## 4 Propagation Model Evaluation
### Scenario Deployment  
The network scenario deployment is shown at Figure 4 (surface view). The volume size is defined as a cube of 5,000 \(\times\) 5,000 \(\times\) 50 m (Length \(\times\) Width \(\times\) Depth); the covered area is divided in cells of 1,000 \(\times\) 1,000 m. The gateway (sink node) is always placed in the middle cell at a fixed depth of 10 m. Then we put one node per cell in a random position inside the cell, as well as random depth (this parameter will be bounded by the scenario bathymetry). Once all the nodes are deployed the connectivity of the network is checked by guaranteeing that every node has a path to the gateway (one-hop or multi-hop paths) and that there are not isolated subnetworks or nodes. Using the same area and cell size, ten different random scenarios have been built and validated for the simulation test. In Figure 5, a 3D representation of the network scenario is shown. It is located at coordinates 39\({}^{\circ}\)48'13.14\"N and 0\({}^{\circ}\)4'34.53\"W (Valencia, Spain). This view let us appreciate the different depths of the nodes, close to the surface, middle depth and at the bottom of the scenario. We have fixed the wave activity with waves of 2 m height and 80 m length. The network scenario floor is composed of gravel. All of the scenario and environmental parameters were taken from National Geophysical Data Center  
Figure 4: Network deployment 2D. databases [22, 23] related to the specific global coordinates of our network. This example could represent a typical network scenario of shallow waters with a low altimetry shape where the bottom relief is deeper as it goes farther from the coast. In Table 1 the main parameters used in the simulations are shown. With respect to network traffic load, we proposed a constant bitrate approach where every sensor node generates fixed length data packets at a generation rate defined with an exponential distribution. All sensor nodes in the network will send packets towards gateway node, so we will obtain a hot-spot traffic pattern, where all the packets are delivered to the same destination. In this section we will consider a One-Hop (OH) network topology, where all network nodes are able to reach the gateway in one hop. The power transmission is set constant in all nodes and it is calculated as the energy required by the farthest nodes (the ones in the perimeter) to reach the gateway, considering [PERSON]'s attenuation model. That means that in Thorp simulation these nodes will always reach the gateway but in both MMPE and Bellhop reachability it is not guaranteed, mainly due to the more realistic assumptions about the acoustic propagation. So, the time-varying acoustic signal \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Propagation Models & THORP, MMPE, BELLHOP \\ \# Sensors & 24 \\ \# Gateways & 1 \\ Month & Annual Average \\ Wave Height (m) & 2 \\ Wave Length (m) & 80 \\ Frequency (kHz) & 10 \\ Scenario Depth (m) & 50 \\ Global Load (packets/s) & 0.16 to 4 \\ Data Packet Size (bits) & 1,024 \\ Control Packet Size (bits) & 24 \\ Bandwidth (kbps) & 5 \\ \# Scenarios & 10 \\ Simulation Time (s) & 3,600 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation and network scenario parameters. Figure 5: Network deployment 3D (Google EarthTM). attenuation may produce packet loses due to the lack of signal strength at gateway, which is supposed to have an impact on the network performance. In Figure 6(a) the transmission range of node \(\#1\) is fixed (are covered by the circle) during all the simulation, indicating the set of nodes that always receive the transmissions from node \(\#1\) (in particular the gateway node) using Thorp propagation model. However, in Figure 6(b), MMPE and Bellhop models define the transmission range with two dashed circles.