BlockIoTIntelligence: A Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence

Abstract In the recent year, Internet of Things (IoT) is industrializing in several real-world applications such as smart transportation, smart city to make human life reliable. With the increasing industrialization in IoT, an excessive amount of sensing data is producing from various sensors devices in the Industrial IoT. To analyzes of big data, Artificial Intelligence (AI) plays a significant role as a strong analytic tool and delivers a scalable and accurate analysis of data in real-time. However, the design and development of a useful big data analysis tool using AI have some challenges, such as centralized architecture, security, and privacy, resource constraints, lack of enough training data. Conversely, as an emerging technology, Blockchain supports a decentralized architecture. It provides a secure sharing of data and resources to the various nodes of the IoT network is encouraged to remove centralized control and can overcome the existing challenges in AI. The main goal of our research is to design and develop an IoT architecture with blockchain and AI to support an effective big data analysis. In this paper, we propose a Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence that provides an efficient way of converging blockchain and AI for IoT with current state-of-the-art techniques and applications. We evaluate the proposed architecture and categorized into two parts: qualitative analysis and quantitative analysis. In qualitative evaluation, we describe how to use AI and Blockchain in IoT applications with “AI-driven Blockchain” and “Blockchain-driven AI.” In quantitative analysis, we present a performance evaluation of the BlockIoTIntelligence architecture to compare existing researches on device, fog, edge and cloud intelligence according to some parameters such as accuracy, latency, security and privacy, computational complexity and energy cost in IoT applications. The evaluation results show that the proposed architecture performance over the existing IoT architectures and mitigate the current challenges.