A Research Framework for Blockchain-Driven AI Models in Predictive Analysis for IoT Systems
DOI:
https://doi.org/10.46610/AIBTIA.2025.v04i03.001Keywords:
Anomaly detection, Artificial Intelligence (AI), Blockchain technology, Convolutional Neural Networks (CNNs), Internet of Things (IoT)Abstract
This paper presents a pioneering framework that seamlessly integrates blockchain technology with Artificial Intelligence (AI) to enhance predictive analysis in Internet of Things (IoT) environments. In the proposed system, blockchain’s decentralized and immutable ledger ensures end-to-end data integrity, security, and transparency, mitigating risks such as tampering, unauthorized access, and data loss. Concurrently, advanced AI models are deployed to perform real-time predictive analytics, enabling accurate forecasting, anomaly detection, and intelligent decision-making based on large-scale IoT datasets. The architecture is designed to address key challenges in conventional centralized IoT analytics systems, such as single points of failure, trust issues, and vulnerability to malicious data manipulation. By combining distributed trust mechanisms with intelligent data processing, the framework enhances the reliability, scalability, and auditability of IoT applications. Experimental evaluations conducted across multiple domains including healthcare monitoring, smart city infrastructure management, and industrial automation demonstrate substantial improvements in prediction accuracy, a marked reduction in false positives, and enhanced resilience against security threats. Comparative analysis with traditional systems reveals the framework’s superior performance in maintaining data veracity and delivering timely, accurate insights. This integration of blockchain and AI offers a secure, transparent, and intelligent solution for next-generation IoT ecosystems.
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