An improved distance vector hop algorithm and A* algorithm with modified supernova optimizer for 3-dimensional localization in wireless sensor networks

Localization is crucial for accurate data interpretation in a wireless sensor network (WSN). The goal of localization is to locate the nodes with their respective coordinates acquired from anchor nodes. It helps to transfer the information via several nodes in WSN. In WSN, accurate node localization provides diverse benefits and enables large applications. Tracking the accurate position or location of the sensor nodes maximizes the system performance in WSN. However, attaining better localization in WSN is critical, because of the dynamic behavior of wireless communication in networks. Several localization algorithms and deep learning (DL) techniques have been developed to enhance the localization accuracy in WSN. These localization algorithms face challenges in several networks, particularly indoor communication; they consume more power. Evaluating the optimal value of anchor nodes, identifying scalability, and maximizing node localization in WSN are complex tasks. Distance Vector Hop (DV-Hop) is referred to as the non-ranging-aided 3D positioning approach with more errors and less positioning accuracy. Focusing on these difficulties, a framework for the 3D localization of DV-Hop (3D-DV-Hop) in the WSN is recommended in this work. Hence in this paper, the DV-Hop Algorithm and A* Algorithm are introduced to resolve the above-mentioned issues. With the implementation of WSN, the experiments of 3-dimensional (3D) node localization provide significant outcomes. The ideology of the A* algorithm and DV-Hop algorithm are integrated to enhance the node localization in WSN. The developed model consumes low power, low data-rate communication solutions, and minimal cost. The multi-objective optimization is carried out by Modified Random Value in Supernova Optimizer (MRV-SO) to locally optimize the node coordinates. This optimization process reduces the average localization error to improve its effectiveness. The comparative analysis of the developed MRV-SO model shows 89.971, 5.0612, and 2.8862 in terms of MASE, MAE, and RMSE. The simulation evaluation is carried out to ensure the recommended model attains better robustness and effectiveness. In this research paper, the free space propagation approach is used for 3D node localization.