## 4 IoT-Cloud-Based EWS
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & **Sensor Node** & \
\begin{tabular}{c} **Employed** \\ **Environment** \\ \end{tabular} & **Used Data Type** &
\begin{tabular}{c} **Used Measurement** \\ **Parameter** \\ \end{tabular} & **Source** \\ \hline
[184] & \
\begin{tabular}{c} Acceleration sensors \\ (MMA8452, \\ ADXL355, and MPU9250) \\ \end{tabular} &
\begin{tabular}{c} US \\ \end{tabular} & Acceleration data & PGA & NIED and USGS \\ \hline
[180] & Mobile node & Coastal areas & Tsunamic data &
\begin{tabular}{c} Hypo-center \\ and magnitude \\ \end{tabular} & NOAA \\ \hline
[177] & UAV nodes & ODLOS & Aerial images data & Received frames/sec & Local drones \\ \hline
[185] & Smartphones & S-D environment & Acceleration data & Earthquake data & NIED and USGS \\ \hline
[188] & Seismometer & UG &
\begin{tabular}{c} GPS and weak \\ motion data \\ \end{tabular} & Earthquake data & IRIS and NIED \\ \hline
[173] & MEMS & UG & Acceleration data & Acceleration, SNR & NIED \\ \hline
[174] & Arduino Cortex M4 & UG & Acceleration data & \
\begin{tabular}{c} Earthquake detection \\ accuracy and detection latency \\ \end{tabular} &
\begin{tabular}{c} Local data observed by MEMS \\ accelerometers \\ \end{tabular} \\ \hline
[175] & Acceleration nodes & IDNLOS & Acceleration data & PGA and human activity & Local distributed smartphones \\ \hline
[176] & Soil and terrain nodes & UG &
\begin{tabular}{c} Soil moisture, shear strength of the soil, \\ severity of the rain \\ \end{tabular} & Soil moisture, rain & GSI \\ \hline
[53] & Tmote Sky & ID and OD & Seismic velocity data & Location magnitude & JMA and Hi-net \\ \hline
[179;195] & IoT gateway & UG & Seismic waveform & \
\begin{tabular}{c} Earthquake \\ predictions \\ \end{tabular} &
\begin{tabular}{c} Local datasets and \\ regional data \\ \end{tabular} \\ \hline
[183] & Acceleration nodes & UG & Acceleration data & PGA & NIED \\ \hline
[185] & MEMS & Noisy environments & Seismic waveform & P-wave arrival & STEAD \\ \hline
[182] & Raspberry Pi & Mesh network & Seismic waveform & Local earthquake & Locally observed \\ \hline
[196] & SSN/SOSA ontology & UW & Volcanic data &
\begin{tabular}{c} Volcano-tectonic, \\ long-period earthquakes, \\ water explosions, \\ and quarry blasts \\ \end{tabular} & Local data \\ \hline \hline \end{tabular}
\end{table} 
Table 3: IoT-based EEWS main efforts. mobile systems. It operates in two stages. The first stage is pre-disaster, as ML models are used to detect the commencement of the principal wave. This procedure is extremely advantageous for risk minimization, such as rapid shutting down of nuclear power plants, electrical producers, and so on. The second phase begins after the disaster has occurred, with the goal of mitigating/reducing the disaster's impacts. Using an integrated system, for example, allows for more accurate statistics regarding the affected people, buildings, utilities, and areas. As a result, an effective evacuation strategy can be implemented. A solution that is both flexible and intelligent and that is able to deal with complex problems in a relatively short amount of time is required for such a system. ML has the potential to play a significant and critical part in the administrations that are interconnected and working on achieving successful EWS among the variety of existing current techniques. ML is a promising method that works regardless of the data type, format, length, and other factors such as these. Indeed, real-time monitoring takes place across all of the dispersed organizations shown in Figure 8, which serves as the foundation for a reliable EEWS. As a consequence of this, the transfer of data between various entities needs to be thoroughly investigated and estimated. After that, ML models are utilized to zero in on the current status of each object and even provide an estimate for a certain word. As a consequence of this, those institutions are capable of making useful contributions prior to, during, and after earthquake disasters.