# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 6 Tsunamis
### Reviewed Use Cases for Tsunami EW
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Article** & **Focus and Objectives** & **Results** \\ \hline
[79] & The study focuses on characterizing an underwater communication link for Tsunami Early Warning & The tested link showed high reliability with a 350 bps data rate, while higher rates, even if still achievable, provide a less reliable connection. The 1 s latency for the receiver alongside the good data rates makes this solution feasible for real-time tsunami warning. \\ \hline
[80] & Method to determine an optimal array configuration of offshore sensors for near-field Tsunami EW, also in terms of deployment costs. & The study was able to prove that a 3-sensor configuration is able to provide accurate estimations. It also highlighted some factors that influence predictions, such as depth, time of arrival and position of the sensors. \\ \hline
[81] & Method for near-real time tsunami forecasts using the S-Net network. It classifies sensors based on their distance from the uplift region and then computes the region’s area. & The technique was successfully validated on two previous tsunamiigenic events, and it required 1 min to run the classification algorithm and not more than 10 min to estimate the source area of the tsunami. \\ \hline
[82] & Proposal of a framework for a WSN to produce Tsunami EW system detecting magnetic field, animal behavior and tide behavior changes and using an ML model. & The tested ML model showed that the most interesting features in terms of prediction were tide level changes and migration pattern changes. \\ \hline
[83] & Proposal of an EW system to improve the performance of existing Tsunami EW systems, particularly in terms of cost and compromised communications. & The produced vehicles have low mobility and a mostly passive operational mode, allowing low power consumption. The use of satellite communications and offline back-ends would allow the system to survive communications fails in case of disaster. \\ \hline
[84] & Design of a device for wave height monitoring using an ultrasonic sensor placed above water, directly on the shore line. When the water level rises, a GSM module also sends an SMS with a warning message. & The prototype was successfully tested but the sensor cannot work at heights above 3.19 from seawater, and as such it requires a careful installation. \\ \hline
[85] & Development and deployment of a buoys system that uses buoys positioning measurements to monitor the motion of sea surface, also sending other data such as sea level. An acoustic system was also deployed to monitor the ocean’s bottom motion. & Field experiments showed centimeter precision in buoys positioning. An experiment showed data gaps, possibly due to the tilting of the buoy degrading the communication, which led to the implementation of a system to stabilize the antenna. \\ \hline
[86] & Design of a floating IoT device for early warning and wave anomalies detection, using Fuzzy Logic for accurate prediction. & The Fuzzy Logic algorithm had high accuracy (98\% to 100\%) in testing phase. The LoRa communication showed 4.6279 s of delay and minimal error rate. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Reviewed Articles in Tsunami EW. Factors that influence a prediction include: position of the sensor compared to the main wave energy beam (parallel or orthogonal to the wavefront); time of arrival of the pressure wave to the sensor (sensors closer to the event have a smaller observation time and therefore less data available for predictions); depth of the sensor (must be high enough as to ignore non-linear effects); position of the sensors relative to each other (the network granularity is chosen so that it is higher than the tsunami's wavelength). Results showed that a three-sensor configuration can provide accurate estimations of the tsunami arrival time and peak amplitudes for the first wave.