# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 7 Landslides
### System Requirements and Constraints for Landslide EW Systems  
The reviewed literature highlighted the following constraints and aspects that should be taken into consideration when developing a Landslide EW IoT solution: (i) importance of avoiding false alarms (ii) minimizing costs, for example using MEMS sensors, which also reduce the difficulty of installation; (iii) optimization of sensors placement through risk analysis, the choice of the right parameter to monitor and the right sampling rates for each sensor; (iv) limiting battery consumption; (v) enhancing resilience in case of extreme events, also for communications through redundancy or other methods [21; 93]; and (vi) planning for failure for both the communication channel and data acquisition.  
It is very important to avoid false alarms, for example optimizing thresholds or defining multiple thresholds based on different parameters [21; 91]. [PERSON] et al. also added the prospect of using data fusion techniques to reduce the effect of human interference on measurements. Some articles also considered asking experts to check the collected data if the warning level is not high enough to issue an immediate alarm.  
Battery consumption can be limited both using simple sleep modes in between transmissions, which are the most energy consuming operation for a sensor node, or employing network topologies that allow to reduce the number of transmissions for each node.  
Lastly, landslide systems can be damaged by the events they monitor and as such one should plan accordingly, for example placing sink nodes further from the monitored area, or using more than one gateway to serve each sensor, or planning for redundant communication, since a failure on the data acquisition or transmission levels could lead to a system unable to predict dangerous events [21].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Article** & **Perception Layer** & **Communication Layer** & **Application and Edge Layer** & **Edge/Fog Computing** \\ \hline
[91] & PWP sensors, accelerometers, soil moisture sensor, rain gauge station & Zigbee, GSM/GPRS & Threshold-based warnings at central station & No. \\ \hline
[92] & Tilt Sensors, volumetric water content sensors & Not specified wireless communication & Data aggregation into local datalogger; processing and analysis at remote server & No. \\ \hline
[93] & Continuous Shear Monitor, piezometers and extensometer, inclination sensor, water sensor. & LoRa, GSM systems & Data analysis and threshold-based warnings at Cloud Server; alarms through Smartphone Application and local sound systems & No. \\ \hline
[94] & Camera Sensor & Not specified & Landslide detection performed locally; storage of images to database; alarms through Smartphone Application and SMSs & Yes. \\ \hline
[21] & Rainfall, pore water pressure sensors & Not specified & Data analysis at central server; forecasting and nowcasting ML models. & No. \\ \hline
[95] & Weather nodes (temperature, humidity pressure, rainfall, wind speed), ground nodes (GPS, soil moisture, accelerometer, gyroscope) & LoRa & Edge AI implementation for prediction and monitoring; Cloud server for storage and additional processing power & Yes. \\ \hline
[96] & Moisture, porepressure and displacement sensors & Wi-Fi, GSM model training at Cloud server; ML model prediction at Edge node. & Yes. \\ \hline
[97] & GNSS station; ground crack monitoring station; rainfall monitoring station. & 5G, GPRS & Web platform to analyze data and issue warnings based on slope deformation & No. \\ \hline \hline \end{tabular}
\end{table}
Table 8: Technologies and solutions used for each layer of the reviewed papers for Landslide EW.