## 2 Related Work  
The range-free algorithm has several advantages: it is low cost and has low power consumption and simple hardware. DV-Hop localization algorithm has become an economical yet exceptionally important localization algorithm due to its simple algorithm, high coverage, and good feasibility that converts the estimated distance between nodes into the product of hop-count value and average distance per hop [34; 32; 33]. Even so, the disadvantage is that the localization accuracy is not good enough; accordingly, improvements in the localization accuracy are the key issues to this research. In this section, we introduce some related work of the DV-Hop localization algorithm.  
[PERSON] et al. proposed to estimate the distance of hop-count based on the value of neighbors in the same block [35]; to reduce the localization error, this study used weighted node distance to calculate the final location of nodes. [PERSON] et al. used RSSI values to estimate the distance between the anchor node and the adjacent sensor node in one hop; such an estimated distance is applied to replace the average distance per hop in the original algorithm [31]. As a sensor node is located, it is elevated to act as an additional anchor node for subsequent use in other sensor nodes since the availability of the additional anchor node improves the accuracy of locating the remaining sensor nodes. [PERSON] et al. proposed a DV-Hop algorithm based on local weighted linear regression [35], where the unknown nodes attempt to estimate their locations by using nearby anchor nodes that maintain the proportional relationship between the geometric distance and the average distance per hop. The kernel method to improve the localization accuracy by increasing the weight of adjacent anchor nodes is applied.  
Besides traditional optimization algorithms, many meta-heuristic algorithms to improve the localization algorithm have been considered, as they are competent to increase the accuracy of the original localization algorithm. [PERSON] et al. proposed a genetic-based localization algorithm to improve the localization accuracy and convergence speed, limiting the feasible population region when the initial population was created. Three anchor nodes as the center to construct three squares are applied, in which the shadow area of the intersection of three squares is the overall workable area of the unknown nodes, and the initial population is generated randomly in the functional area. [PERSON] et al. applied an improved algorithm for localization and then used particle swarm optimization to refine the results [36]. [PERSON] et al. proposed an improved localization algorithm based on hybrid chaotic strategy [10]. [PERSON] et al. used the differential evolution algorithm to minimize localization error in wireless sensor network [12], while [PERSON] et al. improved the values of hop-count by the values of common single-hop nodes between adjacent nodes and converted the discrete hop-count values into more accurate continuous values [37]. The differential evolution algorithm is introduced to achieve the optimal global solution corresponding to the estimated location of unknown nodes although it induces vast time overhead and energy consumption while improving the localization accuracy.