## 4 Analytical Framework for Heterogeneous Service Provisioning
### Algorithms for Rate-Adaptive Provisioning
In the absence of publicly available, granular healthcare IoT traffic datasets tailored for our experiments, we generate synthetic traces based on empirically modeled service request patterns observed in smart hospitals. The simulation includes patient monitoring, kiosk-based diagnostics, and public display services, each with distinct latency and bandwidth demands. Request arrivals follow a Poisson process augmented with diurnal variations to mimic real-world usage peaks. The LSTM model is trained on 70% of the generated traces and validated on the remaining 30%, ensuring stable prediction performance across unseen sequences. For RL, we simulate episodic training in the environment using a reward function based on latency and delivery utility trade-offs. To introduce temporal dependencies and reflect realistic traffic bursts, we train an LSTM model on synthetic sequences derived from statistically informed rate distributions. These traces simulate a 24 h workload with both periodic and bursty patterns. The LSTM predictor is further benchmarked against a classical ARIMA model to validate forecasting accuracy. The predicted bandwidth demand directly influences link pricing and capacity updates in the provisioning algorithm. In parallel, an RL control agent adapts service delivery rates in response to real-time feedback, including queue delays and link utilization. Together, these ML components enable anticipatory resource allocation and dynamic service adaptation under fluctuating network conditions. Static and dynamic energy models are also applied, with energy values for mist nodes, edge cloudlets, and the central cloud. The simulation captures both static and dynamic resource usage, allowing energy-aware evaluation of the proposed algorithms. ML-enhanced components operate asynchronously to generate demand predictions and guide rate adaptation decisions, ensuring the provisioning process remains robust to service fluctuations in real-time healthcare environments. \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value/Description** \\ \hline Gradient-based step size \(\lambda\) & 0.01 \\ Number of service consumers & 9 \\ Number of service types & 3 (diagnostics, info, Wi-Fi) \\ Number of communication links & 13 \\ Link capacity \(t_{1}\) & 20 Mb/s \\ Link capacity \(t_{2}\)-\(t_{4}\) & 16, 15, 14 Mb/s \\ Link capacity \(t_{5}\)-\(t_{13}\) & 13 Mb/s \\ Edge forwarders (mist nodes) & 3 \\ Edge cloudlet nodes & 1 per forwarder \\ Central cloud nodes & 1 \\ Mist node energy consumption & 3.5 W (static baseline) \\ Edge node energy consumption & 3.7 W (static baseline) \\ Cloud energy consumption & 9.7 kW (data center model) \\ CPU & Intel Core i7 E3-1225 \\ Processor frequency & 3.3 GHz \\ RAM & 16 GB \\ Operating system & Windows 10 64-bit \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters for smart healthcare evaluation. Traffic traces are generated using a Poisson process (\(\lambda=25\)-\(50\) req/min) with a burstiness index of 1.3 to capture temporal fluctuations. Diurnal variations are introduced via sinusoidal modulation to reflect peak hours in hospital environments. The LSTM model is trained on 70% of synthetic traces with early stopping and validated on the remaining 30% over 60 epochs. The synthetic workloads simulate service types such as real-time diagnostic kiosk queries, public health video displays, and periodic EHR lookups. These generation strategies align with patterns observed in related healthcare IoT simulations [5; 24]. ### Performance Metrics and Baselines

To evaluate the effectiveness of the proposed framework, we measure its performance across five key dimensions: latency, service delivery rate, energy consumption, bandwidth utilization, and load balancing efficiency. These metrics collectively capture the system's responsiveness, throughput, energy profile, communication efficiency, and fairness in resource distribution. #### 5.3.1 Latency

Latency represents the responsiveness of the system in delivering services.