Experimental Performance Analysis of a Scalable Distributed Hyperledger Fabric for a Large-Scale IoT Testbed

Area of Science:

• Computer Science
• Distributed Systems
• Cybersecurity

Background:

• Blockchain offers secure data management for decentralized Internet of Things (IoT) applications.
• Current blockchain platforms face limitations in processing massive transaction volumes from IoT devices.
• A comprehensive approach for integrating blockchain into IoT systems is lacking.

Purpose of the Study:

• To empirically measure the performance of Hyperledger Fabric (HLF) in large-scale, distributed IoT environments.
• To identify performance bottlenecks within HLF for blockchain-based IoT applications.
• To present a performance monitoring model for HLF tailored to IoT requirements.

Main Methods:

• Developed and implemented a performance monitoring framework for HLF.
• Evaluated HLF performance under varying network workloads in a distributed setting.
• Assessed key performance indicators: throughput, latency, network size, scalability, and peer capacity.

Main Results:

• The study identified specific performance bottlenecks in HLF relevant to IoT applications.
• The proposed monitoring framework effectively evaluated HLF performance with detailed, real-time insights.
• HLF's capacity to handle large-scale IoT network demands was quantitatively assessed.

Conclusions:

• The developed framework provides a scalable solution for monitoring blockchain performance in IoT.
• Understanding HLF bottlenecks is crucial for its effective adoption in large-scale IoT systems.
• This research contributes to optimizing blockchain integration for future IoT applications.