A Two-Step SD/SOCP-GTRS Method for Improved RSS-Based Localization in Wireless Sensor Networks

Area of Science:

• Wireless communication
• Sensor networks
• Localization algorithms

Background:

• Accurate wireless localization is vital for sensor networks and smart city applications.
• Signal attenuation and noise challenge localization precision.
• Existing methods struggle with performance degradation.

Purpose of the Study:

• To propose a novel two-step localization framework, SD/SOCP-GTRS, for enhanced target localization precision.
• To improve accuracy in wireless sensor networks using received signal strength (RSS) measurements.
• To mitigate performance degradation caused by convex relaxation in localization algorithms.

Main Methods:

• A two-step framework: SD/SOCP for initial estimation and GTRS for refinement.
• Utilizes semidefinite programming (SDP) and second-order cone programming (SOCP) for convex relaxation.
• Employs weighted least squares (WLS) and generalized trust region subproblem (GTRS) for refined estimation.

Main Results:

• The SD/SOCP-GTRS approach significantly reduces root mean square error (RMSE).
• Demonstrates superior performance compared to existing localization techniques.
• Approaches the theoretical performance limit for wireless localization.

Conclusions:

• SD/SOCP-GTRS offers a robust solution for high-precision wireless localization.
• The framework effectively overcomes challenges posed by signal attenuation and noise.
• Provides a significant advancement for wireless sensor network applications.