# Routing Selection Algorithm for Mobile Ad Hoc Networks Based on Neighbor Node Density
## 4 Proposed New Routing Method
### Cnd-Aodv
Specifically, the z-score represents the number of standard deviations between a data point and the mean. By using z-scores, one can determine the position of a data point within the entire dataset and assess its deviation from the mean:  
Figure 6:1  
where \(z\) is the z-score, \(X\) is the value of the data point, \(\mu\) is the mean of the dataset, and \(\sigma\) is the standard deviation of the dataset. In order to better evaluate the degree of deviation, it is necessary to calculate the standard deviation \(\triangle c_{j}\) of the number of common neighbor nodes \(N_{ij}\) between sender \(j\) and its neighbor nodes, i.e.,  
Figure 6:2  
According to the z-scores method, the specific expression for calculating z-scores in the data is as follows:  
Figure 6:3  
Then, the relative z-scores of nodes in the data can be obtained, where positive values indicate being above the mean, with larger values indicating a greater deviation from the mean. On the other hand, negative values indicate being below the mean, with smaller values indicating a greater deviation from the mean. Then, the z-scores method involves different confidence intervals with varying proportions, as shown in Figure 6:4  
Thus, nodes that fall outside the confidence intervals can be considered to have significant deviations and can be defined as isolated nodes or over-dense nodes. Isolated node: When the z-score generated from the number of common neighbor nodes \(N_{ij}\) between the receiver \(i\) and the sender \(j\) is less than the negative z-threshold, the receiver \(i\) is considered an isolated node of the sender \(j\). In the concrete route discovery process, when the receiver \(i\) receives the RREQ and confirms that it does not meet the destination node conditions, and the number of its neighbor nodes exceeds the threshold \(k\), it further checks whether the receiver \(i\) is an isolated node of the sender \(j\). If it is determined to be an isolated node, it will forward the RREQ with a probability of 100%, otherwise, it will perform a probability calculation to decide whether to forward the RREQ. Over-dense node: When the z-score generated from the number of common neighbor nodes \(N_{ij}\) between the receiver \(i\) and the sender \(j\) is greater than the positive z-threshold, the receiver \(i\) is considered an over-dense node of the sender \(j\). In the concrete route discovery \begin{table}
\begin{tabular}{c c} \hline \hline
**Threshold \(z\)** & **Confidence Interval** \\ \hline \(-1.282\leq z\leq 1.282\) & 80\% \\ \(-1.440\leq z\leq 1.440\) & 85\% \\ \(-1.645\leq z\leq 1.645\) & 90\% \\ \(-1.960\leq z\leq 1.960\) & 95\% \\ \(-2.576\leq z\leq 2.576\) & 99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The different confidence intervals using different thresholds of \(z\). process, when the receiver \(i\) receives the RREQ and confirms that it does not meet the destination node conditions, and the number of its neighbor nodes exceeds the threshold \"k\", it further checks whether the receiver \(i\) is an over-dense node of the sender \(j\). If it is determined to be an over-dense node, the RREQ is directly discarded; otherwise, it will perform a probability calculation to decide whether to forward the RREQ. Based on the above analysis, we will obtain a new route discovery method, and its flowchart can be expressed in Figure 12.