## 1 Introduction  
Wireless Sensor Networks (WSNs) are composed of sensors, or nodes, that measure and collect environmental data, such as temperature, light, noise, and humidity, within a specific geographical area. These nodes process the collected data and transmit them to a central point for further analysis. Randomly deployed sensors in static or mobile conditions are wireless network elements that relocate their positions to collect data on sudden changes in physical factors. A typical sensor is an electronic device consisting of a processing unit, single or multiple sensing elements, a mobility module or/and a position tracking module, and a power unit [1]. The sensors also are capable of wireless communication and signal processing. Therefore, they can be installed, self-organized, and adapted to other applications without infrastructure or human intervention. Due to these advantages, WSNs are used in different disciplines and various projects. In [2], WSN applications are examined in six main categories: military, health, environmental, flora and fauna, industrial, and urban. Studies in the field of optimization generally focus on location tracking, routing [3], sensor placement, positioning [4], energy conservation [5], clustering [6, 7], and synchro nization [8]. Without a determined node location, collected sensing information is meaningless [9]. Due to the high number of sensors in WSNs, methods like GPS cannot be used for location determination because of their high cost. Instead, to determine the locations of undetermined nodes, reference (anchor) nodes, which are typically connected to GPS, are utilized. Finding sensor locations using other regular sensors and anchors has emerged as a new approach to collaborative location determination [1]. Various localization techniques are introduced in the literature to determine the precise location of nodes with undetermined locations (target nodes) by using several anchor nodes whose location is known in advance. The initial studies focused on designing two-dimensional simulations for WSNs. In subsequent research, three-dimensional scenarios were developed for the nodes. Positioning the nodes in a three-dimensional environment increased the complexity of the problem. However, when designing a simulation that represents a 3D real-world environment, the localization algorithm needs to ensure enough accuracy in node placement. However, theoretical research on the 3D node localization method of WSNs is still in its infancy [10]. In static WSN scenarios, localization can be performed accurately for target nodes with unknown locations. However, when the target nodes are mobile, localization becomes quite challenging. This study focuses on developing a solution to determine the location of moving target nodes in 3D Wireless Sensor Networks (WSNs) using a single anchor node that is fixed and has a known location. Section 2 presents the proposed solutions developed for localization in WSNs with different characteristics in the literature. However, the study in [11] stands out as it specifically addresses a 3D environment with a single anchor, emphasizing the \"umbrella approach\" and the projection of virtual anchors. Therefore, this study adopted the approach explained in detail in [11], and the simulation was prepared based on this methodology. For anchor nodes, a hexagonal projection is created and the projection of these virtual nodes in six directions is used. At least four reference points are used to determine the coordinates of a node in a 3D environment. In this context, when the target nodes fall within the projection range, the location of these target nodes is estimated using the anchor node and three selected virtual nodes. It is important to note that localization in three dimensions requires a minimum of four non-coplanar anchor nodes. The main contributions of this study to [11] can be listed as follows:  
* The location has been identified for more moving nodes. In this context, the approach is proven to be scalable for a large-scale WSN.