The implementation of the Internet of things in healthcare is a promising challenge to achieve coverage for a bigger number of users in different places at lower costs. Internet of things might mean better technology regarding response time and proper control of medical parameters. This study introduces an Internet-of-things system for healthcare with possibilities to control medical variables according to recent breakthroughs in sensors and data processing. The goal of the system is to optimize the development of applications to obtain variables in real time and with less energy consumption. The proposed model is validated on the measurement and monitoring of oxygen saturation, heart rate, and body temperature in patients with respiratory disorders. This was achieved by the optimization of data acquisition, integrated into a secure architecture using Message Queuing Telemetry Transport protocol. A cloud architecture with interconnection to low-cost and open-source devices was implemented, which interconnect to the sensors and actuators’ network. The experimental results were statistically treated against the device pattern data, through hypothesis tests for mean differences to probe the accuracy of the model. Finally, the proposed model demonstrates an efficient performance in several clinical parameters, such as oxygen saturation and heart rate per minute.