Optimizing energy constrained target localization and tracking with radial bias and seeker optimization algorithms in...

Area of Science:

• Wireless Sensor Networks
• Localization and Tracking
• Optimization Algorithms

Background:

• Standard localization methods use fixed anchor node positions, limiting adaptability to deployment environments.
• Energy constraints pose significant challenges for target localization and tracking in wireless sensor networks (WSNs).

Purpose of the Study:

• To propose a novel approach for energy-constrained target localization and tracking by combining Radial Bias (RB) and the Seeker Optimization Algorithm (SOA).
• To enhance localization accuracy and optimize sensor deployment and data transmission for minimal energy consumption.

Main Methods:

• Integration of Radial Bias (RB) for refining target position estimates.
• Application of the Seeker Optimization Algorithm (SOA) to optimize sensor deployment and data transmission paths.
• Evaluation of the proposed Radial Bias-Seeker Optimization Algorithm (RadB_SOA) across various particle counts (5, 10, 15, 20, 25).

Main Results:

• The RadB_SOA approach achieves a balance between tracking precision and energy efficiency.
• Achieved metrics include 12.4% transmission error, 14.6% ranging error, 96.3% localization coverage, 98.65% Packet Delivery Ratio (PDR), and 21.56% energy consumption.
• Reduced particle count in the algorithm accelerated positioning and tracking while maintaining accuracy.

Conclusions:

• The proposed RadB_SOA significantly enhances target localization precision and optimizes energy usage in WSNs.
• Simulation results demonstrate superior tracking accuracy, reduced errors, and increased localization coverage compared to existing methods.
• The study confirms that optimizing particle count enhances network efficiency, positioning speed, and localization precision.