A Novel Machine Learning Algorithm for Enhancing Blockchain Consensus Mechanisms in IoT-Enabled Smart Cities

Abstract

The need to have more effective and secure Blockchain consensus mechanisms has been brought about by the fact that the Internet of Things (IoT) devices have become increasingly deployed in the smart cities. Though Blockchain brings a decentralized, non-reversible platform to implement the IoT applications, the conventional consensus mechanisms, such as Proof of Work (PoW) and Proof of Stake (PoS) are questionable in its scalability, energy expense, and latency particularly in the IoT systems with extremely large number of devices. To address these challenging concerns in IoT-powered smart cities, this paper proposes a new Machine Learning (ML) algorithm that would generate the best Blockchain consensus mechanism. The ML algorithm is dynamically flexible with time, and modifies the consensus strategy based on the real-time network health, way in which the IoT appliances behave, and load due to transactions giving the solution more scalability, which is more energyefficient and permits to handle transactions at a faster rate. The proposed approach is addressed on the simulated case of a smart city where data, such as those transmitted by smart meters, traffic control sensors, and environmental sensors, are collected on a realtime basis using the IoT technology. The experimental results confirm that the integrated combination of ML and the Blockchain agreement protocols provides the opportunity of refining the functioning IoT systems in the smart cities in relation to the resources optimization, the reduction of latency, the safety of transaction, and the transparency. With the aid of the Internet of Things, the study introduces an easy answer to the issue of scalability and efficiency that the current models of Blockchain pose to smart cities framework in the sense of being able to scale.