# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 4 Floods
### Reviewed Use Cases for Flood EW

Table 1: Reviewed Articles in Flood EW. While successful, [PERSON] et al. [46] pointed out that this kind of solution has the downside of needing many different kinds of nodes and dealing with heterogeneous data; therefore it is more expensive and it requires more complex computation. They propose a generic and simple EW architecture to solve this and other issues such as power consumption, resilience in absence of cellular coverage, less delay and shorter computations. The architecture consists of flow and water level sensors that send data to a server only when sensed data rises above a fixed threshold. An android app would be used for mobile users, working with Zigbee hardware that is connected to the mobiles via USB On-The-Go module for communication, so that the system works even if cellular coverage is down, but this approach would require users to be equipped with said USB On-The-Go system. In the flood EW IoT-based architecture proposed by [PERSON] et al. [26] the IoT layer is responsible for collecting and aggregating data from sensors and the Fog computing Layer is used to decrease latency by pre-processing the raw data from the sensors before sending it to higher layers, which is useful to ensure timely predictions and to lower the amount of data sent to the cloud. A forecasting model is also implemented in the fog nodes. The framework proposed in [26] also introduces a variable size hexagonal division of the monitored space to cover large areas while providing uniform sensing, optimized cost requirements and energy saving based on the probability of flood in each hexagonal division: by defining five probability-of-floods labels, the hexagons in which a monitored area is divided can be categorized so that only hexagons with a high probability of flood have all the sensors active, while devices in low probability areas can be put in sleep mode, minimizing energy waste. Taking advantage of existing telecommunication infrastructures for connectivity is often one of the best and easiest solutions, as shown in the use case developed by [PERSON] et al. [47] in Colima, Mexico, which use the 3G network and the Message Queue Telemetry Transport (MQTT) protocol to send data to a remote Cloud server for data processing and storage. The system also employs drifters (mobile sensor nodes) with LoRa modules which forward data to the 3G connected nodes. The great amount of data from different data sources and the reliable communication network make the solution successful in providing timely warnings, but it comes with a high cost. Cellular networks generally offer good coverage in most locations and future 5G deployments will further improve their performance. This is not always true, as certain locations might instead have bad coverage because of Non Line of Sight (NLoS) propagation problems or because there are not enough base stations to properly cover the entire area where the sensors are deployed. In this case, a better solution might be achieved creating ad hoc networks using protocols such as LoRaWAN, as observed by [PERSON] et al. in [48]. They evaluated the radio performance of both LoRaWAN and GSM in a rural environment with weak cellular coverage and with different cellular network providers in the case of GSM. They showed that 2G technology is not always suitable for this kind of application and that there is a need to migrate to newer technologies such as LTE or NB-IoT, while also proving LoRaWAN potential in this context. Critical situations such as water levels rising over a certain threshold might require the sensors to make more measurements, with a much bigger energy consumption. For sensors equipped with photo-voltaic systems, adverse weather conditions can compromise the recharging of batteries. [PERSON] et al. [49] took into account these factors to evaluate the energy budget of their developed solution based on EnOcean for better planning before deployment. They used a generic ultrasonic sensor which has two modes of operation: normal mode and critical mode.