# Difference DV_Distance Localization Algorithm Using Correction Coefficients of Unknown Nodes
## 5 Simulations and Performance Comparison
### Results and Analysis  
#### 5.2.1 The Influence Analysis of Ranging Error  
It can be seen from Figure 5, the average localization error for each algorithm increases with the increase of noise intensity. The performance of basic DV_Distance algorithm is worst, and the main reason is that there is no correction on the accumulated hop distance. The ratio of the actual Euclidean distance to the accumulated hop distance is used by [PERSON], and we can see from the result that its average localization error is slightly better than that of basic DV_Distance algorithm. However, [PERSON], [PERSON] and [PERSON] use the difference of the actual Euclidean distance and the accumulated hop distance, their localization error are obviously better than [PERSON]'s method and basic DV_Distance algorithm. [PERSON] makes the distance difference evenly distributed to each hop, and the average distance error per hop under unit distance is adopted by [PERSON]. Under the low noise intensity, the average localization error of [PERSON]'s method is slightly better than that of [PERSON]'s method, and the average localization error of [PERSON]'s method is also slightly better than that of [PERSON]'s method. With the increase of noise intensity, the average localization errors of [PERSON] method and [PERSON]' method tend to be the same, but the average localization accuracy of [PERSON]' method is significantly worse due to the great influence from noise. Similar to [PERSON]'s method, the ratio of the actual Euclidean distance to the accumulated hop distance is used by [PERSON], but it uses the correction model based on unknown nodes, so its average localization error is better than basic DV_Distance algorithm and [PERSON]'s method, but equivalent with the improved algorithms in references [23; 24; 25], which all use the distance difference to correct. The proposed algorithm combines the advantages of correction using distance difference and the correction model based on unknown nodes to calculate the correction coefficient of unknown nodes. From the results, we can see that the localization accuracy of the proposed algorithm is the best, and its performance is significantly better than the other algorithms. Figure 6 shows the change curve of mean square deviation with noise intensity. At the situation of low noise intensity, the stability of the proposed algorithm is better than that of other algorithms. With the increase of noise intensity, the localization stability of the proposed algorithm is basically equivalent to that of basic DV_Distance algorithm and methods in [22; 23; 24; 26], and the localization stability of [PERSON]'s method is the worst under high noise intensity.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline Monitoring area & [0,100 m] \(\times\) [0,100 m] \\ Total number of sensor nodes & 100 \\ Total number of anchor nodes & 20 \\ Total number of unknown nodes & 80 \\ Standard deviation \(\sigma\) of Gauss white noise & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter table for influence analysis of maximum communication radius.