Reconfigurable IoMT Framework for 5G Network Communication with Experimental Validation on ESP32
Medical Internet of Things; IoMT requirements; ESP32; FreeRTOS; reconfiguration.
The Internet of Medical Things (IoMT) expands the use of connected devices in healthcare, but faces challenges related to data reliability, power, interoperability, security, and communication adaptation. In this context, this dissertation uses requirements identified in the Digital Health literature to guide the development of a Reconfigurable IoMT Framework for 5G Network Communication with Experimental Validation on ESP32, using FreeRTOS and MQTT over TLS (MQTTS) communication. The architecture integrates sensor acquisition, local processing, secure communication, and external infrastructure, allowing operation in continuous and event-driven modes. The device can request mode changes, while the network can accommodate them or perform proactive reconfigurations independently. The validation used two thermal sensors and one inertial sensor, with indirect access to the 5G infrastructure via mobile hotspot and experimental representation of the network decision by a human operator. From a future perspective, the framework was designed to allow the embedded system to interact directly with the available communication infrastructure, whether it be the 5G network or another technology that may assume this role, maintaining the request and reconfiguration logic regardless of the access technology used. The results demonstrated coherent behavior between acquisition, processing, and transmission, preservation of physical quantities and structured metadata in payloads, operation of the local logic, reactive and proactive network reconfigurations, and temporal behavior compatible with the different sensory profiles evaluated. The temporal analysis also allowed the characterization of the local, external, and end-to-end components, as well as the closure of reactive cycles. The energy analysis allowed the characterization of the consumption of the configurations and the estimation of their autonomy. It is concluded that the framework constitutes a viable approach to integrate sensing, local processing, communication, and reconfiguration in IoMT applications, maintaining potential for evolution towards automated decision mechanisms, new physiological sensors, and direct cellular connectivity.