## 1 Introduction
It divides the scenario in a grid of 3-D cells, performing the requiring computations to get a representative sound pressure for each cell. If we reduce the cell size, we can obtain more accurate prediction results, but the computational demand for the corresponding calculations is overwhelming at medium to large size network scenarios. The MMPE model is implemented with OPNET Modeler [4], so small scale network simulations may be performed, but there is an intrinsic scalability problem when performing network simulations. Another underwater network simulation framework is the World Ocean Simulator System (WOSS) [5]. It is composed by several tools like (a) ns2 simulator [6], (b) Bellhop tool that accurately models the underwater sound propagation by an specific ray tracing algorithm, and (3) network scenarios defined with real data (temperature, salinity, wave activity, _etc._) from well-known world ocean databases. The WOSS framework and the MMPE model are two approaches that perform an accurate acoustic propagation modeling, but they suffer from high complexity limiting the simulation to small network scenarios and low numbers of network nodes. In order to alleviate the complexity constraint, in [7] we proposed a statistical prediction model based on the Bellhop ray tracing tool that reduces its complexity and achieves similar levels of prediction accuracy, so we will be able to perform network modeling with reasonable high accuracy level and low computational overhead. These modeling tools and a lot of variations around them lead to the hard task of comparing two different proposals unless they are implemented on the same platform, and even in this case, the simulator should be as realistic as possible towards the real environment conditions. Otherwise the results will lack of accuracy, and empirical testing, at least in scale-down experiments, should be done before releasing the final implementation of the underwater nodes, reducing the power of simulation tools for predicting real network behavior. At simulation time, when we define the parameters of a network scenario and the location where network nodes would be deployed, we may use a simple assumption through general scenario parameters or define those scenario parameters that will have a direct influence in the acoustic propagation behavior. For example, we may decide to use a simple scenario where the sound speed propagation is considered as a fixed value of 1,500 m/s, with a two dimensional deployment area (depth is not considered) and a simple acoustic propagation approach like the one proposed by [PERSON] [8], to evaluate the performance of a point-to-point link between two nodes. On the contrary, we could define a more detailed network scenario by including, among others, the scenario location with bathymetry and floor sediment composition that will impact on the way sound propagation is reflected/absorbed at the ocean floor. Also, the temperature of the water will depend on both the latitude and longitude of network scenario and the season of the year. This fact together with the water salinity and the depth may change the sound speed between 1,450 and 1,540 m/s. There are other important factors that may change sound propagation behavior such as the well-known ocean wave's influence which is different for shallow and deep waters, or the noise produced by ships, biological activity or shoals. All of these scenario parameters should be taken into account in order to develop detailed acoustic propagation models for UWSN, so modeling higher-level network protocols will be aware of network scenario conditions, and the obtained simulation results would be closer to the ones obtained in experimental tests [9]. In this work, we will review several acoustic propagation models from simple approaches to the more accurate ones, and observe their behavior when different network scenario parameters are changed (_i.e._, wave activity), so we can determine their sensibility to environmental network scenario parameters.