Deep Learning-Based Transmitter Localization in Sparse Wireless Sensor Networks

Author:

Liu Runjie12,Zhang Qionggui12,Zhang Yuankang1,Zhang Rui3,Meng Tao3

Affiliation:

1. National Supercomputing Center in Zhengzhou, Zhengzhou 450001, China

2. School of Computing and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China

3. Zhengzhou Radio Center, Henan Provincial Department of Industry and Information Technology, Zhengzhou 450051, China

Abstract

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 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 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.

Funder

Key scientific research project of colleges and universities in Henan Province, China

Henan Province major science and technology project

Publisher

MDPI AG

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