## 5 Discussion
In contrast with typical IoT health monitoring solutions that depend on constant raw data streaming to cloud-based infrastructures, this approach is expected to improve power efficiency and network resource allocation, and future evaluations will assess the performance of Monit4 Healthy under real medical conditions. Prior to being sent to an ICIPRO cloud-based server, collected data first go through edge-level filtering, feature extraction, and structuring. While maintaining data integrity and system responsiveness, this approach successfully lowers network congestion, maximizes bandwidth usage, and supports appropriate real-time monitoring. The Monit4 Healthy system's fusion of edge and cloud computing is strengthened by the fact that edge-level processing reduces the need for constant raw data streaming to cloud servers by enabling real-time filtering, feature extraction, and data structuring. This approach significantly lowers the amount of data transmitted, which consequently decreases network congestion and improves reaction time for critical alerts compared to conventional cloud-only architectures. The system can handle time-sensitive tasks at the edges, like identifying anomalies and providing event-driven alerts, using a Raspberry Pi as a local processing unit. Solely necessary structured data for long-term storage and analysis are offloaded to the ICIPRO cloud infrastructure. To illustrate the benefits of integrating edge computing in the Monit4 Healthy system, a comparative analysis was conducted under laboratory conditions, focusing on latency and bandwidth usage in two configurations: edge-cloud hybrid (Monit4 Healthy system) and cloud-only. In both scenarios, physiological data from ECG and PPG sensors were collected and processed. Edge-level preprocessing, performed on Raspberry Pi, reduced the volume of transmitted data by approximately 70%, as only filtered and feature-extracted data packets were sent to the cloud, compared to raw signal transmission in the cloud-only setup. For a 60 s monitoring session with ECG sampled at 250 Hz and PPG at 100 Hz (16-bit resolution), cloud-only transmission required approximately 0.040 MB per minute, while edge-cloud processing reduced this to around 0.012 MB/min. Latency measurements, defined as the time from data acquisition to cloud reception and processing, were reduced from an average of 350 ms (cloud-only) to 120 ms (edge-cloud), representing a 65% latency improvement. This efficiency supports scalable deployment in environments with constrained network resources and enables real-time responsiveness, crucial in health monitoring applications. While these results are preliminary and limited to controlled laboratory testing, they validate Monit4 Healthy's design objectives and establish a technical foundation for future benchmarking in clinical environments. The Medical Blackbox device is powered by an external adapter in the current lab setting so that it can operate continuously. The proposed design takes into account the overall utilization of the active biomedical sensors, ESP8266 module, and Arduino Mega. Although these findings are suggestive, a thorough energy determination evaluation will be conducted to evaluate runtime variability in various operating scenarios. A more thorough quantitative comparison between edge-enabled and cloud-only configurations--measuring specific metrics like transmission delay, data volume, and processing time--is planned in the system's deployment roadmap, despite early laboratory evaluations confirming that edge computing contributes to lower latency and optimized bandwidth usage. In order to compare the system's performance in real-life scenarios and to further assess the technological advantages brought about by the edge computing architecture, this future analysis will incorporate simulation-based and physical testing.