# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 8 Recommendations and Future Work
\begin{table}
\begin{tabular}{c p{227.6 pt}} \hline \hline
**Requirement** & **Recommendations and Possible Solutions** \\ \hline Battery Saving & * Employing smart sleep procedures in-between transmissions, keeping short wake-up and transmission windows [50; 70], or dynamically switching to sleep mode based on the event probability of occurrence in the area [26]. \\  & * Choosing the right network topology or employing multiple topologies in the same solution [91]. \\  & * Using energy harvesting modules [49]. \\  & * Distinguishing between a “normal” mode of operation and a “danger” mode with higher sampling frequencies [49; 91]. \\  & * Using Edge implementations and on-site warnings to limit the amount of transmissions towards higher layers of the EW architecture [62; 95]. \\ \hline Fault Tolerance & * Using mesh topologies and self-repairing/self-organizing networks [44; 54]. \\  & * Planning for redundancy in communication [93] or for non-terrestrial satellite back-ends or communication links [83]. \\  & * Employing a higher nodes density, also to limit prediction corruption from faulty nodes [63]. \\  & * Implementing “nowcasting” procedures, if possible for that use case [21]. \\  & * Using Edge implementations to be able to issue warnings even if the connection to the Cloud is down [96] or to distribute the data processing at different locations [62]. \\ \hline Coverage & * Using ad hoc networks when cellular infrastructures or communications are inefficient [46; 48]. \\  & * Making use of satellite communications to reach difficult locations that are not served by cellular communication [37]. \\ \hline Latency & * Integrating 5G technologies into the project, particularly its low latency services [35]. \\  & * Using on-site methods and Edge implementations to be able to limit response time in time critical applications [39]. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Recommended solutions for each of the main EW constraints. * Moving towards new cellular solutions such as 5G or NB-IoT, since some of the reviewed papers still relied on GSM or generally older cellular technologies. The use of new cellular solutions specifically designed to support IoT device and machine to machine communication (such as EC-GSM, NB-IoT or LTE-M) could provide better coverage, battery saving, data rates and latency performances in this context. * Employment of non-terrestrial-networks and satellite communications. Satellites do not only allow to reach geographically distant locations and overcome the problem of missing terrestrial infrastructures [37], but they can also provide an essential back-end in case of disasters affecting the terrestrial communication infrastructures. * Optimization of the prediction algorithms and data processing speed, especially for use cases such as earthquakes. Data analysis can become the bottleneck of an EW system and data latency is the main component of EEW latency [60], and as such efforts should be focused on its reduction for the use cases that particularly value speed. Besides the signal processing algorithms, packetization and data serialization formats should also be evaluated in terms of their impact on the system speed, and these aspects alongside others such as data logging management should become part of the system design. * Employment of multi-parametric monitoring and modeling to enhance the accuracy of predictions, reduce false alarms and enhance the resilience of the system if some data collection modules experience faults. Rainwater forecasts, satellite ground motion data, satellite imagery, and terrestrial sensor networks provide data that can be used together to provide better forecasting models and thresholds optimizations. A semantic approach can help improve the performance of a system with a wide array of different data sources [11].