## 5 Experimental Results and Analysis
### Effect of Deployment Deviation Ratio \(\lambda\) in a Grid Topology  
In this section, we study the effect of different deployment deviation factors on ALE for two grid topologies. When grid topology nodes are deployed, different values \(\lambda\) can be selected, \(\lambda\in[0,1]\). As settings for the investigation environment, the total number of deployment nodes is 100, the percentage of anchor nodes is 20%, and the communication radius is 20 m. As discussed above, a smaller grid deployment deviation factor will have a better effect in the case of node deployment in a grid topology. As shown in Figure 3, with the increase of \(\lambda\), the ALE of DEIDV-Hop in two grid topologies also increases, though different \(\lambda\) values have a tiny influence on ALE results. When \(\lambda=0\), the grid topology ALE is the smallest and the localization effect is the best. However, in practical production and living applications, it is challenging to deploy nodes with precision at fixed points, and often at random. In this investigation, \(\lambda=0.4\) is adopted as a condition in the distribution of real network simulations, although other values such as 0.3 and 0.5 are also suitable. From Figure 4, it can be seen that, under the four network topologies, the average ALE on four localization algorithms gradually decreases with the increase of the communication radius of the nodes. Among these four algorithms, DEIDV-Hop always has the smallest ALE. As shown in Figure 5:  
* PSODV-Hop reduces the average ALE by 7.61%, GSODV-Hop reduces by 26.35%, and DEIDV-Hop reduces by 34.88% when compared with DV-Hop in the square random topology,
* PSODV-Hop reduces the average ALE by 32.3%, GSODV-Hop reduces by 33.15% and DEIDV-Hop reduces by 40.36% when compared with DV-Hop in the square grid topology,
* Three meta-heuristic localization algorithms PSODV-Hop, GSODV-Hop, and DEIDV-Hop improve in average 40.71%, 41.05%, and 44.79%, respectively on localization accuracy when compared with DV-Hop in the C-Shaped random topology,
* Three meta-heuristic localization algorithms PSODV-Hop, GSODV-Hop, and DEIDV-Hop improve on average 41.49%, 42.85%, and 45.48%, respectively, on localization accuracy when compared with DV-Hop in C-Shaped grid topology. With the increase of communication radius, the communication range of unknown nodes becomes significant; there will have more single-hop nodes that can establish direct contact with more adjacent nodes. Also, it reduces the hop-count value between some of the unknown nodes and anchor nodes. The average distance per hop estimated by the algorithm and the hop-count value between nodes also tends to be accurate. Therefore, the estimated distance between the unknown node and the anchor  
Figure 4: (**a**) Effect of communication radius on ALE in square random topology; (**b**) Effect of communication radius on ALE in a square grid topology; (**c**) Effect of communication radius on ALE in C-Shaped random topology; (**d**) Effect of communication radius on ALE in C-Shaped grid topology. Figure 5: The average value of ALE for four topologies and algorithm. As shown in Figure 6, the ALE of four algorithms decreases with the increasing anchor node ratio, and DEIDV-Hop always has the smallest ALE. As shown in Figure 7 and in comparison with DV-Hop:  
* PSODV-Hop reduces the average of ALE by 3.36%, GSODV-Hop reduces by 14.95%, and DEIDV-Hop reduces by 34.68% in the square random topology,
* PSODV-Hop reduces the average of ALE by 27.58%, GSODV-Hop reduces by 27.89%, and DEIDV-Hop reduces by 39.76% in the square grid topology;
* Three meta-heuristic localization algorithms PSODV-Hop, GSODV-Hop, and DEIDV-Hop improve average 43.51%, 44.79%, and 47.86%, respectively, on localization accuracy in the C-shaped random topology;
* Three meta-heuristic localization algorithms PSODV-Hop, GSODV-Hop, and DEIDV-Hop improve average 44.87%, 45.85%, and 48.4%, respectively, on localization accuracy in the C-shaped grid topology.