Range-free localization using Reliable Anchor Pair Selection and Quantum-behaved Salp Swarm Algorithm for anisotropic Wireless Sensor Networks

Abstract

Localization is one of the essential problems in the Internet of Things (IoT) and other wireless sensor applications. Most traditional range-free localization algorithms ignore the anisotropy factors, which are frequently observed in Wireless Sensor Networks (WSNs) and result in low positioning precision. To mitigate the impact of anisotropy on localization, we propose an accurate localization approach based on Reliable Anchor Pair Selection (RAPS) and Quantum-behaved Salp Swarm Algorithm (QSSA) for anisotropic networks. First, the proposed algorithm uses hop count threshold to limit the number of message transmissions between nodes, which assists to decrease communication overhead. Next, based on the geometric constraints, the selected reliable anchor pairs are divided into two types, namely, super anchor pairs and suboptimal ones. Then, we design different distance estimation equations for the reliable anchor pairs to reduce ranging error. Finally, the QSSA is introduced to calculate the coordinates of regular nodes, which tends to lower the impact of anisotropy factors and improve location accuracy. Extensive simulations show that the proposed algorithm outperforms state-of-the-art algorithms in terms of accuracy and robustness against network anisotropy.