# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 2 Landslide Early Warning Systems
### The Internet of Things and Landslide Monitoring  
In the last decade, the Internet of Things (IoT) has become an important concept for landslide monitoring. Its definition, \"Sensors and actuators embedded in physical objects are linked through wired and wireless networks\" [37], describes why the concept is well applicable to landslide monitoring and early warning. The IoT can help circumvent the problem of often very expensive monitoring systems and other possible problems such as difficult maintenance and rigidity of cable-based (non wireless) systems [38]. IoT systems consist of the Perception layer, the Network layer, the Middle-ware layer and the Application layer [38]. The Perception layer consists of multiple IoT devices that collect data, which is then sent via the Network layer to the Middle-ware layer. Here, the data can be stored and analyzed before it is processed for the user in the Application layer. One of the most important technologies which is the basis of the IoT is wireless communication technology. Several wireless communication standards for different application profiles have been developed. While some, as, e.g., Wi-Fi offer very high data rates, they are less suitable for battery powered setups where low current consumption and at the same time large communication range is required (other systems such as GSM or satellite communications are less likely to be employed due to their comparably high costs [39]). Systems like Bluetooth offer low energy consumption (Bluetooth Low Energy), but are restricted in terms of their range. The LoRa technology is one of numerous low power wide area networks (LPWANs) which offer very low power consumption, while at the same time providing a wide coverage of 2-5 km in urban and 10-15 km in rural areas refs. [40; 41; 42]. For landslide monitoring, LoRa has several advantages over similar LPWANs, for example a payload size which is sufficient for data from multiple sensors on one sensor node (max. 243 bytes) [41]. Furthermore, it is bidirectional, which allows a degree of control over the sensors from the application layer (e.g., changing the time between measurements or updating firmware). Alternatives to LoRa are, e.g., Sigfox and NB-IoT, but LoRa offers the best parameters for this application: it is unlicensed (which NB-IoT is not) and bidirectional (which Sigfox is not), both important factors for a flexible, open-source system [41]. LoRa is being used worldwide in various natural hazard scenarios [39; 43; 44]. Other landslide early warning systems use or have used similar LPWAN technologies [45; 46]. A LoRa data transmission is divided into uplink (device to gateway) and downlink (gateway to device). The uplink usually is significantly bigger than the optional downlink. The uplink packet size has a theoretical maximum of 255 bytes, depending, among others, on the distance from the device to the gateway [39]. In practice, the packet size should not exceed 51 bytes [40]. The LoRaWAN protocol restricts the maximum airtime of devices to 30 s per day, resulting in 20-500 messages per day, depending again on the distance/data rate and the payload size. This ensures that many devices can be in the same network without problems. Another important technology, which has made the IoT possible, is the development of MEMS (micro-electro-mechanical system) sensors. These combine micro-mechanical elements and electronics in a single chip, allowing to develop small, highly available and low cost sensors for different measurement tasks. MEMS-based sensor systems already are being widely used for geotechnical instrumentation and landslide monitoring, especially since open-source microprocessors have become readily available in the last years [31; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57]. They can be a good addition to classic monitoring methods, as discussed by [PERSON] et al. (2013) who investigated the accuracy and precision of a low-cost MEMS accelerometer [53].