## 2 Related Works  
The main challenge in the design of ESNs is to meet the functional requirements of the network, such as reducing time delays and improving the data integrity. Meanwhile, it should deal with the problems of calculation overhead, energy consumption and communication interference under the constrained resource conditions. At present, ESNs usually use various methods to try to optimize each layer in the communication protocol, including: data fusion [19], hierarchical topology [20], minimizing the idle listening time [21], adaptive node wake up schemes [22], lightweight data authentication and encryption [23], and so on. Among them, the deterministic way by deploying sensor nodes is a common optimization strategy, which can ensure the coverage and connectivity of the area to be monitored by planning the nodes' density and deploying their position accurately [24]. However, there are a lot of applications where the sensor network nodes are randomly deployed or the distribution of nodes is not uniform. In order to achieve a uniform node density, high network coverage and stable connectivity, it is often necessary to adjust or control the nodes' state. In addition to the performance of coverage and connectivity, the nodes' location and network topology will affect Quality of Service (QoS) properties, for example, energy consumption, time delay and throughput and so on. Obviously, the communication quality and the throughput of the link will weaken significantly as the distance between the nodes increase excessively, besides, it will increase the energy consumption for packets forwarding. In [25], an optimized broadcast BPS protocol for sensor networks was proposed, which adopts a geometric-based approach to reduce the retransmissions adaptively by maximizing the hop length. In BPS, nodes did not need any neighborhood information and the approximate optimal connected coverage set is solved by a broadcast and time delay scheme. In [26], an Adaptive Coordination Protocol (ACP) was suggested to determine the deployment of backbone nodes over heterogeneous wireless networks. The main idea was to construct the network structure based on the cellular model, and used the node's position in the hexagon model as the \"strategic point\", then to determine the actual location of the sensor nodes. [PERSON] et al. [27] proved that k-degree coverage preservation of the target area can be achieved while the communication range is twice the sensing range, and then proposed a method to determine whether the node's sensing coverage area had been covered by other working nodes. By acquiring the location information of the neighbor nodes in the sensing range, the node can calculate the contribution of circle angle from its adjacent nodes. If the circle angle coverage from its neighbor nodes ran up to \(2\pi\), the node can be determined to be a redundant node and switched to a dormant state. In order to determine whether the node was redundant more accurately, [PERSON] et al. [28] proposed a coverage degree that can be obtained by means of the ratio of the circumference in the nodes' edges being covered. In [29], a novel energy consumption model was devised and a heuristic distributed greedy algorithm was proposed to solve the problem of multiple target coverage. The proposed algorithm aimed at maximizing the number of targets by establishing a mathematical model and adjusted the sensing radius of the nodes adaptively to save network energy consumption. With the intention of maintain the connectivity of the network, a Communication Weighted Greedy Cover (CWGC) algorithm [30] was introduced to avoid potential coverage holes. Aiming at resolving the coverage problem for randomly deployed mobile sensor networks, [PERSON] et al. [31] presented two strategies named coverage-priority and connectivity-priority for node deployment based on a ring model of energy consumption. [PERSON] et al. proposed a novel perception model for three-dimensional space and a coverage-enhancing algorithm for a plane monitoring area [32].