Abstract

This research aims to investigate the Possibility with viability of open-source Internet of Things (IoT) and edge-compatible equipment in the field of health monitoring. The focus is on exploring various IoT health monitoring environ- ments used in e-health applications, taking into account crucial aspects such as sensor integration, data collection methods, communication protocols, secu- rity measures, scalability, and regulatory requirements. The research begins by examining existing IoT health monitoring environments to gain a compre- hensive understanding of their strengths and limitations. This analysis helps identify the gaps and challenges that open-source IoT and edge devices can address in the context of health monitoring. Building upon this groundwork,  a novel IoT-edge-powered deep learning system will be developed specifically for a targeted health monitoring environment. The system will leverage the capabilities of IoT devices, integrate various sensors to capture relevant health data, harness edge computing techniques to process data locally, and utilize deep learning algorithms for advanced analysis and inference. Special empha- sis will be placed on optimizing data preprocessing, feature extraction, model training, and overall system performance. To assess the effectiveness of the proposed IoT-edge-deep learning environment, A comparison with currently implemented solutions will be done. Key indicators will be the focus of the assessment such as precision, accuracy, and efficiency, aiming to highlight the advantages and improvements offered by the developed system. The results  of this research project are anticipated to make major contributions to the implementation of IoT and edge computing in health monitoring. By explor- ing the feasibility of open-source devices, the research will demonstrate their potential for democratizing access to health monitoring technology. Addition- ally, the exploration of e-health monitoring environments will provide valu- able insights into best practices, challenges, and regulatory considerations. Finally, the introduction of an innovative deep learning system will enhance health monitoring capabilities, enabling more accurate and timely detection of health-related conditions. This research holds promise for advancing the field of health monitoring by combining IoT, edge computing, and deep learning, ultimately raising the standard of healthcare as a whole services and patient outcomes.