## 5 Discussion
_Estimated performance contributions of the Monit4 Healthy system_  
With the goal of enhancing real-time biomedical monitoring, the Monit4 Healthy system implements structured data transmission, edge computing abilities, and optimized sensor communication. The architectural design and laboratory testing and validation results highlight improvements in key performance aspects, such as latency reduction, bandwidth efficiency, and energy optimization, although formal large-scale benchmarking remains an area for future research. * _Latency optimization:_ The minimization of transmission delays is accomplished by employing local edge-level preprocessing, therefore reducing the volume of raw biomedical signals being delivered to cloud infrastructures. By substituting the mere reliance on cloud-based processing with real-time physiological filtering and feature extraction occurring at the acquisition level, the system ensures that it is clinically relevant and only transmits well structured data. This approach is expected to reduce latency in scenarios of high-priority health monitoring, such as in detecting arrhythmias based on ECG data, when event-triggered alerts are dire. * _Bandwidth utilization efficiency:_ The Monit4 Healthy system implements adaptive data selection mechanisms, with the purpose of improving network efficiency. To reduce redundant data transmissions, the system replaces the approach of continuously streaming high-frequency signals with periodic reporting and event-triggered alerts. Given that network congestion and resource allocations need to be optimized when designing remote monitoring systems, this design is specifically efficient. The system's dual-mode communication approach--using BLE for short-range sensor connectivity and Wi-Fi for higher-resolution data transmission when necessary--allows scalable and energy-efficient data exchange. * _Energy consumption considerations:_ The system improves energy efficiency by implementing a structured power management strategy, minimizing cloud reliance by passing computational tasks to local processing units. To optimize sensor-device interactions, the system utilizes BLE-based communications protocols, which have established low-power characteristics. Further quantitative analysis is required in order to validate the impact of these features in continuous monitoring scenarios, although they are anticipated to enhance energy-efficient operations. The reduction in continuous high-power transmissions, combined with the event-driven adaptive communication is anticipated to lengthen the lifespan of wearable and portable monitoring units. Despite these projections, battery consumption metrics need prolonged real-world evaluation. To additionally improve energy efficiency in extended health monitoring scenarios, future research could focus on duty-cycling mechanisms and machine learning-driven power scheduling. _Limitations and challenges_:
* Real-world clinical assessments are still unexplored, despite laboratory testing confirming the system's fundamental functioning. Future research is required to evaluate the Monit4 Healthy system's effectiveness in dynamic real-world settings, especially where network unpredictability, patient mobility, and telemedicine integration are involved. * Another challenge is scalability, especially when it comes to easy integration with current telemedicine solutions and healthcare IT infrastructures. Even though the system's modular architecture allows for flexibility, further testing is necessary to guarantee that it can handle extensive medical applications. * Measurements for the long-term power usage of continuous monitoring have not yet been assessed apart from controlled laboratory settings. Although edge computing decreases redundant data transmission, energy efficiency is expected to be boosted using adaptive power management approaches, especially for wearable and portable applications; future experimental validations need to be performed to identify the practical advantages of these approaches in real-word settings.