# Fiber Sensing in the 6G Era: Vision Transformers for \(\phi\)-OTDR-Based Road-Traffic Monitoring
## 1 Introduction
Leveraging the recent developments in image processing, this analysis has been accomplished by deep learning (DL) techniques [24]. In [25], [PERSON] et al. demonstrate simultaneous sensing and data transmission over the installed fiber infrastructure. The \(\phi\)-OTDR and data traffic signals are counter-propagated to avoid unwanted nonlinear effects, with the backscattered signal collected in the same direction as the transmitted data. \(\phi\)-OTDR traces are concatenated to generate images that illustrate traffic evolution over distance and time. These images are then processed and analyzed using data annotation and support vector machines (SVMs) to estimate the position, speed, weight, and traffic density. In [26], [PERSON] et al. apply the Hough Transform over images created from \(\phi\)-OTDR traces to determine the car count and average speed. In [27], [PERSON] et al. propose the generation of synthetic data as a solution for the lack of \(\phi\)-OTDR experimental data for traffic monitoring. In [28], [PERSON] et al. propose a framework for protecting optical cables using optical fiber monitoring. In [29], [PERSON] et al. discuss architectures and applications of \(\phi\)-OTDR. In [30], [PERSON] et al. review the progress and limitations of \(\phi\)-OTDR in the industry. In [31], [PERSON] et al. extensively discuss the challenges of using legacy cables for sensing and its applications. In [32], we evaluate DFOS in the context of smart cities using the MobileNetV2 [33] convolutional neural network. The deployed experimental setup is evaluated in [34]. Despite the extensive literature on machine learning for \(\phi\)-OTDR trace analysis, the absence of open datasets hampers the ability to effectively benchmark and compare these different techniques. In this paper, we provide a labeled dataset collected from a \(\phi\)-OTDR experimental setup built at the university campus, as shown in Figure 2. Unlike most traffic monitoring studies that focus on highways, our work addresses local traffic, as typically found in smart city environments. The ground truth is established using a video camera monitoring the same road where the \(\phi\)-OTDR is installed. Furthermore, we extend [32] by applying the novel vision transformer (ViT) [35] model for traffic parameter estimation in smart cities, comparing its performance with MobileNetV2. The choice of ViT and MobileNetV2 was based on algorithm efficiency over the ImageNet dataset, considered a benchmark for image classification. The collected \(\phi\)-OTDR data are evaluated by the ViT and MobileNetV2 models for vehicle counting and average speed estimation, enabling traffic engineering and road-safety applications. Figure 1: Traffic monitoring scheme with \(\phi\)-OTDR-based DFOS. Data are simultaneously collected from DFOS system and video camera. Concatenated \(\phi\)-OTDR traces form images along time (\(dt\)) and distance (\(dx\)), allowing vehicle density counting and speed estimation through line slope. Figure 2: Waterfall traces corresponding to two vehicles (**a**). The respective video frames (**b**). Monitored fiber path on the university campus (**c**). The remainder of this manuscript is structured as follows. Section 2 introduces the ViT and MobileNetV2 models. Section 3 presents the experimental setup, including the \(\phi\)-OTDR and image data collection setup and the camera-based validation setup. Section 4 presents the results. Lastly, Section 5 concludes the paper.