# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 5 Discussion: Limitations and Future Work
It allows administrators to assess the outcomes of various deployment strategies under configurable constraints--such as response time thresholds, minimum staffing requirements, and traffic congestion levels. This flexibility enables the adjustment of model outputs to align with local operational priorities and safeguards against under-provisioning in high-risk or high-demand regions. To enhance temporal consistency and mitigate the risk of abrupt or unsafe fluctuations in deployment levels, the coefficient of variation (CV) of workload intensity is incorporated into the simulation as a stability metric. Collectively, these design elements ensure that the framework functions as a decision support tool rather than a deterministic allocation engine, with operational safety and adaptability embedded as core principles of the system architecture. In addition to model-level significance testing, feature-level validation remains an open area for improvement. While this study provides a ranked list of variable importance derived from ensemble learning methods, it does not include an ablation study in which key features are systematically removed to evaluate their marginal contribution to predictive performance. Without such an analysis, it is difficult to fully ascertain the functional role of each input variable, especially in cases where multicollinearity or latent interactions may obscure true importance. Conducting ablation studies in future work would allow for a more rigorous assessment of feature utility by isolating their individual impact on model output. This is particularly relevant in high-stakes policy environments such as EMS planning, where understanding which variables drive predictions--and how sensitive the model is to their presence--can directly influence decision-making. Future research should therefore prioritize integrating structured ablation experiments as part of the model validation pipeline, alongside interpretability methods such as SHAP and statistical significance testing. In addition to the aforementioned limitations in model interpretability and validation, the cybersecurity components of the proposed framework also warrant further formal treatment. Although the system incorporates encrypted communication protocols and privacy-preserving data processing--using industry-standard technologies such as AES-based symmetric encryption and SSL/TLS channels--these components were implemented at the architectural level without formal cryptographic modeling. As such, no verifiable proof-of-security framework or threat analysis was included in the present study. To enhance the theoretical rigor and resilience of the system, future work should integrate methods from applied cryptography and formal verification to evaluate the robustness of encryption schemes, data access controls, and potential attack surfaces within the EMS context. Establishing mathematical guarantees of data integrity and confidentiality would be particularly important for large-scale deployment in security-sensitive public health infrastructures.