Localization of Sensor Nodes in 3D Wireless Sensor Networks with a Single Anchor by an Improved Adaptive Artificial Bee Colony (iaABC) Algorithm

In terms of optimization, one of the core challenges in Wireless Sensor Networks is determining the locations of nodes. While simulating this problem in a 3D environment instead of the traditional 2D increases problem complexity, it is crucial for accurately representing real-world scenarios. Furthermore, the success of locating moving nodes in a 3D space is closely linked to the overall efficiency of the network. This study proposes a solution that can detect the locations of target nodes at various levels using a single anchor node. The method employs the Improved Adaptive Artificial Bee Colony (iaABC) algorithm, a model of the classical ABC algorithm. This improvement updates the control parameter values during the scanning, allowing the algorithm to focus its search direction on better exploitation. The performance of the search and convergence of this method was tested on CEC 2022 test suits. The CEC 2022 benchmark functions have more up-to-date content and are fairer because they utilize the same initial solutions for each competing algorithm. Subsequently, the approach was used to determine node locations. The results demonstrated that iaABC can locate 100 target nodes with a single anchor in a 3D environment.