## 2 Related Work
### Hybrid PSOGWO Principle
#### 2.2.2 Gwo
Gaussian filtering eliminates the deviation of the RSS value caused by noise to a certain extent, and the stability is better. \[L(t+1)=L_{p}(t)-A\times D,A=2a\times r_{1} \tag{4}\]2 The relationship between distance and RSS. As shown in Figure 4, the distance within 0-9m is selected to measure the RSS obtained by the receiver, where the black curve represents the original data and the other curves are the fitted RSS attenuation model. In Figure 4, the other values represent the three sets of parameters corresponding to the three fitted models, Logistic, Log3P1 and Poly4. Each set of parameters can ensure that the corresponding model achieves an optimal fit. After comparison with a variety of curve models, three kinds of models with good fitting effect, Logistic, Poly4 and Log3P1, are selected to construct the relationship model between RSS and distance. #### 3.1.2 Model Evaluation

To further select a signal attenuation model with the best fitting effect, this paper evaluates the three types of models from the following three criteria:

\[L(t+1)=L_{p}(t)-A\times D,A=2a\times r_{1} \tag{4}\]3 The RSS processing. \[L(t+1)=L_{p}(t)-A\times D,A=2a\times r_{1} \tag{4}\]4 The RSS processing. Reduced Chi-Sqr: The sum of the squared variances of the estimated and actual \(y\) values at each point is called the sum of residual squares. The smaller the sum of residual squares, the better the fitting effect of the model. Figure 4 shows that the minimum residual sum of squares of the Poly4 model is 4.82058, and the maximum residual sum of squares of the Log3P1 model is 5.38572. R-Square(\(R^{2}\)): The closer \(R^{2}\) is to 1, the better the fitting result is. Among the three models, the \(R^{2}\) of log3p1 model is 0.95437, and the fitting effect is the best. Adjusted \(R^{2}\): The larger the gap between adjusted \(R^{2}\) and \(R^{2}\), the worse the fitting of the model. Figure 4 indicates the gap between \(R^{2}\) and \(R^{2}\) adjusted by logistic, and poly4 and log3p1 of the three models are 0.00508, 0.00629 and 0.00352, respectively. The gap between \(R^{2}\) and \(R^{2}\) adjusted by the three models is small, so the fitting effect is equivalent. To sum up, this paper selects poly4 curve to build the relationship model between RSS and distance. The equation of the poly4 fitting curve is as follows:

\[L(t+1)=L_{p}(t)-A\times D,A=2a\times r_{1} \tag{4}\]5

### Selection Strategy of Reference Point Location

The selection strategy of reference point location includes the following three stages: determine and reduce the selecting region of the reference point, adopt deployment strategy of reference point location and use the weighted distance strategy for selecting reference points. These three stages are introduced in detail below. #### 3.2.1 Determine and Narrow the Selected Region of Reference Point

After determining the target node is in a specific room or region, this paper adopts the dichotomy to binary the region. As shown in Figure 5a, we detect the RSS at the center of the divided four regions. If multiple regions are detected, we select the region with the largest RSS (i.e., the blue region in the figure) as the next sampling region. If the RSS is not detected, as shown in Figure 5b, we divide the four regions again, sample at the green point, and then select the next sampling region (i.e., the pink region in Figure 5b). Through multiple measurements, we found that the measured RSS at the same location will fluctuate up and



#### 3.2.2 Deployment Strategy of Reference Point Location

The dichotomy can effectively narrow the approximate region of the target node. After the approximate region is determined, this paper selects as few reference points as possible to locate the target node.