PF-DTN: Predictive Routing for Intelligent Delay Tolerant Networks Using RNN-LSTM Deep Learning With Monte Carlo Dropout Uncertainty Estimation and Hybrid Deterministic, Probabilistic, and Uncertain Routing Strategies

Delay tolerant networks (DTN/s) represent an evolution of traditional ad hoc networks, specifically designed for extreme environments characterized by intermittent connectivity, unpredictable node mobility, high transmission delays, and frequent disruptions. Conventional routing protocols prove ineffective in these contexts, necessitating adaptive and robust solutions. In this paper, we propose PF-DTN (Predictive Forwarding for DTN), a hybrid adaptive routing algorithm that combines the prediction of future trajectories of DTN nodes using an LSTM (Long Short-Term Memory) model with relay selection leveraging deterministic, probabilistic, and uncertain strategies. PF-DTN also integrates uncertainty estimation via the Monte Carlo Dropout technique, enabling the dynamic adaptation of the routing strategy based on prediction reliability. The proposed PF-DTN architecture is structured into three phases: contextual mobility data collection, trajectory prediction with uncertainty estimation, and adaptive relay selection. Experimental evaluations conducted on the ONE simulator demonstrate that PF-DTN outperforms the benchmark protocol Prophet and remains competitive with recent state-of-the-art approaches. Our approach achieves a high delivery rate with relative gains over Prophet reaching up to +46.15% in low- to medium-density networks, along with latency reduction of up to +22.89% in high-density environments. The elevated overhead ratio, attributable to the computational demands of predictive and adaptive modules, represents a justified trade-off given the improvements in delivery reliability and latency control. These results demonstrate PF-DTN’s effectiveness and robustness across diverse DTN environments, ranging from predictable to highly dynamic and uncertain networks, establishing an optimal balance between reliability, speed, and communication cost.