Node Localization Method for Wireless Sensor Networks Based on Quantum Zebra Bullhead Shark Algorithm

Distance Vector Hop method (DV-Hop) spurs improvement research due to its innovative non - ranging idea. The purpose of this paper is to propose an efficient node localization method for Wireless Sensor Networks (WSNs) based on a Quantum Zebra Bullhead Shark Algorithm (QZBSA-Dv-Hop) integrated with a leap distance correction strategy. The QZBSA-Dv-Hop is inspired by the cooperative foraging behavior of zebra bullhead shark and enhanced by a simulated quantum rotation gate, enabling global exploration with fast convergence and strong stability. Through dynamic position updating and adaptive quantum evolution, the algorithm effectively avoids local optima and achieves high localization precision. The leap distance correction strategy further refines distance estimation by utilizing anchor node information, ensuring that estimated distances closely approximate actual network topology. This method achieves accurate, robust, and energy-efficient localization under complex and dynamic network environments. Its superior convergence performance and adaptability make it well suited for large-scale WSN deployment and real-time engineering applications, providing a reliable foundation for intelligent perception, environmental monitoring, and autonomous control in practical wireless sensor systems.