## 1 Introduction  
The rapid growth of traffic demand has led to the increasing construction of rock tunnels, especially in China [1, 2]. Although tunnel boring machines have gained popularity in the last two decades due to the complex and uncertain geological conditions in rock tunnel engineering, the drilling and blasting method is still the most widely used construction process in rock tunnel excavation [3, 4]. The problem of large squeezing deformation of the excavated tunnel face is often encountered during the working progress in a rock tunnel [5] under high in situ stress or unfavourable geological conditions consisting of a weak surrounding rock mass. Therefore, grasping the tunnel face deformation during the rock tunnel construction process is an important research topic for civil engineering scholars [6, 7, 8]. During rock tunnel excavation, an unstable surrounding rock mass or potential collapse failure can delay the construction process and substantially increase the construction cost [9, 10, 11]. Monitoring and predicting the deformation of surrounding rocks in the rock tunnel construction process are of great significance. Site engineers often adopt a comprehensive monitoring scheme to obtain the surrounding rock deformation data of the tunnel and make engineering judgments based on the monitoring data to ensure the construction safety of the rock tunnel [12, 13, 14]. Accurate and in-time prediction of the tunnel face deformation can help the site engineers to conduct adequate treatment to maintain the stability of the excavated surrounding rock. The total station is a widely adopted monitoring tool in tunnel construction used to obtain the surrounding rock deformations. Reflecting targets are implemented in the tunnel linings, and then the site engineers perform regular measurements of the movements of those targets and then determine the deformation values. The frequency of monitoring tunnel deformation via the total station is limited to a few times a day due to the high labour cost and the heavy equipment [15; 16; 17]. However, a higher granularity of the deformation data is required to make in-time decisions during the rock tunnel construction process in unfavourable geological conditions. Therefore, an advanced monitoring method should be implemented to obtain higher granularity deformation data so that field engineers can better predict the stability of the exposed tunnel face [18; 19]. In practice, the robotic total station (RTS) is often adopted in the tunnel deformation monitoring scheme. Nevertheless, due to the high cost of the RTS equipment and lack of protection measures, the RTS monitoring locations are always at a certain distance away from the excavating tunnel face. Therefore, the ability of the RTS to measure the deformation data and predict the stability near the tunnel face is limited in rock tunnelling construction. Wireless sensor networks (WSNs) have received much research interest in recent years for their promising potential applications in health monitoring of civil infrastructure, such as in the railway industry [20; 21], pipeline inspection [22], and long-span bridges [23]. Sensors in the WSNs, often equipped with micro-electromechanical systems (MEMS) technology, can acquire and stably transmit high granularity monitoring data to the cloud server, which significantly assists the experienced engineers in the office to respond quickly. In recent years, WSNs have been used to monitor tunnel health status in the metro systems of many cities [24; 25; 26]. The deformations of the tunnel linings are often monitored in the operation stage to aid in the maintenance of the tunnel. Nevertheless, few studies have shown its application in rock tunnel construction. The underlying reason lies in the harsh construction environment and the dynamic construction process in the rock tunnel site. In addition, monitoring data obtained from the rock tunnel construction practice are often noisy and challenging to interpret.