## 2 Related Works
The optimal pitch angle of the node was obtained by the computational geometry method, and then the optimal angle of deflection was calculated by the particle swarm optimization algorithm. In [33], the minimum cost to achieve full coverage for discrete points on the plane area was implemented under a deterministic deployment, which constructs a mathematical model of an integer linear programming problem. In [34], a distributed C2 algorithm was proposed based on mobilized sensors and a clustering structure. The monitoring region was divided into several hexagonal grids by using the nodes' communication distance, and the connectivity of the network was guaranteed by adjusting the position of the nodes in the neighboring clusters. Considering that the network cost was minimized while the resulting lifetime was at least equal to a given value, [PERSON] et al. [35] placed the minimum number of sensors that can be deployed to cover the given target objects during a limited time interval. Next, they proved that the problem of covering the target objects completely is NP-hard. [PERSON] et al. proposed a distance-based on linear sleep scheduling algorithm [36], which was suitable for clustering ESNs with dense sensor nodes. The main idea of the proposed algorithm was to make the cluster member nodes far from the cluster heads have higher probability to enter the dormant state, so as to ensure the quality of network services and reduce the energy consumption of the whole network. Besides, each node can dynamically adjust the transmission power to change its own emission radius, and it can improve the energy efficiency of network nodes, especially for the member nodes close to the boundaries of the cluster. The problem was that the proposed algorithm only applied to static clusters, that is, once the cluster head was selected the structure of the cluster will not be changed. [PERSON] et al. [37] proposed an integrated coverage and connectivity configuration for energy conservation in sensor networks, which can ensure that the network achieve \(K\)-coverage and \(K\)-connectivity while maximizing the number of dormant nodes. The proposed scheme was applied to the convex region, and fully considered the impact of the nodes dispersed around the boundary of the network. In [38], a heuristic algorithm was proposed to select exclusive sets of sensor nodes to cover the monitoring area completely. In addition, the exclusive sets can be updated after a round robin to balance the energy consumption of all nodes. Next, the target coverage problem was regardedas a Maximal Set Cover (MSC) problem and proved the NP-completeness of the problem. [PERSON] et al. proposed two heuristics based on linear programming and greedy approach to find the maximum number of set covers [39]. [PERSON] et al. [40] introduced a coverage-preserved and connected distributed Voronoi coverage algorithm to detect coverage redundancy sensors and implemented an energy efficient self-scheduling strategy to improve the astringency of distributed scheduling. In [41], an energy-efficient protocol was proposed for deterministic and probabilistic coverage in sensor networks, where the nodes determined their final state autonomously according to a \(\delta\)-circle and random intervals. In [42], an optimal deterministic algorithm was proposed based on the target weight by applying the target overlap and greedy algorithm. By using a perceived probability model and the greedy algorithm, the minimum value of indicator function was determined, and the overlap degree of the targets and the optimal deployment location of sensor nodes can be obtained. In [43], an adaptive adjustment of a sensing radius algorithm was proposed to achieve an effective coverage control, in which the nodes chose the optimal coverage adaptively and nodes' energy consumption could be reduced. [PERSON] et al. proposed a detect-repair scheme to solve the problem of coverage holes for area coverage in wireless sensor network with a Voronoi algorithm [44].