The risk indicator is evaluated based on the volume mixing ratio of the excess CO\({}_{2}\) inhaled by uninfected people in an environment for 1 h.  
Several studies have developed a sensor system for monitoring the concentration levels of indoor CO\({}_{2}\). In [23], a sensor node was developed to monitor air quality remotely in a subway station. The design focused on the size, cost, and reliability aspects. The node uses a Non-Dispersive Infrared (NDIR)-based CO\({}_{2}\) sensor for CO\({}_{2}\) sensing and Sensorion Humidity Temperature (SHT)75 sensor for temperature and humidity sensing. It utilizes ATmega 128L as a processor and Chipcon CC2420 as a wireless communication modem. A 12 V external power supply is applied as power source. The sensor node was applied in a real subway station using a Wireless Sensor Network (WSN) system, with the measuring error rate of the CO\({}_{2}\) sensor being \(\pm\) 30 ppm. The authors in [24] developed an air-quality monitoring system for the apartment's data collection and analytics. They used an IoT system and a mechanical exhaust system to control air quality due to the occupants' schedule, behavior, and personal preferences. This research showed the possibility of integration of sensor data in a cloud system and solved the sensor network cost. In [25], a sensor node was developed and equipped with multiple sensors, a CO\({}_{2}\) (MG811) sensor, a temperature and humidity (RTH03) sensor, and a VOC (TGS2602) sensor, to monitor indoor air quality. The node utilizes an Arduino Uno microcontroller board and the Digi XBee module with the radio networking protocol of IEEE 802.15.4 as a communication device. The module can be operated as either coordinator, router or end device. The sensor node can measure CO\({}_{2}\) levels in indoor areas at around \(\pm\)500 ppm. The authors in [26] introduced an environment-monitoring system based on the IoT. It was used to monitor indoor air quality in the educational facility for a healthy learning environment. The system was built using a ThunderBoard Sense 2 wireless sensor board with a built-in CO\({}_{2}\) sensor and other sensors such as TVOC, ultraviolet index, temperature, relative humidity, air pressure, ambient light, and noise level. The sensor node communicates using a BeagleBone Black Wireless (BBBW), which had an ARM Cortex-A8 1 GHz to connect with the IoT. Three NiMh AA-size batteries with 3.7 V and 2450 mAh power are the main power source. The power source can support the sensor node for 13 days without battery shortage. The system can monitor environmental parameters in real time and provides feedback to occupants' facilities. During a busy time, the node can detect CO\({}_{2}\) concentrations up to 1200 ppm. The authors in [27] present a wireless gas sensor network for indoor environment-quality monitoring in the school area. The sensor system was built using a Programmable system-on-chip (PSoC) coupled with a Z-Wave module to connect with an internet gateway to transfer the data and combine the data with a comprehensive picture of the whole building. The integrated sensors involve CO\({}_{2}\), temperature, and humidity. The device uses an NDIR CO\({}_{2}\) sensor and an SHT21 sensor to collect CO\({}_{2}\), temperature, and humidity data. The monitoring system is also integrated with the online data platform \"EnControl\" from Sensing and Control System SL to determine the thermal comfort, ventilation rate, and production rate of CO\({}_{2}\) during a learning activity. A prototype of a CO\({}_{2}\) sensor node was developed for the building's CO\({}_{2}\) measurement and circulation management [28]. The main component of the sensor node includes a COZIR sensor, Arduino Mega for processing, and an NRF24L01 module for communication. Its measurement range is 0-10,000 ppm, with the acquired CO\({}_{2}\) data being transmitted to Rasberry Pi (node sink) using RF communication before subsequent transmission to the server node. An air-quality monitoring system based on IoT was developed in [29].