# Underwater Sensor Network Redeployment Algorithm Based on Wolf Search
## 1 Introduction  
Two-thirds of the Earth surface is covered with water. Underwater wireless sensor networks (UWSNs) were developed to meet the need for environmental data collection, underwater tactic monitoring, environmental monitoring, and others [1; 2; 3; 4]. UWSN coverage control as the foundation of data aggregation, routing protocol design, positioning and tracking [5; 6; 7; 8] has a direct effect on network coverage rate, quality of service, and network lifetime. It has become one of the popular topics in the information field. Practical 3D coverage control algorithms need to consider fully the actual situation of the underwater node distribution. It also needs to adjust the position according to the monitor target and change in the water environment to optimize the coverage of the entire network. Compared with terrestrial sensor networks, UWSNs are mainly characterized by acoustic communication, signal delay and attenuation, sparse distribution of nodes, self-organization capability, and easy changing of the network topology under the influence of water flow and aquatic organisms [9]. In the large-scale deployment of UWSNs, underwater nodes are difficult to replace when batteries fail. All of these conditions are significant challenges in the process of UWSN redeployment. In the past, UWSN redeployment algorithms were mainly based on graph theory, cube lattice category, virtual force and swarm intelligence optimization, and other related ways [10; 11; 12; 13; 14; 15]. The first three types of redeployment algorithms are relatively complex and unsuitable for large-scale underwater environmental problems. The swarm intelligence optimization algorithm [16] is generally much faster than traditional optimization algorithms in terms of optimal solution search and simple calculation. The advantage is that it does not provide a global model and centralized control. The algorithm possesses strong applicability and generality [17]; therefore, it is preferred by scholarsin the field of UWSNs [18; 19; 20; 21; 22]. [PERSON] et al. [19] proposed an algorithm for underwater sensor networks based on particle swarm optimization. The algorithm solves the problem of node deployment, and it has the advantage of high convergence speed. [PERSON] et al. [20] proposed a positioning and deployment scheme for UWSNs based on the genetic algorithm by using optimization techniques. Their main objective was to achieve a coverage area of interest with the least number of nodes but easily falls into a local optimum of the considered objective function. [PERSON] et al. [21] proposed a redeployment algorithm for UWSNs based on artificial fish (OAFSA). The algorithm is an improved version of the traditional fish school algorithm. A dynamic threshold was added to foraging behavior. Naturally, the coverage rate was improved. However, the latter easily falls into a local optimum of the considered objective function. Redeployment algorithms based on swarm intelligence optimization are intuitive and easy to describe and prove. Swarm intelligence optimization algorithms exhibit common problems of poor robustness in the late stage, low convergence speed, and ease of falling into a local optimum of the considered objective function. Therefore, the search for a new swarm intelligence optimization algorithm that exhibits excellent performance in the field of UWSNs is highly significant. After the initial deployment, nodes will move freely according to the specific algorithm again, and they will finally reach coverage demand. The paper uses this redeployment. The UWSN deployment algorithm of obstacle avoidance is inspired by the predator avoidance of the wolf search algorithm. Existing redeployment algorithms generally disregard the presence of obstacles in the underwater environment. However, in reality, obstacles hinder the movement of nodes. For example, a hidden reef in a lake or ocean, swimming fish, and dense grasses are obstacles that not only affect node movement but also interfere with the communication among nodes.