# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 5 Discussion: Limitations and Future Work  
Despite the promising performance of the proposed framework in optimizing ambulance deployment through real-time data integration and machine learning, several limitations should be acknowledged, particularly in relation to system robustness and field-level operability. One critical limitation involves the dependence on real-time data streams enabled by IoT devices, which may not be consistently available in all deployment scenarios. In particular, geographically isolated rural areas and disaster-affected regions often suffer from unstable communication infrastructures, posing significant challenges to continuous data transmission and system responsiveness. This limitation underscores the need for architectural enhancements that can ensure operational continuity in low-connectivity environments. To address these challenges, future iterations of the system should incorporate fallback mechanisms, such as localized data caching and asynchronous synchronization protocols, to preserve functionality during network outages. Furthermore, the adoption of hybrid communication architectures--combining cellular networks with alternative channels such as satellite links or low-power wide-area networks (e.g., LoRaWAN)--may enhance system resilience against infrastructure disruptions. Designing with redundancy across both data pathways and processing components can further contribute to fault tolerance and service availability under adverse conditions. From a policy and operational perspective, embedding such resilience features is essential for practical deployment in emergency medical services (EMS), where real-world environments are often unpredictable and infrastructure may be compromised. Future research should empirically evaluate the performance of such systems under various network stress scenarios and explore the cost-benefit trade-offs of hybrid communication models. In addition, future work may explore the integration of more advanced ensemble algorithms such as HistGradientBoosting and NGBoost, which could further enhance predictive accuracy and model interpretability under different operational constraints. To address temporal variability, future work should expand the dataset to include multiple years of EMS dispatch records, which would enable the identification of seasonal and annual trends in emergency demand. Incorporating such temporal variability will be an important direction for future work to enhance the adaptability and robustness of the proposed framework. Expanding the model's generalizability beyond urban areas such as Gyeonggi Province, to include diverse geographic and socioeconomic contexts, will also be a key direction for validating its scalability. While the proposed framework has been designed and validated using data from Gyeonggi Province, Korea, its applicability to other national EMS systems or regions with differing emergency response protocols remains an open question. EMS infrastructures vary significantly across countries in terms of dispatch hierarchies, resource availability, and integration with healthcare systems. For example, centralized versus decentralized command structures, differing traffic regulations, and varying levels of IoT adoption may all influence the system's performance and operational constraints. Future research should assess the adaptability of the model by applying it to international datasets, possibly through transfer learning or domain adaptation techniques. Such efforts would help to identify which components of the model are universally transferable and which require localization to ensure their effectiveness within diverse EMS environments. The integrated use of IoT-based real-time data, machine learning prediction, and simulation-based evaluation represents a significant advancement over conventional population-based deployment strategies. By dynamically accounting for spatiotemporal variability and operational constraints, the proposed framework provides a more adaptive and equitable foundation for EMS planning.