An Enhanced WSN Node Localization Approach in IoT

With the development of satellite communication technology, the integration of satellite communication into the satellite Internet of Things(Satellite IoT) is gradually becoming a key technology for achieving global coverage and seamless connectivity. Among these technologies, wireless sensor networks (WSN), as a core component of the satellite IoT, play an indispensable role in real-time data collection and processing. However, the positioning accuracy of WSN nodes still faces challenges such as complex environments and signal interference. To address the issue of low positioning accuracy of WSN nodes, this paper proposes an improved heterogeneous Ivy algorithm integrated with Kalman filtering (KF-HIvy). This approach, combined with the three-anchor centroid method and expected hop progress(EHP), aims to reduce WSN node positioning errors. The proposed method first uses the three-anchor centroid method and EHP to estimate the distance of unknown nodes, and then applies the KF-HIvy algorithm for node position estimation. Simulation experiments on WSN show that the KF-HIvy algorithm achieves low positioning errors under different WSN configurations, demonstrating strong adaptability in various scenarios, and significantly improving the stability and reliability of node positioning.