# Fiber Sensing in the 6G Era: Vision Transformers for \(\phi\)-OTDR-Based Road-Traffic Monitoring
## 1 Introduction  
It is envisioned that 6G will bring about a revolution in how we connect, communicate, and interact with the world. Artificial intelligence (AI) will be natively integrated into 6G systems to enable them to be smarter and more responsive [1]. Moreover, the integration of sensing into wireless communication systems, known as integrated sensing and communications (ISAC) [2] or joint communication and sensing (JCAS) [3], will allow 6G devices to progress beyond communication. ISAC will allow 6G devices to sense their surroundings for real-time applications such as autonomous driving and healthcare monitoring [4]. While much of the current research in this area focuses on the \"wireless\" medium for joint/integrated communication and sensing, this manuscript contributes to an expanded approach for 6G that also incorporates a \"wired\" medium infrastructure for sensing applications. Combining wired and wireless sensing modalities offers a multilayered approach to environmental perception, enhancing the accuracy, robustness, and coverage of 6G networks. For instance, the limitations of wireless sensing in challenging environments,such as tunnels, underground structures, or areas with dense foliage, can be effectively addressed by leveraging the resilience of optical-fiber-based sensing systems. Wired optical sensing [5] can provide valuable contextual information for wireless networks, enabling intelligent resource allocation, improved network management, and enhanced security. This integrated approach holds the potential to create a comprehensive digital twin representation [6] of the physical world, facilitating seamless interaction between the digital and physical realms envisioned for the 6G era. The ubiquity of fiber-optic networks in metropolitan areas makes them attractive not only for data transmission but also as a large sensing platform [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. Distributed fiber-optic sensing (DFOS) plays a critical role in achieving real-time monitoring capabilities for smart communities, enabling applications such as pipeline leakage monitoring, infrastructure health monitoring, maintenance, seismic monitoring, and support to transport infrastructure and mobility. Optical fibers installed along streets and avenues detect vehicle-induced vibrations, enabling the determination of vehicle position, speed, weight, and street parameters such as pavement quality [19; 20]. Conventional camera-based monitoring systems are frequently compromised by discretization and environmental factors such as snow, rain, fog, storms, inadequate lighting, and limited viewing angles, which result in the creation of blind spots. In contrast, DFOS-based monitoring, when deployed underground, is largely resistant to these events, with its performance predominantly influenced by seismic variations. Among the DFOS techniques, \(\phi\)-OTDR-based systems provide high sensitivity and long range. Typical \(\phi\)-OTDR systems can be implemented with direct or coherent detection, recovering the backscattered signal intensity, phase, or both [21; 22]. The direct detection approach is particularly suitable for short-range applications, requiring lower complexity with reasonable performance [23]. Coherent-detection options offer higher sensitivity at the cost of higher complexity. Given the low-cost requirements in the smart city application scenario, we focus this paper on a direct-detection solution. Figure 1 depicts an example of a \(\phi\)-OTDR-based DFOS system applied to traffic monitoring. Moving vehicles generate vibrations that cause time-varying phase perturbations in a fiber installed along the road. These phase perturbations are converted into backscattered time-varying amplitude changes, which are then detected by the DFOS interrogator. The patterns generated by the \(\phi\)-OTDR are used for event recognition and parameter estimation. The \(\phi\)-OTDR spatial-temporal information is transformed into image-like representations, enabling evaluation by computer vision algorithms.