## 5 Discussion
_Future work_ involves further research in several important directions in order to improve the Monit4 Healthy system's practical relevance:
* Carrying out extensive experiments to assess system performance in genuine clinical and digital health settings. * Implementing a full English language option to improve usability for international users. * Connecting EHRs and other type of healthcare solutions to provide simple interoperability and data sharing. * Investigating hybrid IoT communication models that provide high-bandwidth, low-latency medical data transmission by integrating 5G-enabled frameworks. * Future developments focusing on implementing advanced multi-sensor fusion algorithms, enabling the generation of composite health indicators by correlating synchronized ECG, EMG, PPG, and GSR data. * Developing predictive analytics powered by AI to improve the automated identification of abnormalities and individualized health monitoring. * To optimize network efficiency, current implementations are targeted toward structured data transmission. Despite ongoing approaches, future research could employ adaptive power management techniques, such as dynamic sensor sampling rate adjustments, to allow the system to reduce data acquisition frequency, and duty-cycling mechanisms, to power down non-essential components during periods of inactivity. To further lengthen the battery life, energy-aware task scheduling could be explored as well. * Future experimental validations will quantitatively analyze Monit4 Healthy's energy consumption across various operating conditions, assessing the real impact of edge computing and adaptive data transmission on power efficiency. * Integration with IoT-specific protocols like MQTT is being considered for future development phases, particularly to improve scalability and interoperability in smart city ecosystems or more intricate, dispersed healthcare infrastructures. This will further align the Monit4 Healthy system with IoT communication standards by enabling efficient device-to-device and device-to-cloud connection. * The potential benefits of edge computing will be addressed in more depth. This consists of real-world and simulation-based comparisons of edge-enabled and cloud-only setups, with an emphasis on important performance indicators including power consumption, processing time, data volume, and transmission latency. The findings from these investigations will allow for the additional optimization of the Monit4 Healthy system's edge-cloud integration strategy and quantitatively validate the architectural benefits of the system across various healthcare situations. * Comprehensive clinical accuracy benchmarking against certified medical equipment is scheduled during real-world deployment phases to validate sensor performance under operational healthcare conditions. * Adaptive communication strategies that dynamically optimize protocol selection and channel allocation in real time, aiming to enhance transmission reliability and energy efficiency in high-density and variable network environments, will be taken into consideration. * Miniaturizing the Medical Blackbox by designing a custom PCB to replace the current prototyping board aims to optimize the device's size, improve power efficiency, and enhance hardware reliability for long-term deployment in both clinical and home-based settings. * Future research will involve thorough power profiling under a range of operating situations, such as continuous monitoring, event-driven alerts, and inactivity states, in order to comprehensively assess the energy efficiency of the Monit4 Healthy system. All system components' current drawing and consumption trends will be tracked using standardized energy measuring instruments. We will analyze metrics like overall battery duration under particular tasks, energy per transmitted data unit (mWh/MB), and average energy consumption (mW).