# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 5 Earthquakes
### IoT Architectures for Earthquake EW Systems
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Article** & **Perception Layer** & \
\begin{tabular}{c} **Communication** \\ **Layer** \\ \end{tabular} & **Application and Edge Layer** &
\begin{tabular}{c} **Edge/Fog** \\ **Computing** \\ \end{tabular} \\ \hline
[53] & Inclinometers & CANopen &
\begin{tabular}{c} SWEDA Algorithm (on-site P-Wave \\ detection) \\ \end{tabular} & Yes. \\ \hline
[62] & GPS Stations, seismometers & Not Specified &
\begin{tabular}{c} Machine Learning classifiers both at sensor \\ and central server level \\ \end{tabular} & Yes. \\ \hline
[63] & Seismometers & \
\begin{tabular}{c} Model based on \\ TDMA \\ \end{tabular} &
\begin{tabular}{c} Crosscorrelation-based multi-station \\ algorithm to locate epicenters \\ \end{tabular} & No. \\ \hline
[64] & MEMS Accelerometers & Ethernet, 3G/4G &
\begin{tabular}{c} P-Wave detection algorithm and processing \\ at remote server \\ \end{tabular} & No. \\ \hline
[65] & MEMS Accelerometers & 3G/4G &
\begin{tabular}{c} P-Wave detection algorithm and processing \\ at remote server; real-time Shake Maps \\ computation \\ \end{tabular} & No. \\ \hline
[67] & Geophones & Ethernet &
\begin{tabular}{c} Cloud Based solution based on AWS IoT for \\ data storage processing and P-Wave \\ detection \\ \end{tabular} & No. \\ \hline
[68] & Smartphone MEMS sensors, GPS modules. & Wi-Fi, 2G/3G/4G &
\begin{tabular}{c} Smartphone application with classification \\ algorithm; Machine Learning prediction \\ algorithm at central server \\ \end{tabular} & Yes. \\ \hline
[69] & Smartphone MEMS sensors & Wi-Fi, 3G/4G &
\begin{tabular}{c} Smartphone application with ANN-based \\ detection algorithm; cluster-based EW \\ algorithm at central server \\ \end{tabular} & Yes. \\ \hline
[35] & Optic fibre, MEMS and piezoelectric sensors. & \
\begin{tabular}{c} MBUS radio \\ 169 MHz, 5G \\ \end{tabular} &
\begin{tabular}{c} Threshold-based sentinel node; SHM-based \\ event monitoring \\ \end{tabular} & Yes. \\ \hline
[70] & SHM sensors, P-Wave Detector. & IEEE 802.15.4, 3G/Wi-Fi &
\begin{tabular}{c} P-Wave Detector on-site node; SHM-based \\ event monitoring \\ \end{tabular} & Yes. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Technologies and solutions used for each layer in the reviewed papers related to Earthquake EW. When it comes to detection algorithms and data processing, P-Wave detection is usually the easiest method to be implemented. Moreover, it allows to run earthquake detection algorithms on memory constrained devices to create on-site warning systems and to implement faster Edge solutions. Fast algorithms should be used to avoid adding data processing latency to the system. On-site methods are useful for detection of earthquakes near a seismogenic zone [66]. In particular, STA/LTA algorithms are especially suited for real-time monitoring and early warning, especially when the devices have limited memory or processing power [73]. Other methods such as Machine Learning can also be effectively used. Compared to the other use cases reviewed in this article, the papers for Earthquake EW made a larger use of Edge solutions, which are essential to reduce the latency of the system to the minimum, for example allowing the nodes to detect earthquakes and send warnings without processing the ground motion data at a central server or Cloud, where the data can still be used for more accurate estimations later without reducing the overall speed of the alert. Moreover, employing an Edge Computing approach, particularly a distributed one like the system proposed in [62], could be useful to avoid issues deriving from network outages during large earthquakes by distributing the earthquake detection capabilities over various devices disseminated on the territory.