# Wireless Sensor Network-Based Rockfall and Landslide Monitoring Systems: A Review
## 3 Rockfall and Landslide Monitoring Based on WSNs
### WSN-Based Monitoring Systems
If a route is blocked, the nodes will vary the hop sequence for data exchange with the gateway. The MEMS units showed that these sensors have good accuracy and resolution to detect movements like tilting and mass spreading. The sensor fusion allowed the verification of received data, thus, a better interpretation of the dynamics of the affected area. An important point in the SLEWS project was low-cost development with accuracy capabilities. This was accomplished using low-cost sensors with standardized industrial interfaces. Past works have presented distinct patterns of accelerometric data in the behavior of the structures [88]. Pattern-relative information is fundamental for the understanding of landslide engineering for the implementation of mitigation structures. In [89], the authors presented a system that communicated with an on-site trailer station using the 802.11 g standard [90]. Computers were equipped on the trailer for on-site data analysis and to provide cellular uplink. The latter was used for internet communication to a storage server. The authors carried out experiments using a rock equipped with a sensor device, and the results reported different IMU data for each possible movement pattern of the mass. The authors had to realize a rugged prototype, capable of handling the stress of falling with the rock formation during the tests. Although only small-scale experiments were performed, the results were quite encouraging to justify further studies using this approach in real scenarios. Received signal strength indicator (RSSI) localization approaches have been deeply studied in the literature [91], and we found several proposals in landslide and rockfall monitoring systems. In [92], another LoRa-based WSN on an uninhabited hillside was deployed. This area is harsh and affected by to landslides. Therefore, the authors studied the feasibility of a LoRa-based network and focused on the zone coverage of the communication. The evaluation was carried out using RSSI, which was fed to a triangulation algorithm to localize a particular node in the network. If the node had varied its position, then a portion of the landslide had moved. All the elements communicated in a star network to an anchor node where all the data was collected locally. However, the study did not report an in-depthdescription of the hardware implementation of the sensor nodes and the mounting on the hillside. In order to verify the RSSI model, the authors had to make hundreds of RSSI measurements between one anchor and a sensor for different positions. The localization of nodes in a landslide using RSSI and the channel state information (CSI) is sensitive to environmental noise. This results in an inaccurately estimated displacement. The authors of [93] applied an information-fusion method based on bounded-error estimation to improve accuracy. The aim of this work was not an actual implementation of a monitoring structure but to provide enhanced localization algorithms for RSSI-based landslide monitoring systems. The experimental results showed that the accuracy was increased by approximately 4.5% compared to conventional methods. Another system specifically conceived for harsh environments is described in [94], in which an energy-efficient ad hoc communication protocol was employed to ensure robustness and a long lifetime of the overall system. The network was composed of 15 wireless nodes with sensors of various kinds. One of them was equipped with a GPRS [95] modem to act as a gateway toward the internet. The power supply needs of both the nodes and the gateway were fulfilled with batteries, with the addition of a solar panel in the case of the gateway. This had the disadvantage of requiring periodical human intervention for battery replacement, although the residual battery charge check suggested a worst-case lifetime of more than one year. The results obtained showed quite good power performances with an average power consumption of the node of about 0.192 mW.