# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 1 Introduction  
With the rise of the marine economy, countries around the world are paying increased attention to maritime rights and interests. Hence, underwater acoustic sensor networks (UASNs) have become a popular research topic [1; 2]. They are mainly applied to mid- and long-term marine-resource surveys, disaster warning and forecasting, environmental monitoring, and submarine exploration [3]. Their broad application prospects have gained attention in academic and military circles. The sensing range of nodes in UASNs is known to be limited. Therefore, the coverage of a monitored area is an important issue that UASNs must consider in topology control [4; 5; 6; 7]; in addition, UASNs are node-sparse networks, so the coverage problem within the scope of monitoring is more difficult to solve, as the failure of any one node may lead to coverage holes. However, underwater nodes are expensive, and their redundancy is costly and difficult to implement. Reasonable planning is required for coverage and redundancy. To achieve energy optimization, early researchers experimented with many algorithms and strategies for different network structures and application requirements [8; 9; 10; 11; 12]. This research has two main aspects. The first aspect is to achieve energy balance through network topology control, which is mainly achieved by equalizing energy loss within the entire network or optimizing node transmit power. Based on network-topology control, the energy consumption of the entire network is balanced, so that the lifetime of the wireless sensor network is extended, or the node transmit power-control algorithm is optimized. According to the redundancy of the deployment node, optimization and balance are achieved through the sleep-wake rotation of nodes. Based on different characteristics of nodes, the residual energy aware dynamic algorithm [13] dynamically senses the residual energy at nodes, determines the transmit power based on that, and builds a topology relationship. This is aimed at extending the network lifetime by reducing the use of network nodes with low remaining energy for data forwarding. The algorithm uses a centralized control-based topology construction algorithm. It needs to know the topology information of the entire network, and the overhead is too large. A local topology-control algorithm [14] is proposed to avoid the addition of nodes with low residual energy to the forwarding path. This is a distributed topology control algorithm. Fewer algorithms are based on power control and consider the remaining energy of a node. In underwater sensor network topology control, there is still a lack of simple and effective methods. The second aspect is to implement energy optimization through routing strategies. Energy optimization is mainly implemented by reducing the routing overhead. Methods of reducing routing overhead include minimum transmission energy consumption routing, maximum network lifetime routing, and path energy consumption equalization policies. The low-energy adaptive clustering hierarchy (LEACH) protocol [15] is a typical clustering algorithm. Through the cluster head selection strategy, the topology relationship is rebuilt on each selection. The geographical adaptive fidelity (GAF) algorithm [16] is a clustering algorithm based on the geographic location of nodes. The algorithm divides the monitoring area into several cells, assigns nodes to cells according to geographical locations, and periodically selects a cluster head node in each cell. The GAF algorithm is based on the plane model, and it uses the distance between nodes to judge whether they can communicate with each other. However, in practical applications, it is not possible to consider only the physical location, such as the communication channel. Moreover, the algorithm does not consider the problem of node energy balance. Each of the above energy-optimization algorithms focuses on different scenarios and application modes, and some algorithms have good results in the context of their design.