A novel game theory based reliable proof-of-stake consensus mechanism for blockchain

The prominent achievement of blockchain technology stimulates exceptional innovation. The major component of blockchain is the consensus mechanism. The standard consensus mechanisms specifically Proof-of-Work (PoW) rely on mining procedures and stake-based mechanisms such as Proof-of-Stake (PoS) rely on massive stake investment as the sole criteria for selection of leader nodes. However, PoW impose huge computational power requirements and latter may incorporate malicious nodes as leader nodes in anonymous blockchain. These issues might fuel the way for distrust among the participants in blockchain. Henceforth, a novel game theory based reliable PoS mechanism for blockchain has been proposed. Federated learning has been used to compute trust_score for each node. The nodes are trained on locally generated dataset. Further, a game theoretic approach has been proposed that uses a reward and punishment scheme to ensure threshold level of trust_score maintenance by each node. Finally, a crop insurance use case has been developed with the consensus mechanism and blockchain coded in python. The insurance claims are made to operate through smart contract based mobile app system to impart more authenticity. The system is tested and results show an intrusion accomplishment rate reduced by approximate 40% when compared to the standard PoS mechanism and by approximately 33% for algorand, 29% for ouroboros and 20% for tendermint. The mean absolute error also decreases by 30% within specific time. Furthermore, the proposed federated learning-based system is compared with basic neural network-based machine learning model and the results reveal that a significant reduction in average training time amounting to 8.35 second is achieved. Test accuracy has also been analyzed for various learning mechanisms.