Analysis of fault diagnosis in wireless sensor networks using Artificial Intelligence techniques
DOI:
https://doi.org/10.29070/3yf61773Keywords:
Wireless Sensor Networks, Artificial Intelligence, Fault Diagnosis, Machine Learning, Deep Learning, Fault Detection, Long Short-Term MemoryAbstract
Wireless Sensor Networks, also known as WSNs, have emerged as an indispensable element in a wide range of contemporary applications, such as healthcare, environmental monitoring, industrial automation, and smart city infrastructures. Nevertheless, network performance, dependability, and data integrity can be negatively impacted by node failures, communication mistakes, packet loss, and sensor malfunctions. By combining deep learning and machine learning approaches, this study offers an AI-based framework for WSN fault diagnosis, which improves problem identification and categorisation. A number of network defects can be efficiently identified using the suggested methodology, which includes data pretreatment, feature extraction, model creation, and comparative performance evaluation. The results show that by efficiently learning complicated patterns from sensor data, AI-based systems deliver problem diagnosis that is more accurate, adaptive, and dependable than traditional methods Furthermore, feature engineering and data preprocessing significantly improve classification performance and reduce misclassification rates. The proposed framework supports real-time fault monitoring, predictive maintenance, and intelligent decision-making, thereby enhancing network reliability, operational efficiency, and system resilience. The study demonstrates that AI-driven fault diagnosis offers a scalable and efficient solution for developing robust and dependable Wireless Sensor Networks in dynamic and resource-constrained environments.
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