A Modified Differential Evolution for Source Localization Using RSS Measurements

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless sensor networks (WSNs) face localization challenges due to non-convex and nonlinear maximum likelihood estimation problems using received signal strength (RSS).
• Differential evolution (DE) is effective for multimodal cost functions but suffers from slow convergence and local optima.
• Existing localization techniques like semidefinite programming and linear least squares have limitations in complex WSN scenarios.

Purpose of the Study:

• To propose a novel enhanced differential evolution (DE) algorithm integrated with opposition-based learning (OBL) for improved WSN localization.
• To address the limitations of conventional DE, specifically suboptimal convergence rates and susceptibility to local optima.
• To enhance positioning precision and convergence speed in WSN localization using RSS measurements.

Main Methods:

• Integration of opposition-based learning (OBL) principles into the differential evolution (DE) framework.
• Introduction of an adaptive scaling factor to balance global exploration and local exploitation.
• Development of a penalty-augmented cost function to utilize boundary information and eliminate explicit constraint handling.

Main Results:

• The proposed enhanced DE algorithm demonstrates significant improvements in convergence speed and positioning precision compared to state-of-the-art methods.
• Comparative evaluations show superiority over semidefinite programming, linear least squares, and simulated annealing.
• Experimental results validate the algorithm's robustness and effectiveness under various noise conditions and network configurations, especially with sparse anchor nodes.

Conclusions:

• The proposed opposition-based learning enhanced differential evolution (OBL-DE) offers a superior approach for WSN localization.
• The adaptive scaling factor and penalty-augmented cost function effectively improve performance in complex and uncertain environments.
• This enhanced DE method provides a robust and accurate solution for received signal strength-based localization in wireless sensor networks.