## 4 Cluster Sleep-Wake Scheduling Algorithm
### Cluster Sleep-Wake Scheduling Algorithm
#### 4.3.4 Description of the Algorithm
At the same time, since the perceived contribution

Figure 17: Relationship between the number of working nodes and time. of redundant nodes in the redundant node sleep algorithm is smaller than the perceived contribution of the working node, the more redundant nodes are awake, the greater the node redundancy in the network. Therefore, when the node fails, if you want to restore the network coverage, you need to wake up more redundant nodes, so there will be more working nodes in the network, so it will consume more energy. Therefore, when the network running time is the same, the cluster sleep-wake scheduling algorithm has more network residual energy. The relationship between the delivery rate of UWSN network nodes and the number of experimental rounds is shown in Figure 21. In general, the delivery rates of the three algorithms tend to decrease with the increase of the number of rounds. This is due to the gradual death of nodes in the network, which affects the connectivity of the network. The random deployment algorithm and the redundant node sleep algorithm set a fixed sensing radius for the node, and the cluster sleep-wake scheduling algorithm sets the sensing radius of the node by the rank of the node. In addition, the algorithm ensures as much as possible that there are working nodes in each partition unit to keep the network connected. The different sensing radius settings of the nodes enable the network to maintain a higher connectivity in the same experimental environment. The rotation strategy of the control node of the cluster sleep-wake scheduling algorithm and the sleep wake-up method of the node reduce

Figure 19: Relationship between the number of failed nodes and time. Figure 18: Relationship between coverage and the number of working nodes. the energy consumption of the network and avoid premature death nodes, which provides a reliable condition for the network to maintain high connectivity. Therefore, under the same experimental conditions, the cluster sleep-wake scheduling algorithm has higher delivery rate than the random deployment algorithm and the redundant node sleep algorithm. The relationship between the packet loss rate and the number of experimental rounds in the network is shown in Figure 22. It can be clearly seen that the packet loss rate of the three algorithms increases with the network operation. This is because the three algorithms use the same routing protocol (Vector-Based Forwarding Protocol (VBF)), so the packet loss situation increases. The trend is consistent. However, compared with the cluster sleep-wake scheduling algorithm, the random deployment algorithm and the redundant node sleep algorithm have higher packet loss rates when transmitting the same data packet (30 kpt/min). This is because the cluster sleep wake-up scheduling algorithm deploys the nodes, so that the underwater nodes are all within the communication range, ensuring reliable transmission of data. Further, they use the control node rotation strategy to prevent the control node from running out of energy and causing packet loss. All of the above methods can reduce the packet loss problem caused by the node itself in the process of data transmission during the cluster sleep-wake scheduling algorithm. Therefore, although the packet loss rate of the network

Figure 21: Relationship between the delivery rate and time. Figure 20: Relationship between network residual energy and time. increases with time, the cluster sleep-wake scheduling algorithm can still maintain a lower packet loss rate. ## 6 Conclusions

In this paper, a cluster sleep-wake scheduling algorithm is designed for an underwater 3D environment. In the initial stage of the algorithm, many sensor nodes were randomly placed in a 3D environment, and then the sensor network was divided into partition units and clusters. Temporary control nodes could prepare for subsequent distributed scheduling.