Predictive Maintenance for <scp>UWSNs</scp> : A Practical Implementation of Fault Detection Using <scp>NDN</scp> and Deep Learning

ABSTRACT Underwater Wireless Sensor Networks (UWSNs) play a critical role in collecting data for various marine applications, but their deployment and maintenance in harsh underwater environments pose significant challenges. Building upon our prior conceptual work, this article presents a practical implementation of an AI‐driven fault detection system for UWSNs, a key component of a future predictive maintenance framework incorporating Named Data Networking (NDN). Using the “Underwater Sensor Dataset,” we implemented a feed‐forward neural network to classify sensor readings as healthy or faulty. The model achieved high accuracy (99.9%), precision (100%), recall (99.1%), and F1‐score (99.6%) on the test set, demonstrating its effectiveness in fault detection. Our findings highlight the feasibility and potential of AI‐based predictive maintenance in UWSNs, even with a simpler neural network model. The framework promises reduced downtime, minimized maintenance costs, and an extended lifespan for UWSNs.