Deep Learning-Based Transmitter Localization in Sparse Wireless Sensor Networks.

Area of Science:

• Wireless Communication
• Sensor Networks
• Deep Learning

Background:

• Accurate transmitter localization is vital in wireless communication.
• Existing localization methods struggle in sparse wireless sensor networks (WSNs) due to limited sensor distribution.
• Challenges in WSNs necessitate advanced localization techniques.

Purpose of the Study:

• To introduce DSLoc, a novel deep learning-based approach for transmitter localization in sparse WSNs.
• To enhance localization accuracy and reduce miss rates in challenging WSN environments.
• To provide a robust solution for transmitter tracking in sparsely deployed sensor networks.

Main Methods:

• Utilized an improved high-resolution network model within a deep learning framework.
• Designed efficient feature enhancement modules to address sparse network conditions.
• Employed an image centroid-based method for locating transmitter positions on heatmaps.

Main Results:

• Demonstrated significant improvements in localization accuracy, more than doubling performance compared to existing deep learning models.
• Achieved a substantial reduction in the transmitter miss rate.
• Validated performance on WSNs with a very low deployment density (0.01%).

Conclusions:

• DSLoc offers superior accuracy and robustness for transmitter localization in sparse WSNs.
• The proposed deep learning approach effectively overcomes limitations of traditional methods in challenging environments.
• DSLoc represents a significant advancement in wireless sensor network localization technology.