Reinforcement learning and movement prediction for adaptive routing in opportunistic networks

Opportunistic Networks are a type of mobile ad-hoc network in intermittent communication environments, allowing nodes to exchange data whenever they come into contact, making them particularly useful in areas with disruptions or limited infrastructure. Efficient routing is crucial for these networks and is required to adapt to dynamic topology changes. We propose a novel Spray-Learn-Wait routing protocol, utilizing clustering-based movement prediction and reinforcement learning to optimize the data exchange between nodes. Compared to established protocols like Epidemic, First Contact, and ProPHET, message delivery probability may be increased while reducing the network overhead ratio and the number of dropped messages. The protocol follows the principle of minimal data sharing within a network to meet sustainability and privacy requirements.