A Stochastic Differential Equation-Driven Transformer and Graph Attention  Network Model for Predictive Fault Detection in Intelligent Wireless Sensor  Networks.

Dr. Amritesh Chandra Thakur

A Stochastic Differential Equation-Driven Transformer and Graph Attention Network Model for Predictive Fault Detection in Intelligent Wireless Sensor Networks.

Authors

Dr. Amritesh Chandra Thakur

Keywords:

Intelligent wireless sensor networks; predictive fault detection; stochastic differential equation; Transformer; Graph Attention Network; graph neural network; sensor anomaly detection; edge intelligence

Abstract

Intelligent wireless sensor networks (IWSNs) are now used for diverse industrial monitoring, smart infrastructure,precision agriculture and cyber-physical automation applications, but factors like sensor drift, sensor bias, packet dropout, burst noise and node failure affect the reliability of their diagnosis.

References

Z. Noshad, N. Javaid, T. Saba, Z. Wadud, M. Q. Saleem, O. E. Sheta, and A. H. Alkhayyat, "Fault detection in wireless sensor networks through the random forest classifier," Sensors, vol. 19, no. 7, p. 1568, 2019.

R. Ahmad, I. Alsmadi, W. Alhalabi, and L. Aljohani, "Machine learning for wireless sensor networks security: An overview of challenges and solutions," Sensors, vol. 22, no. 13, 2022

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Published

2026-06-18

How to Cite

Dr. Amritesh Chandra Thakur. (2026). A Stochastic Differential Equation-Driven Transformer and Graph Attention Network Model for Predictive Fault Detection in Intelligent Wireless Sensor Networks. Journal of Computational Analysis and Applications (JoCAAA), 35(6), 188–203. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5602