Impact of Mobile Anchor Path Patterns on Wireless Sensor Network Localization Performance

Introduction: Mobility-assisted localization remains a critical challenge in wireless sensor networks (WSNs), particularly for accurately determining sensor node positions. A localization framework that utilizes a single mobile anchor node (MAN) has been proposed in this paper to enhance localization performance in WSNs. The paper also investigates the effect of multiple MAN path-planning models on localization accuracy and computation time. Methods: The proposed approach integrates two error minimization techniques—trilateration and the Harmony Search Algorithm (HSA)—to estimate sensor node positions. Five distinct MAN path-planning models are investigated: Random, Scan, Double Scan, Hilbert, and Circular. These models define the MAN's trajectory, and the resulting anchor points are used as inputs for distributed localization using both trilateration and HSA. MATLAB simulations are conducted to evaluate the framework based on localization accuracy, node coverage, and computational time. Results: Simulation outcomes indicate that the Hilbert path-planning model achieves the highest localization accuracy and node coverage among all trajectories. Furthermore, the HSA-based localization method surpasses trilateration in terms of precision by effectively minimizing localization error, though it requires more computational time. Discussion: The findings reveal a clear trade-off between localization accuracy and computational efficiency. While HSA provides enhanced precision, it incurs a higher computational cost compared to trilateration. These results underscore the importance of selecting appropriate pathplanning and optimization strategies to balance performance metrics in real-world WSN deployments. Conclusion: The study demonstrates that the combination of MAN-based mobility, Hilbert pathplanning, and HSA optimization yields superior localization performance in WSNs. These insights contribute to the development of efficient and accurate localization strategies suitable for dynamic and resource-constrained wireless sensor environments.