Blind RSSD-Based Indoor Localization with Confidence Calibration and Energy Control.

Area of Science:

• Computer Science
• Electrical Engineering
• Ubiquitous Computing

Background:

• Indoor localization is crucial for applications like elderly care, miner security, and smart buildings.
• Wireless Sensor Networks (WSNs) are a key technology for enabling these localization services.
• Accurate and energy-efficient localization remains a challenge in WSNs.

Purpose of the Study:

• To develop an accurate and energy-efficient indoor localization method for WSNs.
• To address challenges of unknown transmission information and measurement noise.
• To prolong the operational lifetime of WSNs through energy conservation.

Main Methods:

• Utilizing Received Signal Strength Difference (RSSD) for target localization.
• Implementing a confidence value for anchor nodes to improve estimation accuracy.
• Employing an automatic calibration device for acquiring node credibility values.
• Integrating an acceleration sensor and Unscented Kalman Filter (UKF) to mitigate measurement noise.
• Adopting a quadtree structure for energy-efficient anchor node management.

Main Results:

• The proposed method achieves high localization accuracy.
• Significant reduction in energy consumption is demonstrated.
• The system effectively handles unknown transmission parameters.
• Noise reduction techniques improve localization reliability.

Conclusions:

• The developed RSSD-based localization method offers a promising solution for WSNs.
• The integration of confidence values, noise reduction, and energy control mechanisms enhances system performance.
• The system is suitable for real-time implementation on embedded platforms, balancing accuracy and energy efficiency.