# Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT
## 2 Related Works  
Several review articles have been published on network node localization [14; 15], which classify and introduce various localization algorithms and techniques proposed in recent years. This section focuses on papers related to the DV-Hop algorithm. [PERSON] et al. [16] proposed a correction strategy to optimize the average hop distance of anchor nodes, thereby improving the accuracy of the anchor node distances. By multiplying the unbiased estimated average hop distance by the hop count, the estimated distance between anchor nodes is obtained. The deviation between the estimated and actual distance is used as a correction factor to adjust the average hop distance of anchor nodes. [PERSON] et al. [17] suggested that errors typically follow a Gaussian distribution and argued that using the minimum mean square error criterion, rather than unbiased estimation, is more suitable for calculating the average hop distance of anchor nodes. However, as sensor nodes are randomly deployed within the monitoring area, varying degrees of influence between anchor nodes should be expected. To address this, [PERSON] et al. [18] used the hop count between anchor nodes as a weight and proposed a method for calculating the average hop distance of anchor nodes based on a weighted minimum mean square error criterion. [PERSON] et al. [19] considered the impact of the average hop distance of anchor nodes across the network for distance estimation and proposed using the network-wide average hop distance of anchor nodes as the average hop distance for unknown nodes. However, in real-world scenarios, it is unlikely that all unknown nodes will share the same average hop distance. [PERSON] et al. [20] proposed a constraint strategy to limit the number of anchor nodes involved in the calculation, using the average hop distance of anchor nodes within the communication radius of the unknown node as the average hop distance of the unknown node. Furthermore, [PERSON] et al. [21] analyzed the impact of anchor nodes on unknown nodes and assigned different weights to anchor nodes based on network connectivity and hop count. They proposed a weighted method to calculate the average hop distance of unknown nodes. Experimental results showed that the most accurate average hop distance for unknown nodes was achieved when 10% of the anchor nodes were involved in the calculation. [PERSON] et al. [22] proposed a similar path search algorithm to obtain a multi-hop path between anchor nodes, identifying the optimal average hop distance by calibrating the average hop distance from the unknown node to each target anchor node. Ultimately, both the original DV-Hop algorithm and the aforementioned improved strategies use the product of the minimum hop count and average hop distance for distance estimation. In contrast, [PERSON] et al. [23] proposed an accumulated hop distance method for distance estimation, where each anchor node uses the minimum mean square error criterion to obtain the average hop distance, and a weighted strategy is employed to calculate the average hop distance for unknown nodes. The node-to-node distance is then obtained by accumulating the average hop distances of the unknown nodes along the link. [PERSON] et al. [24] introduced a weighted least squares method to determine the position of unknown nodes, effectively reducing localization errors. To minimize error propagation in solving distance equations, [PERSON] et al. [25] proposed a localization algorithm called PERLA. This algorithm divides each distance equation by the one formed by the closest anchor node to the unknown node and uses the information generated during the solution to correct the coordinates of the unknown node. [PERSON] et al. [26] designed an improved DV-Hop localization method based on Stephenson iteration. This method uses the localization result of the original DV-Hop as the initial value for the Stephenson iterative model and gradually converges to the true position of the unknown node through continuous iteration. [PERSON] et al.