## 3 Communication Protocols, and IoT Challenges for Pandemic Control
The sensors used in the application (temperature, respiratory, accelerometer, pulse, SpO\({}_{2}\) and GPS--global positioning system) are distributed on the patient'sbody. The communication with the main microcontroller unit (MCU) is achieved using a BLE communication link, same as the above related solution. The MCU forwards the measurement information to a local server through an Internet connection. The system provides a solution for the indoor GPS signal limitation by measuring the RSSI (receive signal strength indicator) of the Wi-Fi signal between the sensor device and the locally deployed server. Alerts and notifications are sent to the local authorities if the quarantine conditions are violated. This system is presented as being able to monitor multiple patients, but the capability is reduced due to the low coverage area of the Wi-Fi communication. Another disadvantage is the high complexity of the system on the sensor level, as the sensors are distributed on different areas of the body being mostly a proof-of-concept system, unavailable as an integrated wearable device. [PERSON] et al. [29] present an IoT enabled solution for patient monitoring, using a rule-based approach for determining the current state of the patient health. As with the previous presented system [28], the sensors are distributed on the patient's body to collect data, regarding the pulse, SpO\({}_{2}\), temperature and cough rate. The measurements are sent to the cloud by an 802.11n communication protocol (Wi-Fi), where they are processed and analyzed. The authors define four patient classes, each patient being attributed a class through a rule-based classification process. The classes are summarized in Table 2.  
A similar patient monitoring system is presented in [30]. The system uses an accelerometer sensor and two temperature sensors (a contact one and an IR--infrared sensor) for movement and body temperature measurement, respectively. Additionally, the system includes an ambient temperature and a humidity sensor to detect and warn the patient if the room parameters are off the limits. The measurements are sent to the web-based application via MQTT (message queue telemetry transport) using a BLE connection. The whole system is implemented into a M5 stickC device, being an off-the-shelf embedded solution for a multi-application fast prototyping system. Some published works propose COVID-19 tracing applications using Bluetooth low energy in order to track and monitor the spread of the virus [31; 32]. These applications enhanced with ML capabilities can determine persons that are at risk of being infected because they were in the proximity of a confirmed infected person. During our evaluation regarding the literature survey, three major disadvantages have been identified, one being related to the communication protocol used for patient monitoring systems. In this category, the lack of scalability due to the low communication range of the technology used to send the sensor data to the monitoring center, is a serious problem, which is not available when LoRaWAN protocol is used. Another disadvantage is related to the lack of energy efficiency of the proposed systems because they use a constant communication link, that drains the battery of the monitoring device very fast. The last major disadvantage is related to the hardware architecture modularity. Few monitoring systems use sensors in a wearable device that are comfortable and easy to wear. Thus, most of the solutions are related as a proof-of-concept design, being impossible to add new sensors to extend their functionality.