Research on Underwater OLSR Routing Protocol Based on Optimized MPR Nodes

With the development of underwater acoustic communication technology, underwater acoustic communication (UAC) networks have emerged accordingly. However, the instability of underwater acoustic communication and the mobility of underwater nodes lead to frequent changes in the topology and disorder of communication links. Solving this problem is of great significance for underwater acoustic communication networks.The Optimized Link State Routing (OLSR) is a well-known routing protocol for mobile ad hoc networks. Although it can be applied to underwater scenarios and has the advantages of rapid network convergence and effective reduction of broadcast storms, due to the complexity of the underwater environment, it has numerous drawbacks when applied. For example, the routing overhead for link maintenance among nodes is too high, its adaptability to the frequently changing underwater topological structures is poor, and it fails to meet the requirements for the efficient and stable operation of underwater communication networks. In this paper, by defining and optimizing the Multi-Point Relay (MPR) node selection algorithm, a new MPR node selection algorithm that comprehensively considers key indicators such as data throughput rate, end-to-end delay, and packet loss rate is constructed. Based on the dynamic changes of the underwater environment, this algorithm dynamically adjusts the MPR node set and accurately selects high-quality MPR nodes, and further determines the optimal transmission path, effectively alleviating problems such as frequent topology changes and disordered communication links caused by the complexity of the underwater environment.Through a large number of experiments using the OPNET network simulation simulator, the results show that compared with the traditional OLSR, Vector-Based Forwarding (VBF), and Depth-Based Routing (DBR) routing protocols, the underwater OLSR routing protocol based on the optimized MPR nodes has an increase in the data throughput rate of approximately 9% - 14%, a reduction in the end-to-end delay of approximately 15% - 21%, and an increase in the network lifetime of approximately 17% -25%.