## 3 Optimal Service Provisioning Framework  
The proposed ML-RASPF framework is based on distributed data processing principles to enhance resource utilization and ensure QoS in latency-sensitive smart healthcare environments. As shown in Figure 2, the architecture consists of five coordinated layers that collaboratively manage end-to-end service provisioning across mist, edge, and cloud \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Approach** & \begin{tabular}{c} **ML-** \\ **Based** \\ \end{tabular} & \
\begin{tabular}{c} **Latency-** \\ **Aware** \\ \end{tabular} &
\begin{tabular}{c} **Rate-** \\ **Adaptive** \\ \end{tabular} & **Architecture** & **Domain** & **Key Limitations** \\ \hline [PERSON] et al. [14] & \(\times\) & ✓ & \(\times\) & Fog-Cloud & Healthcare & High overhead due to tensor mapping, lacks adaptiveness under dynamic loads. \\ \hline [PERSON] et al. [12] & \(\times\) & ✓ & \(\times\) & Edge-Cloud & Crowdsensing & Assumes deterministic environment, lacks real-time adaptability. \\ \hline [PERSON] et al. [8] & \(\times\) & ✓ & ✓ & Fog-Cloud & IoT/Healthcare & Uses metaheuristics; lacks learning-based decision-making or predictive models. \\ \hline [PERSON] et al. (JANUS) [7] & \(\times\) & ✓ & ✓ & Edge-Cloud & Streaming & Queue-based heuristic stream selection; lacks proactive learning and mist integration. \\ \hline [PERSON] et al. [9] & \(\times\) & ✓ & ✓ & Edge-Cloud & General IoT & Focus on queue-aware migration; not suitable for highly variable healthcare demands. \\ \hline [PERSON] et al. [20] & ✓ & \(\times\) & \(\times\) & Edge-Cloud & Healthcare & Focus on privacy with FL; lacks delivery rate optimization and latency support. \\ \hline [PERSON] et al. [17] & ✓ & \(\times\) & \(\times\) & Fog-Cloud & IoT–Vehicles & Lacks training scalability and rate adaptation; accuracy affected by data gaps. \\ \hline [PERSON] et al. [18] & ✓ & ✓ & \(\times\) & Edge-Cloud & Smart City & DRL adds processing delay; lacks fine-grained control in mist layers. \\ \hline [PERSON] et al. [15] & \(\times\) & ✓ & \(\times\) & Cloud–Edge & SRC & Multi-objective model, but lacks ML support and ignores dynamic adaptation. \\ \hline ML-RASPF & ✓ & ✓ & \(\times\) & Mist–Edge–Cloud & Healthcare & Integrates ML forecasting + RL adaptation in modular real-time architecture. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of recent service provisioning approaches in IoT and smart healthcare environments. infrastructures. To illustrate its real-world applicability, we present a smart healthcare use case focused on continuous patient monitoring and emergency response. In this scenario, IoT-enabled medical devices, such as wearable health trackers, bedside monitors, and mobile diagnostic units, collect real-time physiological data from patients across various hospital zones or remote care settings. These data include metrics such as heart rate, blood pressure, oxygen saturation, and mobility patterns, all of which require timely and reliable processing to enable prompt medical interventions. A critical requirement in such a healthcare setup is the ability to perform localized, on-demand analytics with minimal latency. For instance, edge cloudlets deployed within hospital premises can process time-sensitive alerts, such as detecting arrhythmias or fall events, before forwarding relevant summaries to central hospital servers or cloud-based electronic health record (EHR) systems. Data from non-critical or latency-tolerant services, such as periodic wellness logs or historical diagnostic trends, can be cached and analyzed at the cloud layer when needed. The proposed framework supports energy-efficient sensing, data transmission, and real-time service delivery by adaptively allocating resources across its layers. This layered orchestration not only ensures low-latency responses for critical healthcare events but also maintains consistent service delivery rates for ongoing data streams.