# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 1 Introduction
Delays in emergency response are frequently reported, particularly in densely populated urban areas affected by traffic congestion and in remote rural regions where access to medical facilities is limited [9]. To address these challenges, recent attention has turned to big data and machine learning-based approaches. By integrating multidimensional datasets--including historical dispatch records, regional demographics, hospital locations, and traffic volume--predictive models can be trained to forecast emergency demand across specific spatial and temporal contexts. This enables a proactive reallocation of ambulance resources in anticipation of emerging needs [10]. This study proposes a predictive framework that integrates machine learning methods with real-world EMS data to optimize ambulance deployment in South Korea. The model incorporates a broad array of explanatory variables, including traffic congestion, proximity to medical institutions, elderly population ratios, and historical dispatch records, to identify determinants of response efficiency. To facilitate practical application, an interactive simulation interface is developed to compare conventional population-based allocation with the proposed data-driven approach. The core model employs tree ensemble regression algorithms--namely, Random Forest, XGBoost, and LightGBM--trained and validated using actual 119 dispatch data to ensure robustness and generalizability. The simulation platform supports policy planning by allowing decision-makers to dynamically adjust regional parameters and explore various hypothetical deployment scenarios. This functionality enables both national and local EMS administrators to make evidence-based and equitable decisions regarding resource allocation. In addition, the system incorporates a performance evaluation module based on the coefficient of variation in workload distribution, aligning with established administrative standards for regional equity. By addressing both operational efficiency and fairness, the proposed framework offers a valuable tool for strategic EMS planning in the face of growing demographic and infrastructural complexity. The main contributions of this study are summarized as follows. First, we develop a predictive model that leverages real-world EMS data and machine learning methods to identify critical factors influencing ambulance response performance. Second, we design an interactive simulation interface that facilitates dynamic comparison between traditional and data-driven deployment strategies. Third, we incorporate a performance assessment mechanism based on the coefficient of variation in workload distribution to ensure regional equity. Finally, the proposed system is developed with integrated cybersecurity safeguards to ensure the secure handling of sensitive operational data. The primary contribution of this study lies in the development of a comprehensive and cyber-secure framework for optimizing emergency medical services through the combined application of predictive modeling, real-time IoT data integration, and simulation-based decision support. In contrast to conventional methods that rely solely on historical data or fixed allocation rules, our approach dynamically incorporates spatial, temporal, and operational factors while ensuring the confidentiality and integrity of data communications. This integrated system provides actionable insights for EMS administrators and policymakers aiming to improve responsiveness and promote equitable service delivery across regions. The remainder of this paper is structured as follows. Section 2 reviews the theoretical background and related research on ambulance deployment and predictive modeling in EMS systems. Section 3 describes the data sources, preprocessing methods, and variable construction. Section 4 presents the predictive modeling process, simulation framework, and performance evaluation results. Section 5 discusses methodological considerations, key limitations, and directions for future research.