## 2 Related Work
### Localization Method Based on RSS  
[PERSON] et al. [26] propose a deep convolutional network localization method. This method firstly divides the localization region into multiple sub-regions, then places the target node in sub-regions (1,1) and measures the RSS from sub-regions (1,1) to (N, N); then, it moves to sub-region (1, 2) and repeats the same operation until moving to (N, N); finally, the deep neural network method is used to train and learn the timestamp, RSSI, SSID and MAC address information of the captured beacon frame to achieve locating. The implementation process of this method needs a lot of data collection, and the data need to be collected again once the environment changes. [PERSON] et al. [22] put forward the WiMAP localization method, which divides the localization process of the target node into three stages: sample collection (select reference point), sample filtering and location evaluation. When collecting samples, the target region is sampled at equal intervals, then the acquired RSS is filtered, and finally, the geometric mean value is calculated only for the selected x% sample points. This method has the problems of redundant sampling and long sampling time. To reduce the number and time of sampling, [PERSON] et al. [23] propose the DWiMAP method, which assigns different sampling densities according to the distance from the target node. [PERSON] et al. [27] further improve the weighted centroid localization (WCL) method [28; 29] and propose an extended weighted centroid localization (EWCL) method, which utilizes four reference points to locate. However, only three are deployed in the localization region. The distance information of these three reference points is used to estimate the distance between the target and the fourth reference point. This method is feasible in the theoretical study, but there may be accumulated errors in the actual test environment.  
[PERSON] et al. [30] and [PERSON] et al. [24] propose the particle swarm optimization (PSO) method to locate the target node. The main idea of [30] is that when the distance from the particle to all samples is equal to the distance from the target node to all samples, the particle location is selected as the target node location. However, the disadvantage of this method is that when the number of samples is large, the particles that can satisfy the condition are not unique, which will lead to high localization error. [PERSON] et al. [24] propose a method based on RSS and PSO to locate the target node, which achieves localization by using multiple samples of RSS at known locations as the input of PSO algorithm. Although this method is easy to implement, it does not solve the problem that PSO is prone to fall into local optimum, resulting in slow convergence speed and low localization precision. To solve this problem, [PERSON] et al. [31] improve the particle velocity but still do not solve the problem that particles are prone to fall into local optima.  
In a word, the localization method based on RSS still has some problems in the selection of reference point location and locating the target node. Therefore, this paper proposes a novel localization method, which not only improves the selection strategy of reference points location, but also introduces a hybrid PSOGWO algorithm when locating the target node to achieve high precision and efficiency.