Machine Learning-based Predictive Analytics for Blockchain-enabled IoT Systems

Authors

  • Tripti Sharma, Shahanawaj Ahamad, Neeraj Gupta, Sivashankar P T, Eda Bhagyalakshmi, Elangovan Muniyandy, Ankur Gupta

Keywords:

Machine learning, Blockchain, IoT, Predictive analytics

Abstract

In the realm of blockchain-enabled IoT systems, machine learning-based predictive analytics serves as a cornerstone for optimizing operations, enhancing security, and maximizing efficiency. By leveraging the wealth of data generated by IoT devices and immutably recorded on the blockchain, predictive analytics algorithms can discern patterns, detect anomalies, and forecast future events with remarkable accuracy. One key application lies in anomaly detection, where machine learning models scrutinize data to identify aberrant behavior or potential security threats in real-time, thereby fortifying the integrity of the system. Moreover, predictive maintenance emerges as a vital capability, as machine learning algorithms analyze historical data to anticipate equipment failures or maintenance needs, preempting costly downtime and prolonging device lifespan. This paper is considering research in area of machine learning for predictive analysis that are made for blockchain enabled IoT system. Paper has focused on role of ML based predictive analytics and conventional research in related area. Moreover works related to blockchain enabled IoT system and ML based predictive system are focused with their methodology, limitations, outcomes and future scope.

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Published

26.03.2024

How to Cite

Elangovan Muniyandy, Ankur Gupta, T. S. S. A. N. G. S. P. T. E. B. . (2024). Machine Learning-based Predictive Analytics for Blockchain-enabled IoT Systems. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1146–1156. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/5516

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

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