# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 4 Floods
### Reviewed Use Cases for Flood EW
When the water levels rise above a certain threshold, the device enters critical mode, making more measurements and transmitting all of them. The study showed that critical mode and dark mode operations reduce the battery life to more than half its life in normal modes of operation. [PERSON] et al. [50] also developed a solution with an heavy focus on energy consumption. Their system consists of a standard LoRa architecture, with a sensor node that communicates with a server through a gateway. The sensors used are resistance sensors, able to detect the presence of water and with a very low cost compared to other sensors, but also very simple processing since they display an on/off behavior in the presence of water. Three sensors are put at different heights in the monitored area, obtaining different thresholds and flood levels from 0 to 2. Low energy consumption is achieved using a dedicated deep sleep algorithm. Tests were made to evaluate energy consumption, especially to consider night time activity, during which the battery is not recharged by solar harvesting. Since the wake-up and data transmission phases are kept very short compared to the sleep period, it is possible to achieve good performance in terms of battery duration. While a sensor node was fully tested, the full WSN implementation was not tested, and while the system is scalable, traffic management needs to be taken into account and might not scale properly with many devices. Protocols that support mesh networks, like Zigbee, are particularly useful for areas, such as river basins, where the central node might fail, and as such mesh implementations are recommended and have been shown to provide better reliability than star or tree architectures in terms of packet loss [54]. There should be a focus on handling nodes loss in the developed solutions to make sure the WSN keeps working under difficult operating conditions. [PERSON] et al. [44] propose an EW model that exhibits high tolerance to faults and node failures, especially in harsh environmental conditions. The architecture is divided into three tiers: (i) sensing nodes, (ii) a fog computing layer, and (iii) a cloud layer. Fault tolerance is obtained by providing nodes from the first two layers with self-organizing algorithms and 'light-weight' forecasting models that allow them to make predictions if the connection to the cloud is impossible, even though with less accuracy. If nodes from the second tier are compromised, first layer nodes take the task of aggregating the data from other nodes of the same layer. Alarms and sound systems equipped to the first layer allow to make localized alarms in case the communication system is heavily compromised. To achieve this level of fault tolerance, the model requires a large number of nodes. [PERSON] et al. [53] proposed a cloud based wireless sensor and actuators network to alert from floods. The proposed system is to be deployed not only to monitor water levels so that warnings can be issued, but it also includes actuators that open watchman inlets to make water flow out if needed. Water level and volume sensors identify the status of water and compare it with a threshold. Gateways are used to manage the communication between sensors and towards the Cloud, which is used to manage the entire system. An algorithm to manage the sensors and actuator was also proposed and validated through model analysis, specifying different checks for water levels: when the 'danger' level is reached, a warning message is generated and the watchman is activated by the gateways. While the paper focused on the WSN and overall IoT architecture, it did not propose a specific communication protocol. Flood data is dynamic and non-linear, and as such using ML algorithms or Deep Neural Networks (DNN) to support the prediction is an often used solution, especially if previous flood and weather data from the area is available.