## 2 Related Work
### Mobile Anchor--Static Node  
In this approach, a node with locational information called MA broadcasts this information periodically, allowing the UNNs locations to be found according to some localization techniques (e.g., RSSI, TOA, or TDOA). The total cost in the network will be reduced by using a single anchor node [18]. The classification of the paths in the approaches finding UNNs with the MA movement and detailed information about these paths are given in the following subsection. Mobile Anchor Paths  
The studies conducted to design an optimum path model in the literature are categorized in [11; 19]. Path models are divided into two main classes as random and planned models, as seen in Figure 3. Random models are often used in applications where high localization errors or high coverage performances are not required. For example, the RWP model is random path mobility in which the direction and speed of the MA are changed randomly, first proposed for routing in ad hoc networks and then modified for use in localization models [20]. Random Direction (RD) and Reference Point Group Mobility (RPGM) are other examples of random path planning models [21]. Planned models can be dynamic or static. In dynamic path planning models, the path is constantly updated with the information obtained on the path according to the need in the application. Therefore, there is no predetermined way. Dynamic Path of Mobile Beacon (DPMB) [22], Breadth-First (BRF) [23], and Deterministic Branch Mobility Scheduling (DREAMS) [24] are some examples of dynamic path planning models in WSNs. These models have a large number of applications in different fields. Some of these are applications such as mobile robots [25; 26], autonomous underwater vehicles [27], and autonomous driving [28]. Figure 3: Different path models for MANAL in WSNs. Figure 2: Classification of localization schemes in WSNs [17]. Studies on designing the path followed by MAs include SCAN, DOUBLE-SCAN, and HILBERT [19], localization algorithm with a mobile anchor node based on trilateration (LMAT) [29], Z-curve [9], H-curve [11], and M-curves [10]. These models can be evaluated in the \"Static Mobility\" subclass of \"Planned Mobility\". The model developed in this paper is also a static path planning model because the path, starting point, and endpoint to be followed by the MA are determined before the localization process. These were designed before the MA was released to the ROI. There is no external intervention with respect to the trajectory it follows while the MA is in movement. Intervention while the MAs are moving belongs to the \"Dynamic Mobility\" class. As the primary goal of static path models is to localize all UNNs, generally speaking, these models have high localization rates and offer low error values compared to other mobility models. Most static path planning models in the literature are based on trilateration and triangulation techniques. Therefore, the designed path should be planned so that the MA provides at least three different location information to the UNNs. However, when a UNN receives a single location information message from one direction or receives multiple location information messages from a different direction on a line, the collinearity problem arises. This is one of the main problems in static path planning models. In [19], SCAN, DOUBLE-SCAN, and HILBERT path planning models were proposed. These models are known as the first mobile-assisted static path planning models in the literature. In the HILBERT path planning model, the idea of making more turns in the trajectory is proposed to solve the collinearity problem of SCAN and the path length problem of DOUBLE-SCAN. For this purpose, the ROI is divided into four squares of equal size, and the points between the squares are joined. UNNs can estimate their location more accurately than the SCAN model using different non-collinear location information from the MA. However, one of the most critical problems of the HILBERT model is the problem of coverage.