An enhanced localization algorithm for 3D wireless sensor networks using group learning optimization

Localization in sensor communication is considered one of the most foundational concepts that facilitates targeted monitoring, optimized deployment, and real-time navigation. The localization algorithms have several applications, including asset tracking, environmental monitoring, industrial automation, and other location-based services. This drives the need for continually refining and enhancing localization techniques for three-dimensional sensor networks. The DV-Hop is a widely used localization technique owing to its lesser range requirements, easy to implement, and suitable for large-scale network of sensors. In this research, we have proposed an enhanced group learning optimization-based three-dimensional DV-Hop algorithm, termed as GL-3DDVHop. The proposed method overcomes the limitations of the original variant of DV-Hop and extended it to three-dimensional environment. In the proposed approach, the communication ring partitioning-based location aware node selection approach is developed to calculate the hopsize of location aware node. The correction factor for hopsize refinement is also added to obtain the corrected distances between location unaware node and location aware nodes in terms of the modified hopsize and hop count. Finally, group learning optimization technique is used to estimate the position coordinates of location unaware nodes. According to our experimentation conducted for 3D wireless sensor network, the localization accuracy of GL-3DDVHop surpassed its existing counterparts, namely 3DDV-Hop and PSO-3DDVHop techniques by 9% and 3%, respectively.