# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 3 Inform@Risk Monitoring System
### Measurement Concept for the LoRa Sensor Nodes  
A modular design has been chosen for the Inform@Risk LoRa sensor nodes. While all nodes share the same central hardware, as, e.g., the microprocessor, some basic sensors and the LoRa communication hardware (Inform@Risk LoRa base module), additional components can be added depending on the requirements of the specific monitoring task and installation location. This especially applies for the power supply (solar, battery and external power source) and optional sensor attachments. While the LoRa base module in general offers the flexibility to attach a wide range of different sensors, two specific sensor attachments have been designed for the use in the Inform@Risk LEWS. As a consequence, three types of sensor nodes can be distinguished which mainly build up the LoRa geosensor network of the LEWS: the Infrastructure Node (IN), the Subsurface Node (SN) and the Low-Cost Chain Inclometer (LCI). While the IN only consists of the base module (with the option to attach further sensors based on need), the SN and the LCI have specifically designed sensor attachments. Each type of node serves a different measurement purpose: The IN is designed to be attached directly to existing infrastructure on site (e.g., buildings, posts, retention walls) and to monitor their movement/deformation. This is mainly done based on high accuracy inclination measurements utilizing an integrated MEMS sensor. When needed, additional sensors such as crackmeters or extensometers can be added to improve the quality of observation.  
The LCI and SN both are designed to monitor subsurface deformation and ground- or seepage-water levels. While the LCI is based on the principle of inclinometer measurements and allows to quantify horizontal displacement, the SN only delivers a qualitative indication and possibly a semi-quantitative assessment of deformation through time. On the other hand the SN allows to perform highly resolved open pipe groundwater-level measurements based on a buoyancy rod, while the LCI utilizes a series of float switches usually with 0.5 or 1 m distance, thus delivering a much lower resolved information on groundwater levels.  
As the requirement for the successful implementation of a LCI sensor probe is to penetrate the basal shear surface of a landslide, these probes have to be installed into greater depths than the SN. The LCI consists of a series of inclination sensors (0.5 or 1 m interval) installed into an easily deformable PVC pipe, while the SN only has one inclination sensor and is installed into a stiff steel pipe. As the LCI penetrates through the sliding mass and the bottom lies in the stable subsurface, the tilt observed by the chain of inclination sensors can be used to calculate a profile of horizontal displacement throughout the subsurface, allowing to determine the depth and amount of deformation in the landslide. Provided the movement is a rotational slide, the SN will tilt upwards if it \"floats\" in the sliding mass, and tilt downwards if the SN reaches stable ground (Figure 2). As the exact geometry of the landslide is unknown, SN measurements can only semi-quantitatively monitor landslide movement. These mechanics are also described by [PERSON] et al. (2020) who explored the direction and pre-failure tilting behavior of slopes and how this affects measured tilting angles of steel rods of different sizes [60; 67].  
As stated earlier, the sensor placement should generally be adapted according to the previously performed risk analysis. Additionally, it is important that each individual sensor location is checked before the installation to ensure that the sensor will be able to detect and measure a possible landslide. Therefore, it is also compulsory that each node location and installation is documented in detail, so it is possible to interpret the data in a meaningful way. In order to facilitate this process, we plan to add an interactive sensor installation guide and documentation feature into the Inform@Risk app.