Fuzzy Logic-Based Mining Strategy for Transaction Congestion Management in Blockchain Networks

Abstract

Objectives: In blockchain, mining is essential for verifying and adding transactions to the chain. Transaction approval time is increasing due to the mining process's limited capacity. To address this issue, this paper aims to reduce the approval time by introducing a new fuzzy logic optimization methodology for dynamic resource allocation of mining capacity based on resource congestion. Method: The proposed methodology does not rely on block size or mining duration and efficiently handles transaction congestion. The proposed fuzzy logic effectively handles the resources in the peak transaction. It allocates the resources dynamically using both horizontal and vertical scaling. It upgrades Transactions Per Second (TPS) and manages difficulty levels considering CPU, memory, and node utilization. Findings: Simulation results demonstrate the efficacy of the proposed methodology in improving blockchain performance compared to traditional blockchain approaches. The analysis includes average active nodes, transaction latency, memory utilization, and transactions per second. Novelty: The proposed work introduces a novel approach to blockchain mining optimization by integrating fuzzy logic for dynamic scaling decisions. This innovative method addresses adaptability and resource efficiency concerns and offers a flexible and efficient solution to blockchain scalability and transaction processing challenges. Keywords: Blockchain, Fuzzy logic, Vertical scaling, Horizontal scaling, Transaction latency