In recent years, low-power wide area networks (LPWANs), particularly Long-Range Wide Area Network (LoRaWAN) technology, are increasingly being adopted into large-scale Internet of Things (IoT) applications thanks to having the ability to offer cost-effective long-range wireless communication at low-power. The need to provide location-stamped communications to IoT applications for meaningful interpretation of physical measurements from IoT devices has increased demand to incorporate location estimation capabilities into LoRaWAN networks. Fingerprint-based localization methods are increasingly becoming popular in LoRaWAN networks because of their relatively high accuracy compared to range-based localization methods. This work proposes hybrid convolutional neural networks (CNNs)-transformer fingerprinting method to localize a node in a LoRaWAN network. CNNs are adopted to complement the strengths of the Transformer by adding the ability to capture local features from input data and consequently allow the Transformer, through the attention mechanism, to effectively learn global dependencies from the input data. Specifically, the proposed method works by first learning the local location features from the input data using the CNNs and passing the resulting information to the transformer encoder to learn global features from the input data. The output of the transformer encoder is then concatenated with information learned at the local level and then passed through the regressor for the final location estimation. With a localization performance of 290.71 m mean error achieved, the proposed method outperformed similar state-of-the-art works in the literature evaluated on the same publicly available LoRaWAN dataset.

中文翻译：

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长距离广域网中接收信号强度指示器指纹定位的混合卷积神经网络-变压器方法

近年来，低功耗广域网（LPWAN），特别是远程广域网（LoRaWAN）技术，由于能够提供成本低廉的服务，越来越多地被采用到大规模物联网（IoT）应用中。低功耗有效的远程无线通信。为了对物联网设备的物理测量进行有意义的解释，需要向物联网应用提供位置标记通信，这增加了将位置估计功能纳入 LoRaWAN 网络的需求。基于指纹的定位方法在 LoRaWAN 网络中越来越流行，因为与基于范围的定位方法相比，其精度相对较高。这项工作提出了混合卷积神经网络 (CNN)-变压器指纹识别方法来定位 LoRaWAN 网络中的节点。采用 CNN 来补充 Transformer 的优势，增加从输入数据中捕获局部特征的能力，从而允许 Transformer 通过注意力机制有效地从输入数据中学习全局依赖关系。具体来说，所提出的方法首先使用 CNN 从输入数据中学习局部位置特征，然后将结果信息传递给 Transformer 编码器，以从输入数据中学习全局特征。然后将变换器编码器的输出与在本地级别学习的信息连接起来，然后通过回归器进行最终位置估计。所提出的方法实现了 290.71 m 平均误差的定位性能，优于在相同公开可用的 LoRaWAN 数据集上评估的文献中类似的最先进的作品。