Uncertain data clustering-based distance estimation in Wireless Sensor Networks.

Area of Science:

• Wireless Sensor Networks
• Signal Processing
• Data Mining

Background:

• Received Signal Strength Indicator (RSSI) is commonly used for distance estimation in Wireless Sensor Networks (WSNs).
• The linear relationship between RSSI and the logarithm of communication distance is often assumed but inaccurate due to real-world uncertainties like obstacles and interference.
• Existing methods struggle to account for the inherent variability and uncertainties in RSSI readings.

Purpose of the Study:

• To propose a novel RSSI-based communication distance estimation method for WSNs.
• To address and overcome the uncertainties in RSSI readings.
• To enhance the accuracy and efficiency of distance estimation in WSNs.

Main Methods:

• Utilized interval data and statistical information of RSSI values to characterize RSSI distribution.
• Applied interval data hard clustering to manage specific levels of RSSI uncertainty.
• Employed interval data soft clustering to address broader RSSI uncertainties.
• Evaluated the method using real RSSI measurements in diverse wireless environments.

Main Results:

• The proposed method demonstrated effective communication distance estimation.
• Achieved promising estimation accuracy compared to existing state-of-the-art approaches.
• Showcased high efficiency in practical WSN scenarios.

Conclusions:

• The novel interval data clustering approach effectively handles RSSI uncertainties in WSNs.
• The method provides a more accurate and efficient solution for communication distance estimation.
• This technique offers a robust alternative for WSN localization and network management.