Machine Learning-Based Prediction System for Optimizing Underwater Wireless Sensor Networks

Background/Objectives: UWSNs have significant applications in the marine ecosystem, oceanographic research, and inspection of marine infrastructures underwater. But the rough underwater conditions come with the problem of a long latency, loss of packets, attenuation of signals and a scarcity of energy, which lower network reliability and performance. This study aims at designing a machine learning-based prediction system to enhance the performance, reliability and energy efficiency of UWSNs by predicting the network behaviour and sensor node states given different underwater environmental and communication conditions. Method: The suggested predictive framework uses several machine learning models, such as CatBoost, XGBoost, LightGBM, Random Forest (RF), Decision Tree (DT), Gaussian Naive Bayes (GNB), and K-Nearest Neighbors (KNN). The models have been trained on a dataset of both real and synthetic data of sensors in underwater environments, sensor properties, and environmental conditions (temperature, salinity, pressure), and network variables (signal-to-noise ratio (SNR), traffic load, and battery level). Preprocessing of data using methods like data cleaning, Min-Max scaling, an