## 8 Conclusions  
This article explores a very relevant application area for society, considering the potential underlying Long-Range (LoRa) telecommunications equipment and devices that are currently available on the market at low cost, but with high potential. The classification of IoT, directly related to the use of these miniaturized devices in the field of ubiquitous and pervasive computing, provides an excellent opportunity for their use in the follow-up and monitoring of elderly people and in the monitoring of their homes, namely with sensors that can detect floods, gas leaks, excess carbon monoxide, fires and other data.  
The use of LoRa and TTN networks is specifically targeted at agricultural production and farming as well as the monitoring of environmental conditions in cities. Our approach, by introducing aspects related to the monitoring of people who are in a particularly vulnerable situation, especially the elderly, derived from COVID-19, is a challenge for us. We believe that we have demonstrated that it is possible to monitor people and their homes, offering them more security as well as a low-cost system, while ensuring their privacy.  
Nevertheless, there are some barriers to the mass use of systems based on this technology, first of all due to the weak network of gateways available in the region, making it necessary to carry out studies on the implementation of equipment that reduces the shadow zones and allows redundant coverage.  
Another relevant factor is related to the frequency of sending data and the volume of data produced, since LoRa networks are not designed to send large volumes of data, nor to be permanently connected in order to send data at a high frequency, for example, every 5 s. They are usually designed to send data in intervals of several minutes (10 to 30 min or more), which, to guarantee assistance to individuals in danger or who have experienced an accident, may be too long and may save lives or minimize risks. For this reason, the use of Artificial Intelligence with machine learning algorithmscan make an important contribution, foreseeing and anticipating risk situations and minimizing the probability of risky events occurring.  
As in all data collection systems with continuous processes, having production databases in Online Analytical Process (OLAP) mode would be ideal; however, given the restrictions mentioned above, it is not currently possible to have systems that are capable of continuous data analysis processes. Thus, the inclusion of data mining, machine learning and Artificial Intelligence algorithms will have to operate on previously prepared databases rather than on production databases, using the concept of data warehousing, with pre- and post-processing. However, this does not invalidate the fact that, through Node-RED, a continuous connection for data analysis can be established, for example, by Message Queue Telemetry Transport (MQTT), which is the standard for IoT messaging.  
Finally, it should be noted that, in order to guarantee all ethical and data protection principles in future work, the National Data Protection Commission will be informed of the objectives of our system and the way in which it functions. Anonymous data collection authorization will be requested, for statistical purposes only, such as for academic and scientific research.