# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 7 Landslides
### Reviewed Uses Cases for Landslide EW
The data from the sensors is collected by a sink node and then sent to a central station for analysis, warning generation and decision making on the network topology, depending on the state of the landslide. The central station uses a thresholds-based system to determine a warning level using data from both a rain gauge station and the WSN. It is not always easy to identify the right thresholds for a Landslide Early Warning System, even when historical data is available. [PERSON] et al. [92] developed a Landslide EW system in the Himalayas based on MEMS tilt sensors and water sensors embedded in the soil. They also studied the correlation between rainfall levels and on-site conditions to obtain better thresholds and avoid false alarms. The monitoring system they developed consisted of six low-cost units each equipped with antennas to send measurements to a data logger placed near the sensor nodes, with 10 min sleep intervals after each transmission. The tilt sensors were able to measure slow ground displacement, showing they are viable to predict failures. They also showed that rainfall is not always directly correlated with tilting variations and that both short-term and long-term rainfall should be considered to develop regional thresholds. [PERSON] et al. [93] developed a MEMS-based system that uses LoRa for communication. The lower layer of the Landslide EW system consists of: (i) a multi-sensors LoRa network with three types of sensor nodes and at least two LoRa Gateways serving each node for redundancy; and (ii) a Continuous Shear Monitor, piezometers, and extensometer systems, which require more space and are harder to install compared to the LoRa nodes. The LoRa nodes are therefore used to cover a large area and to monitor a wide array of environmental parameters at lower costs. A central station gathers data from the LoRa nodes and the other systems and forwards it to a central Cloud server called Inform@Risk. The Cloud manages and combines the data from the various sensors to produce Early Warnings and danger levels based on thresholds which are currently still being determined. Warnings are issued through an App and sirens installed locally, but alarms are immediately sent only when at least two neighboring nodes display strong accelerations at the same time and a data analysis algorithm has been run; otherwise, an expert is tasked to control the data to prevent false alarms. [PERSON] et al. [94] proposed a landslide EW system that uses cameras to detect motion. They use a fairly simple motion detection algorithm so that it can be implemented on a Raspberry Pi connected to the cameras to detect landslides on-site. This way, the only transmissions are related to the images corresponding to a detected landslide event. The paper did not specify the preferred communication method. They also implemented a storage of events data on a Cloud database that can be accessed through a user application. [PERSON] et al. [21] developed a Landslide EW system that uses ML for predictions, an they also analyzed different factors to enhance the reliability of the system. They divide the system into five main components: (i) data collection, (ii) data transmission, (iii) forecasting, (iv) warning, and (v) response. By using Machine Learning algorithms in the system, they are able to obtain good results even if the first two layers fail, and they also stressed how not all current Landslide EW systems take into account improving the reliability of data transmission or adding redundancy. They consider two types of prediction: \"nowcasting\", which uses rainfall forecast information instead of sensor data to still be able to produce a prediction when communications or sensors fail; forecasting, which provides extra lead-time for early warning in the fourth component by using data in the first two components when available. [PERSON] et al. [95] proposed an Edge AI architecture for Landslide EW that can significantly reduce the latency of the system by doing the training of the ML model at the edge of the IoT network.