MIOT Based ADHOC Network for Remote Medical Assistant
Dr G Mary Jansi Rani, Head of the Department,
(Department of Electronics and Communication Engineering)
KGiSL Institute of Technology
Coimbatore, India maryjansirani.g@kgkite.ac.in
Sam Gladson A
Department of Electronics and Communication Engineering
KGiSL Institute of Technology
Coimbatore, India
samgladson1213@gmail.com
Vimal Kumar R
Department of Electronics and Communication Engineering
KGiSL Institute of Technology
Coimbatore, India
vimalvijay770@gmail.com
Santhika D
Department of Electronics and Communication Engineering
KGiSL Institute of Technology
Coimbatore, India
santhikadevaraj@gmail.com
Sobhika V
Department of Electronics and Communication Engineering
KGiSL Institute of Technology
Coimbatore, India
sobhikavadivelan2002@gmail.com
Abstract— This paper explores the development of a Medical Internet of Things (MIoT) platform for tracking real-time critical health metrics and emergency medical assistance in isolated or resource-scarce environments. The platform includes biomedical sensors such as the MAX30105 pulse oximeter and a digital temperature sensor for continuously tracking patient physiological parameters. Such sensors are connected with an ESP8266-based network of microcontrollers running in the self-healing mesh topology that provides stable ad hoc communication independently of traditional internet infrastructure. Such sensor data are sent to the backend server made with PHP which processes data from a K-Nearest Neighbour (KNN) classifier algorithm to diagnose and forecast potential health abnormalities. When critical conditions are detected, the system automatically sends out alarms and provides real-time diagnostic information on an LCD module attached to the patient node. Highlighting low power consumption, ease of deployment, and cost-effectiveness, this solution is especially applicable to rural healthcare, remote diagnosis, and emergency response scenarios. The paper outlines the architecture of the system, hardware software integration, implementation of machine learning, and performance evaluation, establishing its efficacy in promoting medical responsiveness by intelligent, real-time monitoring of health.
Index Terms— ADHOC System, ESP8266, Arduino IDE, PHP, KNN Algorithm, Euclidean distance metrics, MIOT, MAX30105, UART.
