A Fuzzy Logic-Based Transaction Traffic Control Scheme with Enhanced Kalman Filter to Improve EHR Blockchain Performance

Using a blockchain instead of traditional databases or protocols is a desired choice that compromises impractical requirements, including performance, cost, and security. The limited transaction processing capability of the blockchain system is the biggest obstacle to replacing well-known alternatives like distributed database systems. In this paper, a fuzzy logic-based transaction traffic control scheme with an enhanced Kalman filter is proposed to enhance the performance of blockchain systems. The Kalman filter predicts the transaction throughput with an artificial neural network (ANN) learning module to improve the prediction accuracy. A fuzzy inference system is designed to govern the blockchain network's real-time transaction traffic flow in accordance with the estimated transaction throughput value. A Hyperledger Fabric-based EHR testbed is used to assess the efficiency of the proposed scheme. The results indicate that the proposed scheme can increase the transaction throughput and reduce the transaction latency of the blockchain system.