Liu Runjie, Zhang Qionggui, Zhang Yuankang, Zhang Rui, Meng Tao
National Supercomputing Center in Zhengzhou, Zhengzhou 450001, China.
School of Computing and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China.
Sensors (Basel). 2024 Aug 18;24(16):5335. doi: 10.3390/s24165335.
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.
在无线通信领域，发射机定位技术对于实现精确的源跟踪至关重要。然而，现有的定位方法在无线传感器网络（WSN）中面临诸多挑战，特别是由于传感器在大面积区域稀疏分布所带来的限制。我们提出了DSLoc，一种基于深度学习的稀疏WSN中发射机定位方法。我们的方法基于神经网络中改进的高分辨率网络模型。为了解决稀疏无线传感器网络中的定位问题，我们设计了高效的特征增强模块，并提出使用基于图像质心的方法在热图中定位发射机位置。在部署密度为0.01%的WSN上进行的实验表明，与现有的深度学习模型相比，我们的方法显著降低了发射机遗漏率，并将定位精度提高了一倍多。结果表明，所提出的方法在稀疏WSN环境中提供了更准确、更稳健的性能。
