Banca de QUALIFICAÇÃO: LEMUEL CLÉCIO DE ARAÚJO

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
STUDENT : LEMUEL CLÉCIO DE ARAÚJO
DATE: 29/06/2026
TIME: 08:00
LOCAL: ECT - Sala 4
TITLE:

IoMT Framework for ESP32 with Supervised Reconfiguration and Communication via 5G Infrastructure

KEY WORDS:

Internet of Medical Things; IoMT; Embedded Systems; ESP32; FreeRTOS; MQTT; Supervised Reconfiguration; Digital Health.

PAGES: 98
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUBÁREA: Circuitos Elétricos, Magnéticos e Eletrônicos
SPECIALTY: Circuitos Eletrônicos
SUMMARY:

The Internet of Medical Things (IoMT) has significantly expanded the use of connected devices for monitoring, acquiring, and transmitting health information. However, the development of IoMT solutions involves challenges related to reliable data acquisition, processing and power limitations of embedded devices, interoperability, information security, and dynamic adaptation of communication. In this context, this dissertation presents the development and experimental validation of a reconfigurable embedded framework applied to IoMT, implemented on the ESP32 platform with FreeRTOS, using MQTT communication and a supervised reconfiguration architecture. The methodology adopted included a structured analysis of the technical and normative literature, the consolidation of technical, security, and regulatory requirements, the definition of the framework architecture, and its experimental validation using the DS18B20, MAX30205, and MPU6050 sensors. The architecture was organized into functional blocks responsible for sensor acquisition, embedded processing, secure communication, and external supervision, allowing operation in continuous and event-driven modes, with supervised reconfiguration via MQTT messages. The results demonstrated that the framework was able to operate consistently under different sensory profiles, preserving coherence between local acquisition, embedded processing, and data transmission. The analyses showed temporal behavior compatible with the physical characteristics of the evaluated sensors, adequate functioning of the local decision mechanisms, dynamic adaptation of the operational modes, supervised communication via MQTT, and the use of transmission strategies capable of directly influencing energy consumption and system autonomy. Additionally, the results allowed experimental association of the observed behavior with the requirements identified in the literature, demonstrating the applicability of the proposed architecture as an embedded solution for digital health scenarios. It is concluded that the developed framework constitutes a viable approach for integrating sensing, local processing, operational supervision, and communication in IoMT applications, providing a reconfigurable architecture capable of adapting its transmission policy according to different acquisition dynamics. Future perspectives highlight the integration with additional physiological sensors, the incorporation of artificial intelligence mechanisms to support operational supervision, and the use of dedicated cellular modules to expand communication capabilities in distributed IoMT environments. The Internet of Medical Things (IoMT) has significantly expanded the use of connected devices for monitoring, acquiring, and transmitting health information. However, the development of IoMT solutions involves challenges related to reliable data acquisition, processing and energy limitations of embedded devices, interoperability, information security, and dynamic adaptation of communication. In this context, this dissertation presents the development and experimental validation of a reconfigurable embedded framework applied to IoMT, implemented on the ESP32 platform with FreeRTOS, using MQTT communication and a supervised reconfiguration architecture. The methodology adopted included a structured analysis of technical and normative literature, consolidation of technical, safety, and regulatory requirements, definition of the framework architecture, and its experimental validation using the DS18B20, MAX30205, and MPU6050 sensors. The architecture was organized into functional blocks responsible for sensor acquisition, embedded processing, secure communication, and external supervision, allowing operation in continuous and event-driven modes, with supervised reconfiguration via MQTT messages. The results demonstrated that the framework was able to operate consistently under different sensor profiles, preserving coherence between local acquisition, embedded processing, and data transmission. The analyses showed temporal behavior compatible with the physical characteristics of the evaluated sensors, adequate functioning of the local decision mechanisms, dynamic adaptation of operational modes, supervised communication via MQTT, and the use of transmission strategies capable of directly influencing energy consumption and system autonomy. Additionally, the results allowed for the experimental association of the observed behavior with the requirements identified in the literature, demonstrating the applicability of the proposed architecture as an embedded solution for digital health scenarios. It is concluded that the developed framework constitutes a viable approach for integrating sensing, lo-processing, and other technologies.

COMMITTEE MEMBERS:
Presidente - 1555898 - DIEGO RODRIGO CABRAL SILVA
Interno - 4523538 - MARCELO BORGES NOGUEIRA
Externo ao Programa - 2614800 - MARCONI CAMARA RODRIGUES - UFRN