## 4 Results
### Examined Works
The system is based on an ionization chamber to continuously sample radon levels in a procedure very similar to the real-time situation. The device measures radon concentration, air temperature, relative humidity, atmospheric pressure, some high-level air quality indicators, and has Wi-Fi and Sigfox connectivity. The system architecture has two main components: the sensors and the server. The sensors will send information periodically to the server, which will store the data and make a linear regression-based prediction of the radon level for each device. The alerts are based on MQTT messages that trigger events to inform interested parties by email, either that the levels are above the threshold or back to normal. The corrective actions must be taken by a human operator (opening windows, turning on airflow control systems, etc.). This work also considers the security of end-to-end communication (HTTPS with SSL) to avoid data forging attacks. To address secure networking, it uses an open-source firewall mainly for load balancing, web content filtering, and reverse proxying for TCP and HTTP-based applications. In [74], [PERSON] et al. give sequence to the work presented in [73]. This research ex-tends the design implemented in [73] and implements the capability of automatically controlling airflow systems based on the radon concentration as a measure for air quality predictions. Besides this variable, it also monitors the temperature, barometric pressure, relative humidity, and total volatile organic compounds (TVOC). The main objective is to create a fully autonomous system that would maintain the air quality at a certain level. For that, they developed a control device that can be attached to an existing ventilation system and can receive commands from the server to switch it to on/off based on the de-fined metric. If the algorithm predicts a decrease in air quality, it sends a command to the control device to turn on the airflow system they are attached to and located in the same room as the monitoring devices. After the experimental results, the system reactivity was better with a two-hour interpolation window which provided the lowest mean radon concentration, considering as metrics both the measured radon concentrations and the exposure time to high radon concentrations. The authors concluded that the system could achieve an 86% reduction in the radon concentration, maintaining it low for 90% of the time, by having a ventilation system on during only 34% of the time (every week), demonstrating that it is possible to keep indoor air safe using low-cost resources. In [75], [PERSON] et al. presented the first network of outdoor air radon sensors for monitoring radon diluted in the air of a volcanic environment. To achieve this purpose, a commercial low-cost radon sensor (Algade \(\AE\)R Plus) was tropicalized to be operated continuously in harsh conditions (mainly high temperatures and acidic corrosion) and high altitudes with autonomy for several months. The main intention of this work is to study a possible way to predict volcanic eruptions. The system consists of the installation of two stations on Mount Etna volcano, in Italy, at ~3000 m of elevation and the deployment of a private network to transfer the measurements from the stations directly to a server in France. LoRaWAN technology was chosen to connect the sensors, due to its tweaking capabilities, and include a gateway connection to the Internet by SIM card (3G). The two ra-don stations are connected to specific LoRa end nodes that transfer the frames, that contain a few measurement results, to the gateway. The latter then sends the data over the Internet to a data lake, structured with an open-source LoRaWAN Network Server stack. The authors concluded that the sensor tropicalization has proven to be very efficient under harsh conditions for near real-time surveillance of active volcanoes.