LSRR-LA: An Anisotropy-Tolerant Localization Algorithm Based on Least Square Regularized Regression for Multi-Hop...

Area of Science:

• Wireless Sensor Networks
• Localization Algorithms
• Network Topology

Background:

• Multi-hop range-free localization algorithms perform well in isotropic networks.
• Anisotropic network topologies significantly degrade localization accuracy in existing algorithms.

Purpose of the Study:

• To enhance localization performance in anisotropic wireless sensor networks.
• To address the limitations of traditional algorithms affected by network topology.

Main Methods:

• Developed a novel multi-hop range-free localization algorithm utilizing Least Square Regularized Regression (LSRR).
• Established a mapping model between hop counts and geographical distances using LSRR.
• Applied the mapping to determine sensor node positions.

Main Results:

• The proposed LSRR-based algorithm effectively mitigates the impact of network anisotropy.
• Experimental evaluation using Average Localization Error (ALE) demonstrates significant improvements in positioning accuracy compared to similar methods.

Conclusions:

• The LSRR-based approach offers a robust solution for accurate localization in challenging anisotropic network environments.
• This method provides a considerable improvement in positioning accuracy for wireless sensor networks.