# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 5 Earthquakes
### Reviewed Use Cases for Earthquakes EW
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt}} \hline
**Article** & **Focus and Objectives** & **Results** \\ \hline
[53] & Proposal and testing of an SWEDA earthquake algorithm to achieve milliseconds earthquake detection and warning and optimizing computational costs, also designing and producing 2 kinds of sensor nodes. The system is also able to detect different kinds of seismic waves. & The solution was tested with three different systems installed in SHM sites at Qatar University, and it identified P-waves 11 s before a second trigger for s-waves was generated, showing good Earthquake EW capabilities. \\ \hline
[62] & Proposal of a distributed Edge Computing approach for EEW which uses GPS stations and seismometers to run a ML model. The distributed approach would reduce the amount of data to be transmitted towards the Cloud and provide a more resilient system in case of wide earthquakes. & GPS stations and seismometers have a complementary performance (for large earthquakes the prediction accuracy is 99\% for GPS, 28\% for seismometers). As such, they evaluated a multi-parametric model, confirming how a combined model is the better choice in terms of accuracy. \\ \hline
[63] & Modelling and simulation of a WSN for Earthquake EW and evaluation of how its performance changes depending on different WSN parameters, such as synchronization protocol, sampling frequency and node density. & TPSN gives velocity and localization errors 0.6 to 6 times and 1 to 5 times greater than with perfect synchronization, but errors are 5 to 266 times and 96 to 361 times greater with no synchronization. Using 5 nodes, a faulty node corrupts 25\% of the data, while with 2 nodes only 4\% of the data is corrupted. A higher sampling frequency also improves performance. \\ \hline
[64] & Development and testing of a sensor node for dense Earthquake EW networks, achieving low costs using MEMS sensors and standalone TCP/IP units to handle communications, so that the device can be controlled by a simple and cheap MCU. & Data from the low cost sensor arrays have good consistency with data from standard seismographs, and seismic phases obtained were accurate enough for EW. Even small earthquakes were identified within a 20 km range from the sensors. Bigger earthquakes (M 4.7) were detected at up to 200 km from the sensors. \\ \hline
[65] & Performance Evaluation of a Dense MEMS-Based Seismic Sensor Array with 170 sensors (based on the design developed in [66]) to show that they can be classified as class B sensors. & The proposed sensors duplicate the performance of 'traditional' earthquake sensors with a percentage difference below 10\%, also when far from the epicenter (150 km). Stations with difference higher than 20\% were due to calibration errors. \\ \hline
[67] & Design and testing of a low-cost, low-power cloud based Earthquake EW platform called Earthcloud, which uses geophones to detect strong motions and issue first alarms to AWS IoT using MQTT, and then further processes the data using Amazon Kinesis for precise alarms. & The proposed sensors duplicate the performance of 'traditional' earthquake sensors with a percentage before 10\%, also when far from the epicenter (150 km). Stations with difference higher than 20\% were due to calibration errors. \\ \hline
[68] & The articles proposes and validates a 3-layered architecture that uses smartphones to create a MCS widespread Earthquake EW network, using a layer intermediate to the sensors and the application layer to ensure reliability. & Validation shows a 12 s anticipation of the seismic peak, but the validation phase is still to be improved. Low power usage (4.9\%) was achieved. \\ \hline
[69] & Overview and valuation of the performance of the MyShake smartphone earthquake monitoring application through simulation, with a focus on the possibility to use it for EW. & MyShake phones detect the earthquake 3.8 s after the origin time and the system recognizes the event when 26 phones have been triggered, 15.6 s before the S-wave reaches the monitored city, but slightly underestimating the magnitude. Alert delivery latency was of 2.8 s, but it could be reduced and it is unclear how it would scale with many devices. \\ \hline
[35] & Proposal of an Early Warning system that uses SHM to detect seismic events and evaluation of the advantages of a 5G gateway in this context, using the URLLC service. & Latency will be reduced (<1 ms) while maintaining very high reliability using URLLC. While the proposed system architecture and 5G networks provide advantages in two key Earthquake EW constraints, field tests still need to be carried out. \\ \hline
[70] & Performance evaluation of an energy economical WSN for SHM embedded with an Earthquake EW system, employing WoR modules to minimize the energy consumption required to wake up the WSN when an earthquake is detected with a P-wave detector. & WoR modules and radio triggering allow the nodes to go into complete sleep mode when no sensing is required, reducing power consumption to 350 uA with a 229 ms delay for waking up the nodes, with a better performance IEEE 802.15.4. \\ \hline \end{tabular}
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