## 4 Analytical Framework for Heterogeneous Service Provisioning
### Algorithms for Rate-Adaptive Provisioning
#### 5.4.4 Bandwidth Utilization

Figure 8 compares the bandwidth utilization across different IoT service types and provisioning strategies. ML-RASPF consistently achieves higher utilization across all three service categories, demonstrating its effectiveness in congestion management and predictive load balancing. Specifically, it attains 86%, 89%, and 84% utilization for diagnostic kiosks, video displays, and patient Wi-Fi services, respectively. In contrast, JANUS achieves 78%, 81%, and 76%, while the energy-aware offloading baseline yields lower values of 72%, 75%, and 70%. Heuristic methods such as FCFS and LRFS show even lower utilization, particularly under high service concurrency. This validates ML-RASPF's adaptive capability in fully exploiting available bandwidth resources and avoiding underutilization in dynamic healthcare IoT networks. #### 5.4.5 Load Balancing Efficiency

Figure 9 compares the load balancing efficiency of ML-RASPF against four baseline methods across three healthcare service types. ML-RASPF demonstrates superior balancing

Figure 8: Bandwidth utilization across healthcare IoT services. ML-RASPF demonstrates superior utilization efficiency under variable traffic loads. Figure 7: Total energy consumption (in Watts) for diagnostic kiosks, video displays, and patient Wi-Fi services across five provisioning schemes. ML-RASPF shows consistent energy savings across all service categories. performance, with an average load deviation reduction of 18.2% compared to JANUS and 24.6% compared to energy-aware offloading. Specifically, for latency-sensitive diagnostic kiosks, ML-RASPF achieves 91.5% load balancing efficiency, outperforming FCFS (78.3%) and LRFS (84.1%). For bandwidth-intensive video displays, ML-RASPF maintains 87.2% efficiency, while JANUS and energy-aware offloading drop to 69.8% and 65.4%, respectively. For latency-tolerant patient Wi-Fi services, ML-RASPF achieves 85.6%, reflecting its stable adaptation even under relaxed latency requirements. This improvement is attributed to ML-RASPF's RL resource control, which allows it to dynamically assess edge node capacity and queue states before adjusting delivery paths. In contrast, heuristic-based strategies such as FCFS and LRFS fail to account for real-time link saturation or cross-tier compatibility. The consistently higher efficiency across all services underscores ML-RASPF's robustness in maintaining system-wide balance, minimizing node overloads, and optimizing edge resource usage. Overall, the experimental results affirm the superior performance and adaptability of the ML-RASPF framework across diverse healthcare IoT service scenarios. ML-RASPF achieves consistent gains in latency reduction, delivery rate, energy efficiency, bandwidth utilization, and load balancing by integrating predictive traffic forecasting and RL-based rate control into a mist-edge-cloud architecture. Compared to state-of-the-art methods such as JANUS and energy-aware offloading, as well as classical heuristics like FCFS and LRFS, ML-RASPF delivers up to 25-35% improvements across key metrics. These outcomes demonstrate the framework's suitability for dynamic, resource-constrained smart healthcare environments and establish its potential as a robust, QoS-aware orchestration layer for next-generation IoT deployments. The simulation framework effectively captures the behavior of healthcare IoT systems under dynamic load; however, it abstracts certain real-world variables such as human-in-the-loop delays, hospital-specific device heterogeneity, and emergency surge patterns. Future work will focus on extending ML-RASPF for deployment on real hospital networks to validate generalizability under operational constraints. ## 6 Conclusions

This paper presented ML-RASPF, an ML-based, rate-adaptive service-provisioning framework for heterogeneous IoT services in smart healthcare systems. ML-RASPF is a modular mist-edge-cloud architecture, which orchestrates service delivery through context-aware resource allocation, delay-aware utility modeling, and predictive control.