Range-Free Localization Approach Based on Triple-Anchor Centroid and QAGWO for Anisotropic WSNs

The rapid development and integration of Wireless Sensor Networks (WSN) in the consumer electronics industry signify an important shift towards more intelligent and interconnected technologies. The localization of network nodes is essential for promoting intelligent automation and effective decision-making processes across a wide range of consumer applications. To achieve more precise intelligent localization, this paper proposes an algorithm based on Triple-Anchor Centroid and Expected Hop Progress (EHP) weighting to estimate the distance between sensing devices (regular nodes) and gateway devices (anchor nodes). The algorithm is a range-free positioning scheme that combines the geometric constraints between two devices and the advantages of EHP. And it uses the geometric centroid of the shadow part formed by the Triple-Anchor point to estimate devices distance. Then, the devices distance is weighted based on EHP estimation, and the experimental results show that the proposed method improves the accuracy of estimating distance. Finally, an improved Quantum-Adaptive Grey Wolf Optimizer (QAGWO) is proposed to estimate the coordinates of sensing devices. The step factor is proposed to balance the global search and local search capabilities to improve the search efficiency and performance of the algorithm. The simulation results show that the scheme is superior to other algorithms in terms of positioning accuracy, and the application scenarios are more extensive.