## 5 Discussion  
By incorporating multi-sensor fusion, real-time biomedical signal processing, and efficient data transmission approaches, the Monit4 Healthy system enables a cohesive structure for IoT-enabled health monitoring. The system addresses challenges related to sensor interoperability, reliable information exchange, and real-time monitoring features in order to address meaningful challenges in IoT-driven healthcare. Its multi-layered architecture allows highly effective data gathering, edge processing, and secure communication protocols, providing accurate physiological and biomechanical data analysis. Its usefulness across connected health ecosystems is sustained by laboratory validation, which demonstrates its capacity to provide responsive signal processing, continuous monitoring, and comprehensive health data management. _Main contributions and foreseen performance impact_  
The Monit4 Healthy system's _structured data transmission process_ has been designed as one of the most important factors that aims to decrease energy consumption and network congestion while improving real-time biomedical signal processing. Future assessments will be carried out to establish the extent of these expected improvements. Only relevant and processed health parameters are transmitted owing to the system's edge-level data preprocessing, responsive data selection, and event-driven alert mechanism. It is a better alternative for typical IoT-based health monitoring systems as it successfully minimizes data redundancy while maintaining high data integrity and real-time responsiveness. Preprocessing based on edge computing is a key element of this approach, since it considerably decreases the amount of raw health data sent to the cloud. In order to make sure that only the most relevant health parameters can be processed into optimum datasets, physiological signals collected through sensors like ECG, EMG, PPG, and GSR are subject to initial filtering, feature extraction, and aggregation at the data gathering stage. By reducing used bandwidth in the same time with preserving signal reliability, this process allows for smooth data transmission while not burdening computational resources. The Monit4 Healthy system updates its communication protocols according to network scenarios and the data level of importance to further enhance structured data transmission. Wi-Fi transmission is used for the high-resolution transmission of health data when mandatory, while BLE is applied for short-range, energy-efficient data sharing. In order to facilitate the real-time detection of abnormalities and multi-sensor interaction, the system likewise makes sure that delivered data are structured with timestamps, sensor identifiers, and abnormal flags. The Monit4 Healthy system's event-driven trigger alert system, which evaluates measured parameters according to clinical relevance, is a prominent part of its structured transmission process. Instead of continuously transmitting gathered data, the system assesses patterns in medical standards and ultimately triggers an alert when particular limits are surpassed. For instance, typical transmission cycles are overridden by a persistently aberrant ECG rhythm, guaranteeing timely response. In contrast, data are aggregated and sent at predetermined intervals when physiological values remain within normal limits. This minimizes excessive network congestion while providing medical professionals with thorough and organized insights. Maintaining low-latency, energy-efficient, and high-integrity transmission of data while preserving real-time responsiveness is a significant challenge in IoT-enabled health monitoring. The Monit4 Healthy system was designed with a hierarchical communication approach that incorporates edge computing to minimize needless data transmission.