Dynamic Cost Intelligent Routing Algorithm for Heterogeneous Communication Networks

Routing is essential in communication as it determines the most efficient path for data packets to travel from the source to the destination, ensuring reliable network connectivity. However, with the emergence of cross-domain communication scenarios involving air, surface, and underwater environments, the traditional routing protocol—which considers only limited information including bandwidth or delay as static link cost during route selection—falls short of meeting the rapid and efficient demands of cross-domain heterogeneous communication. To address this issue, this paper proposes a dynamic cost intelligent routing algorithm. The algorithm incorporates an effective bandwidth estimation module and a link priority prediction module, which dynamically process the extracted link information from air, surface, and underwater communication networks. The processed data is then fed into the deep learning model, where it takes multi-dimensional link attribute information as input and outputs a one-dimensional cost corresponding to each link. The effective cost are then integrated back into the network, dynamically adjusted the routing decisions using the shortest path algorithm to optimize overall network performance. The algorithm ensures more reliable data transmission and efficient utilization of network resources. To simulate real-world scenarios, this study utilized the NS3 platform to construct a cross-domain simulation environment that encompasses radio communication, underwater acoustic communication, and optical communication across air, surface, and underwater domains. We conducted experiments by constructing a heterogeneous network consisting of 36 nodes. The results show that this algorithm outperforms the routing algorithms based on the traditional OSPF routing algorithm, and several other algorithms, in relation to throughput, packet loss rate and average delay.