# Ocean-Current-Motion-Model-Based Routing Protocol for Void-Avoided UASNs
## 1 Introduction  
In recent years, with attention being paid to marine resources by many countries around the world, the exploration and application of the ocean have been increasing, and the study of UASNs [1] is also attracting considerably more attention. UASNs are widely used in environmental monitoring, underwater early warning applications, oil exploration, marine biological research, etc. [2; 3; 4]. As shown in Figure 1, underwater sensor nodes collect data and transmit them to the sink node from deep to shallow depths using single-hop or multi-hop processes [5]. However, the nodes of UASNs are easily moved because of the ocean current movement, temperature, pressure, and salinity [6], along with other unpredictable factors in the harsh underwater environment. This results in a change in the topology of the network, the formation of the routing void [7; 8], and the interruption of theoriginal route. These factors lead superfluous data retransmission [9; 10; 11; 12] in order to seek out the latest route. Furthermore, it is difficult to replace the battery [13] embedded in the underwater node, and the increase in data retransmission will frequently consume a large amount of node energy and reduce network life. Therefore, it is particularly important to find the alternate relay nodes and rebuild new routes quickly and efficiently to recover the network communication when the topologies change and routing voids appear in dynamic underwater network environments. The main factors that influence node movement in the ocean are the tide and ocean current. Consequently, we need to analyze the tide and ocean current and predict the movement of nodes. However, the traditional node motion prediction algorithms need to calculate the node positional information, such as TOA [14] and TDOA [15]. This requires a large amount of computation, resulting in greater energy consumption and a reduction in network life. In this study, we obtain the node velocity vector information, based on a simple ocean current motion model with limited coverage. To determine the optimal value of the velocity vector, we can choose from many global optimization approaches, such as the WOA [16], ALO [17], GWO [18], etc. In [16], the WOA is highly competitive with other optimization approaches and is also appropriate for continuous optimization problems. The node of movement happens to be a continuous optimization problem in ocean current motion. However, the traditional WOA is limited by slow convergence. In order to increase the rate of convergence of the traditional WOA, we set the protection radius based on the optimal outage probability and constructed the relay node candidate set within the protection radius. The optimal velocity vector is obtained with the improved WOA, and the node with this velocity is the optimal alternative relay node. In terms of the routing strategy, a new route is constructed after selecting the optimal candidate node, which maintains the stability of the network topology, effectively solves the problem of routing voids caused by ocean current movement and reduces the end-to-end delay and average energy consumption. The contributions of this paper are as follows:  
1. We use the Gaussian radial basis function curve multiplied by multiple influence factors and tidal components of ocean currents to predict the ocean motion with limited coverage. The velocity vector of network nodes is calculated by this model to simulate the real-time node motion. 2. We improve the WOA and design a protection radius with optimal outage probability so that we can search the alternative nodes within the limited protection radius and improve the convergence rate of the traditional WOA. 3. We design the void avoided strategy to suppress the problem of the routing void. In addition, the OCMR can select the optimal relay node in the candidate forwarding set and rapidly rebuild a new route to avoid retransmission when the void occurs. Figure 1: Routing diagram of UASNs.