Mar Avila

Mohammadhossein Homaei, Oscar Mogollon Gutierrez, Ruben Molano et al.

Digital Twins (DTs) for Water Distribution Networks (WDNs) require accurate state estimation with limited sensors. Uniform sampling often wastes resources across nodes with different uncertainty. We propose an adaptive framework combining LSTM forecasting and Conformal Prediction (CP) to estimate node-wise uncertainty and focus sensing on the most uncertain points. Marginal CP is used for its low computational cost, suitable for real-time DTs. Experiments on Hanoi, Net3, and CTOWN show 33-34% lower demand error than uniform sampling at 40% coverage and maintain 89.4-90.2% empirical coverage with only 5-10% extra computation.

Mohammadhossein Homaei, Mehran Tarif, Pablo Garcia Rodriguez et al.

Industrial Control Systems (ICS) in water distribution and treatment face cyber-physical attacks exploiting network and physical vulnerabilities. Current water system anomaly detection methods rely on correlations, yielding high false alarms and poor root cause analysis. We propose a Causal Digital Twin (CDT) framework for water infrastructures, combining causal inference with digital twin modeling. CDT supports association for pattern detection, intervention for system response, and counterfactual analysis for water attack prevention. Evaluated on water-related datasets SWaT, WADI, and HAI, CDT shows 90.8\% compliance with physical constraints and structural Hamming distance 0.133 $\pm$ 0.02. F1-scores are $0.944 \pm 0.014$ (SWaT), $0.902 \pm 0.021$ (WADI), $0.923 \pm 0.018$ (HAI, $p<0.0024$). CDT reduces false positives by 74\%, achieves 78.4\% root cause accuracy, and enables counterfactual defenses reducing attack success by 73.2\%. Real-time performance at 3.2 ms latency ensures safe and interpretable operation for medium-scale water systems.

Mohammadhossein Homaei, Mehran Tarif, Agustin Di Bartolo et al.

The Internet of Underwater Things (IoUT) faces major challenges such as low bandwidth, high latency, mobility, and limited energy resources. Traditional routing protocols like RPL, which were designed for land-based networks, do not perform well in these underwater conditions. This paper introduces RL-RPL-UA, a new routing protocol that uses reinforcement learning to improve performance in underwater environments. Each node includes a lightweight RL agent that selects the best parent node based on local information such as packet delivery ratio, buffer level, link quality, and remaining energy. RL-RPL-UA keeps full compatibility with standard RPL messages and adds a dynamic objective function to support real-time decision-making. Simulations using Aqua-Sim show that RL-RPL-UA increases packet delivery by up to 9.2%, reduces energy use per packet by 14.8%, and extends network lifetime by 80 seconds compared to traditional methods. These results suggest that RL-RPL-UA is a promising and energy-efficient routing solution for underwater networks.