# SPACNet: A Simulation Platform of an Acoustic Cognitive Network
## 6 Conclusions  
Despite the extreme importance of underwater acoustic cognitive networks (UACNs) in extensive marine missions as recognized by the research community and the industry, the lack of convenient simulation platforms hinders research into UACNs as the conventional network simulation platforms are not specially designed to enable cognitive acoustic networking. To facilitate comprehensive performance evaluation of UACNs via a simulation, a novel simulation tool, i.e., SPACNet (simulation platform for acoustic cognitive network), was designed and is presented in this article. It provides effective and efficient support for the application of CNs in underwater environments by saving substantial hardware  
Figure 11: The topology of the field UACNs and the network nodes adopted. **(a)** The topology of the field UACNs. **(b)** Network nodes adopted. \begin{table}
\begin{tabular}{c c} \hline \hline
**Network Routing Mode** & **PDR** \\ \hline Cognitive routing & 92.5\% \\ Fixed routing & 75.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PDR results of the field experiment. resources, facilities, and personal resource costs associated with field underwater acoustic network experiments. The design of SPACNet is outlined in detail. The key difference between cognitive wireless networks and UACNs is the significant difficulties caused by challenging underwater acoustic channels and the diverse underwater acoustic physical layer and network protocol to mitigate them; thus, the capability to sense underwater acoustic channels and accordingly optimize network operation mechanisms is crucial to the design of a simulation platform for UACNs. Specifically, to enable the high-fidelity formulation of underwater acoustic environments, the Bellhop model with a specific marine environmental parameter input was embedded to generate time-varying underwater acoustic channels between network nodes. Moreover, SPACNet builds a protocol programming framework based on state machines, implements the UACNs protocol within this framework, and simulates cross-layer protocols through information exchange between protocol layers. Based on the marine environment-driven multinode underwater acoustic transmission process and the cross-layer protocol that enables information exchange, the behavior of cognitive acoustic networking can be comprehensively evaluated by SPACNet in terms of multiple quantitative network performance indicators. The effectiveness of the proposed SPACNet in evaluating UACNs was verified by a simulation UACNs consisting of 21 nodes specialized for marine environmental monitoring. Furthermore, a five-node field UACNs similarly configured according to the simulation scenario was deployed in the offshore sea area near Xiamen harbor, the performance of which was then analyzed for comparison with the simulation. For both the simulation and field experiment, with respect to the two different routing schemes, i.e., cognitive routing and fixed routing, the impact of the underwater acoustic channel was adapted to cause different state-transform types under the framework of SPACNet. The corresponding variations of specific routing in the cognitive routing scheme driven by the cognitive network mechanism, thus, verify that the proposed SPACNet is capable of supporting different state-transform types caused by underwater environmental factors. Finally, the general consistency between the results obtained from the SPACNet-based simulation and that obtained from the field UACNs experiment validate the performance of SPACNet. Note that, as a network simulation platform, it is crucial to provide extensive comparison functions to evaluate the performance of different network schemes, including the newly developed example and that previously proposed. However, because there is no current unified protocol standard or protocol framework for underwater networks, the protocol designs details of different UACNs works are highly diverse.