Deep Learning-Based Transmitter Localization in Sparse Wireless Sensor Networks

Deep Learning-Based Transmitter Localization in Sparse Wireless Sensor Networks

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In the field of wireless communication, transmitter localization technology is crucial for achieving accurate source tracking.However, the extant methodologies for localization face numerous challenges in wireless sensor networks (WSNs), particularly due to the constraints posed by the sparse distribution of sensors across large areas.We present DSLoc, a deep learning-based approach for transmitter localization in sparse WSNs.Our iphone 13 pro max price florida method is based on an improved high-resolution network model in neural networks.

To address localization in sparse wireless sensor networks, we design efficient feature enhancement modules, and propose to locate transmitter locations in the Sneakers for Men - Grey - Canvas Mesh Athletic Running Shoes heatmap using an image centroid-based method.Experiments conducted on WSNs with a 0.01% deployment density demonstrate that, compared to existing deep learning models, our method significantly reduces the transmitter miss rate and improves the localization accuracy by more than double.The results indicate that the proposed method offers more accurate and robust performance in sparse WSN environments.