Localization Based on MAP and PSO for Drifting-Restricted Underwater Acoustic Sensor Networks

Area of Science:

• Marine engineering
• Robotics
• Sensor networks

Background:

• Localization is crucial for Underwater Acoustic Sensor Networks (UASNs).
• Existing algorithms struggle with underwater challenges like node mobility and distance-varying noise.
• Beacon-based methods are common but have limitations in harsh environments.

Purpose of the Study:

• To propose a novel beacon-free localization algorithm for drifting-restricted UASNs.
• To address challenges of node mobility and measurement noise in underwater environments.
• To improve localization accuracy and energy efficiency in UASNs.

Main Methods:

• Developed a two-step algorithm: MAP estimation and Particle Swarm Optimization (PSO) localization.
• MAP estimation analyzes mobility patterns for prior knowledge and models distance measurements with noise.
• PSO localization uses a particle swarm to find optimal solutions, incorporating reference selection and bound constraints.

Main Results:

• The proposed MAP-PSO algorithm demonstrates high localization accuracy.
• MAP-PSO significantly reduces energy consumption compared to benchmark methods.
• Performance was evaluated under various settings, with optimal parameters identified.

Conclusions:

• MAP-PSO offers a robust and efficient solution for localization in challenging UASN environments.
• The algorithm effectively handles node mobility and environmental noise.
• It provides a promising alternative to traditional beacon-based localization techniques.