## 4 Anchor Optimization Selection Strategy  
As analyzed, the playback delay is a cost factor, and the positioning satisfaction is a benefit factor. Based on the two types of factors, we present the anchor optimization selection strategy DSA. This paper aims at selecting the most suitable scheme from the limited number of optional anchor interval schemes. Hence, it is a typical MADM problem. As one of the most widely used method to solve the MADM problem, the TOPSIS method [20] proposes the concept of ideal solution (\(S^{+}\)) and negative ideal solution (\(S^{-}\)). The basic principle of TOPSIS is using the distance from each candidate to \(S^{+}\) and \(S^{-}\) as the criterion to rank the candidates. According to the ranking, we can select the most suitable candidate. The detailed steps of the TOPSIS method are summarized as follows:  
**Step 1. Construct the normalized decision matrix A**  
First, it is important to construct the original decision matrix before the normalization, and the decision matrix V with m candidates and n factors is shown in Equation (3):  
In this paper, the decision matrix V consists of 20 optional schemes and 2 factors. \[V=\left[ \begin{array}{cccc}x_{11}&x_{12}&\cdots&x_{1n}\\ x_{21}&x_{22}&\cdots&x_{2n}\\ \vdots&\vdots&\vdots&\vdots\\ x_{i1}&\cdots&x_{ij}&\cdots\\ \vdots&\vdots&\vdots&\vdots\\ x_{m1}&x_{m2}&\cdots&x_{mn}\end{array} \right] \tag{3}\]  
where \(x_{ij}\) is the \(j\)-th factor value of the \(i\)-th scheme. To eliminate the difference of these two factors, the decision matrix must be normalized. For the benefit factor and cost factor, the normalization method is shown in Equations (4) and (5), respectively. \[a_{ij}=\frac{x_{ij}-\min(x_{ij})}{\max(x_{ij})-\min(x_{ij})} \tag{4}\]  
\[a_{ij}=\frac{\max(x_{ij})-x_{ij}}{\max(x_{ij})-\min(x_{ij})} \tag{5}\]  
Then, the normalized decision matrix A is obtained:  
\[A=\left[ \begin{array}{cccc}a_{11}&a_{12}&\cdots&a_{1n}\\ a_{21}&a_{22}&\cdots&a_{2n}\\ \vdots&\vdots&\vdots&\vdots\\ a_{i1}&\cdots&a_{ij}&\cdots\\ \vdots&\vdots&\vdots&\vdots\\ a_{m1}&a_{m2}&\cdots&a_{mn}\end{array} \right] \tag{6}\]  
**Step 2. Calculate the weight of each factor**  
The entropy weight is calculated as follows:  
\[w_{j}=\frac{1-e_{j}}{n-\sum\limits_{j=1}^{n}e_{j}} \tag{7}\]In Equation (7), \(e_{j}\) is the entropy of factor \(j\):  
\[e_{j}=-\frac{1}{\ln n}\left(\sum\limits_{i=1}^{n}f_{ij}\ln f_{ij}\right)\,\left(0 \leq e_{j}\leq 1\right) \tag{8}\]  
In Equation (8), \(f_{ij}\) is the characteristic proportion of the factor:  
\[f_{ij}=\frac{a_{ij}}{\sum\limits_{i=1}^{m}a_{ij}} \tag{9}\]  
Step 3.Construct the evaluation matrix based on the entropy weight:  
\[Z=\left[ \begin{array}{cccc}a_{11}&a_{12}&\cdots&a_{1n}\\ a_{21}&a_{22}&\cdots&a_{2n}\\ \vdots&\vdots&\vdots&\vdots\\ a_{i1}&\cdots&a_{ij}&\cdots\\ \vdots&\vdots&\vdots&\vdots\\ a_{m1}&a_{m2}&\cdots&a_{mn}\end{array} \right]\left[ \begin{array}{cccc}w_{ 1}&0&\cdots&0\\ 0&w_{2}&\cdots&0\\ \vdots&\vdots&\vdots&\vdots\\ 0&\cdots&w_{j}&\cdots\\ \vdots&\vdots&\vdots&\vdots\\ 0&0&\cdots&w_{n}\end{array} \right]=\left[ \begin{array}{cccc}z_{11}&z_{12 }&\cdots&z_{1n}\\ z_{21}&z_{22}&\cdots&z_{2n}\\ \vdots&\vdots&\vdots&\vdots\\ z_{i1}&\cdots&z_{ij}&\cdots\\ \vdots&\vdots&\vdots&\vdots\\ z_{m1}&z_{m2}&\cdots&z_{mn}\end{array} \right] \tag{10}\]  
Step 4.Determine the ideal and negative ideal schemes  
This step determines the ideal scheme \(S^{+}\) and negative ideal scheme \(S^{-}\). \[S^{+}=[z_{1}^{+},z_{2}^{+}\cdots,z_{n}^{+}]=\left\{(\max\limits_{i}z_{ij}|j\in J ),(\min\limits_{i}z_{ij}|j\in J^{\prime})| \begin{array}{c}i=1,\ldots,m\\ j=1,\ldots,n\end{array} \right\} \tag{11}\]  
\[S^{-}=[z_{1}^{-},z_{2}^{-}\cdots,z_{n}^{-}]=\left\{(\min\limits_{i}z_{ij}|j \in J),(\max\limits_{i}z_{ij}|j\in J^{\prime})| \begin{array}{c}i=1,\ldots,m \\ j=1,\ldots,n\end{array} \right\} \tag{12}\]  
where \(J\) is the set of subscripts of the benefit factor; \(J^{\prime}\) is the set of subscripts of the cost factor.