# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 4 Performance Evaluations
### Algorithm Simulation and Analysis  
We used MATLAB for simulation. To eliminate the randomness of the experiment, the final result was taken as the average of 30 experiments. Assuming that the three-dimensional monitoring water area is 200 m in each dimension, the sink node is located at the center of the water surface, and 64 events are to be monitored. Each node has the same initial energy, and all nodes are assumed to be homogeneous. Table 2 shows the meanings and values of simulation parameters. Figure 6: The change trend of the fitness function when the number of nodes changes and the node sensing radius \(r_{\mathrm{s}}\) changes. Figure 7: The change trend of the number of nodes in the minimum node set when the number of nodes changes and the node sensing radius changes. We now demonstrate ESACC's performance evaluation results in extending the life cycle and maintaining coverage of UASNs. ESACC was applied to the sink node. By controlling the sleep or active state of the node, the node energy can be fully utilized while maintaining efficient coverage of the event. Assume that the sink node has enough power to schedule every node in the network. When a node's energy is below a critical value, the ESACC wake-up scheme can determine which neighbor must be awakened to recover uncovered events.Therefore, the network coverage, average remaining energy of network nodes, and network runtime need to be considered simultaneously. To compare the performance of the ESACC with those of LEACH-Coverage-U [11] and CMSS algorithm [6] (the CMSS algorithm is a typical algorithm for extending the network lifetime through sleeping scheduling), the network parameter settings mentioned above were used for simulation. It should be noted that all experimental designs here are based on the idea of clustering. Figures 8 and 9 show the comparison of the network lifetimes of the above three algorithms. Figure 8 is a comparison of the network lifetime of the ESACC algorithm with the CMSS and the LEACH-Coverage-U algorithm when the number of events is fixed at 64, the node sensing radius \(r_{s}\) is fixed at 20 m, and the number of nodes varies from 125 to 1000. As can be seen in the figure, in terms of average network lifetime, the ESACC algorithm is 951.8\(\sim\)2028.9% better than EACH-Coverage-U and 12.2\(\sim\)57.6% better than CMSS. Figure 9 is a comparison of the network lifetime of the ESACC algorithm with the CMSS and LEACH-Coverage-U algorithm when the number of events is fixed at 64, the number of nodes is fixed at 343, and the node sensing radius \(r_{s}\) varies from 15 to 40. It is easy to find that the proposed ESACC algorithm is 1294.1\(\sim\)1958.9% better than EACH-Coverage-U, and 24.3\(\sim\)40.9% better than CMSS. Obviously, the proposed ESACC algorithm shows good performance compared to other existing methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Initial energy of node \(E_{O}\) & 1000 J & Node energy threshold \(E_{th}\) & 10 J \\ Carrier frequency \(f\) & 25 kHz & Packet size \(M^{\prime}\) & 1 kbit \\ Node sensing radius \(R_{s}\) & 30 m & Circuit energy consumption to process one bit of information \(E_{dec}\) & 50 nJ/bit \\ Average depth \(H\) & 100 m & Data transmission rate \(S_{v}\) & 5 kbps \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation scenarios and parameters. Figure 8: Relationship between network lifetime and number of nodes. Figure 10 is a comparison of the network coverage of the ESACC algorithm and the CMSS and LEACH-Coverage-U algorithms with the change of rounds in the network when the number of nodes is fixed at 512, the node sensing radius \(r_{\rm s}\) 20 m, and the event 64. It can be seen that, when the number of running rounds is the same, the network coverage rate of the LEACH-Coverage-U algorithm is significantly lower than that of ESACC and CMSS.