# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 3 Inform@Risk Monitoring System
### Inform@Risk LoRa Base Module  
The Inform@Risk LoRa base module is designed around a SAMD21 32-bit microprocessor board (Arduino MKR WAN 1310) with an integrated LoRa module (Murata CMWX1 ZZABZ). This processor board has the benefit of combining very low power consumption with good flexibility in terms of analog and digital in- and outputs. Furthermore, its design is open-source and it is easily available all over the world. Figure 3 shows the basic design with the attached sensors (and the optional power- and sensor attachments). The detailed circuit board design, schematics and hardware specifications are referenced in Section 6. For easy reproducibility and further development a breadboard-based design  
Figure 2: Measurement concept for the Infrastructure node (IN), Subsurface Node (SN) and Low-Cost Chain Inclinometer (LCI). is available. For actual applications it is recommended to use printed circuit boards (PCB) which make node construction much easier. The PCB and the breadboard-based designs are available for download on our website (Section 6). Three different power supplies can be used: battery power (4-8 AA batteries), solar power (6V solar panel, 1-2 W, LiPo battery and battery charging regulator) or an outside power connection (DC 4-18 V input, optional with LiPo battery). All these options are connected to a voltage regulator, which supplies the required DC 3.3V internal system voltage. Additionally a step-up/down converter offers switchable DC 12 V for, e.g., external 4-20 mA devices. If batteries are used, an input voltage of about 6 to 12 V, corresponding to 4 to 8 AA batteries in series, is recommended. The base module includes a low-cost IMU (Inertial Measurement Unit), which measures the acceleration (0.04\({}^{\circ}\) precision) and orientation of the node, as well as temperature and barometric pressure. Additionally, all Infrastructure Nodes include a high-precision inclination sensor. Because of its price, this unit is optional for all subsurface sensor nodes, where the inclination of the basic module itself is not as important. The sensor can simply be plugged into the circuit board when needed. We also developed a 3D-printed mount which allows the sensor to be placed horizontally, independent of the placement of the node enclosure (e.g., on a skew retaining wall). Furthermore, included on all nodes is a 24-bit analog-digital converter (Texas Instruments ADS1220), to which external geotechnical or other sensors can be attached. It has four channels, which can be combined to max. two differential voltage measurements (e.g.,  
Figure 3: Schematic depiction of the Inform@Risk basic module and the additional sensors for subsurface measurements (SN, LCD). The colors show the data transmission: red: analog signal; orange: I2C (Inter-Integrated Circuit) bus; violet: SPI (Serial Peripheral Interface) bus. for potentiometers) or can individually measure voltage referenced to ground (used, e.g., in combination with a measurement resistors for 4-20 mA measurements). The firmware for the nodes is written in the Arduino programming environment (Integrated Development Environment, IDE) and can easily be accessed and changed with the IDE. The code is distributed via Github (see Section 6). The software comprises five stages: initialization of the sensors, measurements, computation, and LoRa up- and downlink. Although these stages are fixed, multiple parameters can be changed to accommodate for varying on-site requirements. For example, the overall measurement duration as well as the measurement frequency for each sensor can be changed. These parameters can not only be changed when installing the node but also remotely with commands transmitted via LoRa communication. Furthermore, for each sensor it is possible to decide whether median or mean values should be sent to the gateway. For example for the accelerometer, it is best to calculate the median of the measured values since it is less sensitive to outliers from external influences (e.g., vibrations, impacts).