AI-based Block Identification and Classification in the Blockchain Integrated IoT
Joy Dutta, Deepak Puthal, Ernesto Damiani
Abstract
Artificial Intelligence (AI) is gaining popularity in the Internet of Things (IoT) based application-based solution development. Whereas, Blockchain is become unavoidable in IoT for maintaining the end-to-end process in the decentralized approach. Combining these two current-age technologies, this paper details a brief comparative study with the implementations and further analyzes the adaptability of the AI-based solution in the Blockchain-integrated IoT architecture. This work focuses on identifying the of block data in the block validation stage using AI-based approaches. Several supervised, unsupervised, and semi-supervised learning algorithms are analyzed to determine a block's data sensitivity. It is identified that machine learning techniques can identify a block's data with very high accuracy. By utilizing this, the block's sensitivity can be identified, which can help the system to reduce the energy consumption of the block validation stage by dynamically choosing an appropriate consensus mechanism.