PoPI: A machine learning-based consensus mechanism for blockchain-enabled IoT systems.

Abstract

Internet of Things (IoT) enables seamless connectivity and intelligent automation across diverse domains, from healthcare and agriculture to smart cities and industrial systems. However, conventional IoT architectures often rely on centralized servers for data processing and coordination, resulting in poor scalability and decreased system reliability. The integration of blockchain with IoT offers a promising approach to address these limitations of centralized IoT architectures. In practice, however, existing blockchain consensus mechanisms are often unsuitable for resource-constrained IoT devices and dynamic network conditions. To overcome this limitation, we propose Proof of Periodic Inference (PoPI), a machine learning model-based consensus mechanism tailored for blockchain-based IoT systems. PoPI uses a supervised machine learning model to periodically select a group of block producers and maintains security through random block generation within the group. It incorporates both static and dynamically changing device features, such as battery level and resource usage, to select capable nodes with high reliability, and implements fair participation mechanisms to balance network involvement over time. Theoretical analysis and experimental evaluation demonstrate that PoPI achieves high scalability, low latency and improved applicability compared to the state-of-the-art consensus protocols in dynamic IoT environments.