EXTREPSO: An Energy Estimator Based on Machine Learning with PSO Optimization for UASNs

Underwater acoustic sensor networks (UASNs) are severely affected by propagation delay, limited bandwidth, and constrained node energy. This work presents EXTREPSO, a machine learning framework that combines an Extra Trees Regressor with Particle Swarm Optimization (PSO) for offline hyperparameter tuning of a hop-level energy-efficiency predictor. The target is derived from Aqua-Sim NG trace fields using the payload size, the simulator Tx_power field, and observed hop delay. Aqua-Sim NG defines PT as transmitted signal power in watts, so the target has dimensional units of bit/J; however, because the observed hop delay includes propagation and protocol-related waiting, it is treated as an operational trace-derived metric rather than a state-resolved modem-energy measurement. In the original 80:20 benchmark, Extra Trees was the strongest baseline among seven classical ML and three DL models. A leakage-resistant sender-grouped outer five-fold evaluation with inner grouped validation was subsequently used to compare baseline Extra Trees, PSO-tuned Extra Trees, and an interpretable inverse-power baseline on untouched test folds. PSO tuning produced a modest average improvement over baseline Extra Trees (mean RMSE 2107.31 versus 2230.58), but the improvement was not consistent across folds; the inverse-power baseline achieved the lowest mean RMSE (1765.88). A separate five-run equal-budget PSO/GWO sensitivity check also found no meaningful superiority of PSO over GWO. These results support the trace-driven prediction and hyperparameter-tuning workflow while limiting claims of universal optimizer benefit or direct network-energy improvement.