## 4 Analytical Framework for Heterogeneous Service Provisioning
### Algorithms for Rate-Adaptive Provisioning
Within this environment, three types of service consumers are simulated:

* Interactive diagnostic kiosks provide patient-specific diagnostic support, lab report access, and symptom checkers. These require moderate delivery rates but have stringent latency requirements. * Informational displays broadcast hospital alerts, safety protocols, and public health information. These involve high-bandwidth, video-rich content with stable rate requirements. * Patient devices such as tablets or smartphones used by inpatients or visitors for accessing hospital Wi-Fi, EHRs, or teleconsultation. These represent latency-tolerant services. The simulation environment emulates a multi-zone smart hospital setup with three physical zones and nine service consumers. Each zone includes local mist nodes (service forwarders) that collect and process requests from nearby IoT devices, forwarding them to edge servers or a central cloud data center based on latency, delivery rate, and resource availability. The simulation models 13 network links (\(t_{1}\) to \(t_{13}\)), with varying bandwidth capacities to reflect realistic congestion points in healthcare infrastructure. We incorporate an LSTM-based time series forecast to simulate traffic variation patterns and integrate an RL agent that adjusts service rates in response to predicted load and observed delay metrics. These predictive models influence the rate adaptation and path selection process, emulating a real-world intelligent system. The setup includes one cloud node, three edge nodes, and mist-layer service forwarders. Each IoT device is assigned to a service forwarder, which performs local analytics and collaborates with the upper layers for optimal provisioning. The RL controller uses observed system states, including current buffer sizes, link load, and latency feedback, to select rate adaptation actions during each decision epoch. Figure 4 illustrates the simulation topology comprising healthcare zones, service consumers, ML modules, and mist-edge-cloud layers. The simulation models a 24 h smart hospital operation cycle with three zones and nine service endpoints. Each communication link is configured with realistic bandwidth constraints to emulate bottlenecks and prioritization scenarios. All experiments are conducted on a machine with an Intel Core i7 processor (3.3 GHz, 16 GB RAM). Each scenario is repeated over 10 independent trials to account for stochastic variability, with metrics averaged and reported using 95% confidence intervals. The RL-based controller operates with 15 s decision epochs and uses reward functions based on delivery rate improvement and latency minimization to guide adaptive actions. ### Simulation Parameters

Table 2 summarizes the key simulation parameters used in evaluating the ML-RASPF framework. The simulation environment models an IoT-enabled hospital system comprising diagnostic kiosks, informational displays, and patient devices. Each consumer

\[g_{s}^{t}=\max_{\{i|t\in T_{s,i}\}}g_{s,i}, \tag{17}\]5 Simulation topology for ML-RASPF with mist-edge–cloud architecture, predictive traffic and adaptive control modules. Each zone includes a mist layer, an edge cloudlets layer, and a diverse mix of latency-sensitive and latency-tolerant healthcare service consumers. device is associated with one of three distinct service types: latency-sensitive (real-time diagnostics), bandwidth-intensive (video-based displays), or latency-tolerant (Wi-Fi and EHR access). We simulate 13 communication links with different bandwidth capacities, reflecting the heterogeneous nature of real-world hospital networking infrastructure. Link \(t_{1}\) represents a high-capacity backbone connection, while \(t_{2}\) to \(t_{4}\) simulate medium-capacity inter-zone links. The remaining links (\(t_{5}\) to \(t_{13}\)) represent lower-bandwidth edge-to-device connections.