Niyato, Ping Wang, Dongdong Ye, Dong In Kim. 2019
88 citations recorded by the source corpus.
In proof-of-stake based consortium blockchain networks, pre-selected miners compete to solve a crypto-puzzle with a successfully mining probability proportional to the amount of their stakes. When the puzzle is solved, the miners are encouraged to take part in mined block propagation for verification to win a transaction fee from the blockchain user. The mined block should be propagated over wired or wireless networks, and be verified as quickly as possible to decrease consensus propagation delay. In this letter, we study incentivizing the consensus propagation considering the tradeoff between the network delay of block propagation process and offered transaction fee from the blockchain user. A Stackelberg game is then formulated to jointly maximize utility of the blockchain user and individual profit of the miners. The blockchain user acting as the leader sets the transaction fee for block verification. The miners acting as the followers decide on the number of recruited verifiers over wired or wireless networks. We apply the backward induction to analyze the existence and uniqueness of the Stackelberg equilibrium. Performance evaluation validates the feasibility and efficiency of the proposed game model in consensus propagation.
Subfields: Artificial Intelligence, Information Systems
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Citations in the corpus
- Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach (2019, methodology): “For further work, we will consider using blockchains to ensure reliability of local model updates when formulating the incentive mechanism for reliable federated learning in mobile networks [17], [18], [19], [20].”
- Toward Secure Blockchain-Enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory (2018, background): “In the block verification stage, the more verifiers result in a more secure blockchain network [21].”
- Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory (2019, background): “In the blockchain layer, the edge nodes are easy to be connected by the mobile devices and the federated learning task publishers, thus working as the preselected miners of consortium blockchains because of having sufficient computation and storage resources [9], [11], [21].”