# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 5 Earthquakes
### Reviewed Use Cases for Earthquakes EW

Table 3: Reviewed Articles in Earthquake EW. [PERSON] et al. [53] proposed and tested a Seismic Wave Event Detection Algorithm (SWEDA) to achieve milliseconds earthquake detection and warning. It uses inclinometer nodes for Industry 4.0 and the CANOpen communication protocol for industrial IoT. They designed and produced two types of sensor node: a flat inclinometer consisting of two accelerometers, and a cilindrical one consisting of seven sensors coupled with a 24-bit sigma-delta ADC and a programmable gain amplifier to increase the resolution. Besides P-waves, this system is also able to detect S-waves, Rayleigh waves, and Love waves, but different sensors placements are required to detect different types of waves with low computational efforts (matching positions of the sensors and direction/angular displacements of the earthquake). The data processing algorithm was optimized to avoid false alarms and reduce the computational cost of floating-point operations and calibrations, the latter being essential to avoid false alarms. The solution was tested with three different systems installed in Structural Health Monitoring (SHM) sites at Qatar University. The system was able to identify an on-site induced earthquake, with a first trigger for P-waves 11 s before a second trigger for S-waves was generated, showing good early warning capabilities. Machine Learning models can also be implemented to predict earthquakes using data from multiple sensor sources. [PERSON] et al. [62] proposed a distributed approach for Earthquake EW that uses data from GPS stations and seismometers to make predictions based on an algorithm they validated on a real-world dataset. The distributed approach is based on Edge/Fog computing and is meant to reduce the amount of data to be sent on the network by embedding a ML classifier on each sensor. The classifier produces a class output that is sent to a central server that combines the class prediction from each sensor to give a final prediction. The aggregation produced at sensor level combined with the fact that the information transmitted is unrelated to earthquake values drastically reduces the transmission effort over the network, lowering latency and making communications easier. The article showed that GPS stations and seismometers have a complementary performance (for example, for large earthquakes the accuracy is 99% for GPS, 28% for seismometers). As such, they evaluated a combined model, confirming how a multi-parametric model is the best choice in terms of accuracy. [PERSON] et al. [63] simulated an on-site WSN model to predict and detect earthquakes in the island of Mauritius, and they analyzed how different WSN parameters can corrupt or enhance estimations of velocity and location of the epicenter (obtained from P waves) to evaluate the feasibility of such a system. The article focused on synchronization issues for WSNs in earthquake monitoring: it is essential that all nodes and the base station/sink are synchronized, otherwise the clock offsets between the nodes becomes a component of the wave propagation delay, thus making warnings imprecise. The simulated architecture uses Timing-sync Protocol for Sensor Networks (TPSN) to achieve synchronization between nodes. Simulation results show that while networks using the TPSN protocol for synchronization have a worse performance compared to perfectly synchronized (ideal) systems, networks with no synchronization protocols whatsoever have a much worse results in terms of velocity and epicenter localization. The paper also proves that denser networks provide better data and are more resilient to faulty nodes, and higher sampling frequencies allow better performance. Since the efficiency of the earthquake detection relies on the network's density, this could translate into a very high cost for the Earthquake EW solution. Micro-ElectroMechanical System (MEMS) can provide a low cost alternative to traditional seismographs or seismometers and they can be used to build denser networks with a smaller investment. [PERSON] et al.