This project presents a comprehensive study on the design and implementationof an Internet of Things (IoT)-based system for monitoring and alerting salinevolumes in healthcare environments.Background: In healthcare settings, the accurate monitoring of saline volumesin Intravenous (IV) drip systems is crucial for ensuring patient safety and effectivetreatment. Traditional monitoring methods are labour-intensive and prone to human error. The IoT offers promising solutions for automating and enhancing themonitoring process.Objectives: This thesis aims to develop an IoT-based saline volume monitoring and alert system using NodeMCU, a load sensor, an amplifier, the ThingSpeakcloud platform, and the Massachusets Insitute of Technology (MIT) App Inventor.The system is designed to improve the accuracy and efficiency of saline volumemonitoring while reducing the burden on healthcare professionals.Methods: The proposal system employs a Node MicroController Unit (NodeMCU)microcontroller for data processing and communication, a load sensor for monitoring the saline volume, and a buzzer alarm and amplifier for alerting healthcareprofessionals when the saline volume reaches a critical threshold. The system connects to the ThingSpeak cloud platform for data storage and analysis, facilitatingremote monitoring and control through a custom mobile application developedusing MIT App Inventor.Results: The implementation and testing of the system showed accurate and reliable monitoring of saline volumes in real-time, with efficient alerting mechanisms.The user-friendly mobile application enabled healthcare professionals to monitormultiple IV drip systems simultaneously, receiving timely alerts when interventionwas required.Conclusions: The IoT-based saline volume monitoring and alert system demonstrates the potential to improve patient safety and healthcare efficiency. Furtherresearch and development can explore the integration of additional sensors, the refinement of the alert system, and the assessment of the system’s impact on clinicaloutcomes..

This master thesis deals with the topic of Internet of Things and its use in technical diagnostic. In theoretical part, there is a description of IoT systems and its parts, history of the IoT, communication modules and cloud systems. The second part of the thesis is focused on the description of technical diagnostic, diagnostic signal, and detailed description of two methods, most appropriate for use in IoT systems and that is vibrodiagnostic and thermodiagnostic. The practical part is focused on the application of the IoT system in technical diagnostics, including the measurement of vibrations and temperature, on the description of application in cloud system and analysis of measured results in cloud system. Prior to the end the more actions in this field of IoT systems has been recommended.

The topic of this thesis is the design and realization of a modular Data concentrator for various types of measurements in diverse conditions. The device conception, basic principles, design and functionality are described herein. Also, the lightweight protocol design for radio communication, standardized XML format for data storage and cloud usage are described. The electrical design of the device as well as the design of the printed circuit board was made using the Eagle Autodesk electronic design automation software (Eagle Autodesk EDA). The control software written in C/C++ for a target microcontroller (ESP32-WROOM-32) is based on a FreeRTOS platform and ESP-IDF framework. An IDE for managing this software is Visual Studio Code with PlatformIO extension. Selected and used Cloud Platform is ThingSpeak from Mathworks, which uses certain components from Matlab platform.