# Wireless Sensor Network-Based Rockfall and Landslide Monitoring Systems: A Review
## 3 Rockfall and Landslide Monitoring Based on WSNs
### WSN-Based Monitoring Systems
A custom protocol was developed for this implementation, in order to have reliable communication in a harsh environment. In [96], the authors presented a network based on LoRa and intelligent sensing for rainfall and landslide displacement in Shuicheng County, Guizhou Province, China. Four crack nodes were located in cracks to measure the displacement of rock, while one central node acted both as master and gateway. The rainfall was measured at the location of the master node by means of a tipping bucket-switching rain sensor. The hardware and software designs of the system were described comprehensively. The sensor nodes could operate in normal or reduced power mode to obtain a lower current absorption of 1 mA. For this purpose, the authors explained the low-power software design in detail. The sensor nodes operate in slow sampling mode until an over-threshold displacement of 20 mm is sensed. Subsequently, the devices enter a fast sampling mode, collecting data every second until a successful transmission is achieved. The deployed sensor network was able to measure rock movements that exceeded the threshold in one of the test locations, where a sudden displacement was recorded. Deep-earth probes are a possible solution for landslide sensing, especially in soils where the presence of rock is reduced. The work presented in [97] dealt with the deployment of one in a region of India. The authors used a single data acquisition board connected to multiple sensors, namely strain gauges, dielectric moisture sensors, piezometers, and a geophone. Some poles were inserted deep into boreholes with a depth of 2 to 20 m. The authors deployed a network containing 49 sensors during this stage of the research. All the sensing elements were read by the node and transmitted to a central gateway using Wi-Fi. The gateway had a satellite link that allowed communication towards a remote server. The power supply to the nodes was achieved by solar harvesting and lead-acid batteries; however, we did not find an analysis of the energy performance. An in-depth description of the sensor placement was reported. The study showed data from 1 month in the period of July-August 2009 that showed the strain gauge deformation and water activity in the bores. Soil moisture monitoring was also used in [98] where a tensiometer and soil moisture sensors were integrated with an inclinometer. The authors placed three wireless nodes in a landslide-affected area along a hillside in Seoul, Republic of Korea. A comprehensive risk assessment was carried out previous to the installation, in which the authors studied the geomorphological aspects of the area and the vegetation density. The morphology of the environment was also studied for radio propagation purposes because the high density of trees and the height of the test installation were two non-favorable variables for the correct transmission of data. The nodes' radio layer was based on Wi-Fi standard at 2.4 GHz center band frequency. A sink gateway communicated the data to a remote server using LTE. The system was solar-powered by means of a solar panel and a battery. However, we do not have information about its energy performance. The system allowed the authors to obtain data on the rainfall characteristics at different soil heights for several seasons. The authors compared the results obtained by the real implementation of the WSN with the results that were gathered in laboratory tests. The actual field data showed a lower volumetric content of water due to different soil saturation conditions. In [99], a LabVIEW program [100] was developed for the purpose of analysis and presentation of the data collected from an energy-efficient WSN that could run unattended for months. The system was built on the IEEE 802.15.4 [101] protocol, and the various monitored parameters were volumetric water content in the soil, seismic vibrations, displacement of the soil layer, soil temperature, and rainfall measurement. The landslide-prone area and hence the nodes used to monitor it were divided into two clusters.