Energy efficient and robust node localization in WSNs using LSTM optimized DV hop framework to mitigate multihop...

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless Sensor Networks (WSNs) are critical for data collection and analysis.
• Accurate sensor node localization is essential for WSN functionality but remains challenging.
• Existing localization algorithms often struggle with error distribution and accuracy.

Purpose of the Study:

• To introduce an improved DV-Hop positioning algorithm for WSNs.
• To enhance node position prediction accuracy using Long Short-Term Memory (OLSTM-DVHop) networks.
• To address and correct uneven error distribution in WSN localization.

Main Methods:

• Developed an improved DV-Hop algorithm integrating OLSTM networks.
• Implemented data filtering, analysis, and feature extraction for position prediction.
• Designed a robust architecture for WSN positioning with an error correction mechanism.

Main Results:

• The OLSTM-DVHop algorithm demonstrated significant reduction in localization errors.
• The proposed error correction mechanism effectively adjusted node positions with deviations.
• Simulation experiments validated the algorithm's improved reliability and accuracy across various terrains.

Conclusions:

• The enhanced OLSTM-DVHop algorithm offers superior accuracy and reliability in WSN localization.
• The algorithm outperforms existing localization methods in diverse experimental scenarios.
• This advancement contributes to more effective WSN positioning systems.