Maritime Opportunistic Network Routing Strategies for Assessing Link Connectivity Based on Deep Learning

In opportunistic networks, where link performance is often highly variable or extreme due to the intermittent nature of communication links between nodes, there may never be a continuous and complete path between the receiver and the sender, and packets of information can only be stored and carried by the movement of nodes, which then look for forwarding opportunities when they meet. Existing routing protocols for opportunistic networks suffer from problems such as excessive network memory consumption or insufficient link prediction that is focused on link connectivity determination. In this paper, we propose an efficient opportunistic network routing protocol that evaluates the historical values of encounter probability, movement posture, and acquired resource availability of all nodes within the communicable range based on link prediction. The intelligent prediction of link connectivity state provides a reliable aid for routing decisions, which can provide longer-period communication in the ocean; the consideration of nodes’ comprehensive attributes establishes the priority of message forwarding, avoids duplicate transmissions and route invalidation phenomena, and effectively improves the success rate of message delivery. It also reduces the transmission latency and routing overhead compared to the existing schemes.