## 2 Related Works  
Localization in WSNs has attracted attention and several techniques and algorithms have been proposed in the recent years [4]. These techniques differ based on the objective in a number of ways such as the localization method (range-based, range-free), localization processing (distributed, centralized), deployment areas (two-dimensional, three-dimensional), application areas (outdoor, indoor), or anchor types (static, mobile). Generally, localization models using anchor nodes are classified under one of the following types: (static nodes and static anchors), (mobile node and static anchors), (static nodes and mobile anchors) and (mobile nodes and mobile anchors) [8; 18]. Our proposed model falls under the third category; hence, our related work overview will be focused in such area. In (static nodes and mobile anchors), the movement of anchor typically follows one the three types: random, static or dynamic as described above. Based on the objective of the localization and the available sources (time, energy, etc. ), a movement strategy can be chosen. Since our technique focuses on static and dynamic movement path planning, we limit our discussion only to such [8; 10; 18; 19; 20; 21; 22; 23] techniques. SCAN and Hilbert [18] are two of the first static mobility models proposed for node localization in WSNs. Although they open a wide area of research, they both suffer from the collinearity problem, which affect negatively both the localization ratio and accuracy. In [19], another mobility model called, CIRCLES, is formed based on circular path movement. The main goal of CIRCLES is to overcome the collinearity problem. However, this model is unable to reach the nodes located in the corners of the network, thus, affecting negatively the localization ratio. This problem can be solved by adding more outer circles to the path, however, this will bring another issue about the path length. In order to overcome the collinearity problem, [PERSON] et al., proposed a mobile anchor-assisted localization algorithm based on a regular hexagon (MAALRH) [20]. MAALRH is a static path planning technique that starts from the centre of the network and its movement path is based on a hexagon-shape movement. However, simialr to CIRCLES, MAALRH is unable to localize some of the nodes especially those around in the corners of the monitored area. Another path planning technique, called Z-Curve was proposed in [24]. The path is designed as a set of connected lines forming \(Z\) shapes. Another localization algorithm based on trilateration called (LMAT) is proposed in [10]. In LMAT, the movement path is designed in a set of symmetrical triangles. This approach meant to avoid the collinearity. The trilateration concept is used to increase the accuracy of localization. In comparison to other similar models such as SCAN and MAALRH, LMAT provides an excellent performance in both accuracy and ratio. In [8], a new static model is proposed. The main focus of the designed path is avoiding collinearity while guaranteeing all UNs can receive the localization information from the MA. The model succeeded to provide competitively accurate estimation with a higher accuracy in comparison to other models. A dynamic model proposes using Breadth-First (BRF) and Backtracking Greedy (BTG) algorithms to transform the path planning problem into a spanning tree [25]. However, it suffers from high error rate [26]. The dynamic path of mobile beacon (DPMB) algorithm, presented in [21], divides the nodes into three classes; unknown, settled, and reference nodes. Each node contacts its neighbours that are located in its transmission range. The MA determines its path from node to node according the weight, which is represented as a quantity of neighbours for each unknown node. Considering an exceptional scenario where the MA movement is limited or constrained, the authors in [22] suggest using a multi-hop localization technique called DV-Hop to increase the localization ratio in static path planning.