# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 1 Introduction  
Timely response in emergency medical care is a critical determinant of patient survival, particularly in acute cases such as cardiac arrest, severe trauma, or stroke. When an emergency occurs, the ability of an ambulance to arrive swiftly at the scene and transport the patient to an appropriate healthcare facility significantly influences the likelihood of recovery and long-term health outcomes. Accordingly, emergency response time remains a core performance indicator within emergency medical services (EMS) systems worldwide [1,2]. South Korea's national EMS system, which operates under the 119 rescue and emergencydispatch framework, plays a central role in the country's public health infrastructure by delivering critical prehospital care and linking incident locations to tertiary medical institutions. As the nation undergoes a rapid demographic transition into an aging society, the demand for timely and high-quality emergency interventions has surged. This shift is accompanied by an increase in age-related emergencies, particularly in regions where access to specialized care is limited, thus intensifying the burden on the EMS system to maintain operational responsiveness nationwide [3]. Despite these growing complexities, the current ambulance deployment strategy in South Korea still largely relies on static criteria--most notably, population size. As shown in Figure 1, the spatial distribution of ambulance allocation (right) closely mirrors the population distribution (left), suggesting that deployment decisions are primarily driven by demographic counts rather than actual emergency demand patterns. This one-size-fits-all approach overlooks critical factors, such as temporal variability in emergency incidents, traffic congestion, accessibility to hospitals, and regional vulnerability, potentially resulting in resource underutilization in low-demand areas and chronic shortages in high-need zones [4]. Ambulance deployment plays a vital role not only in enabling rapid medical intervention but also in ensuring equitable access to care. Insufficient ambulance coverage or poorly optimized placement strategies can lead to delayed response times, decreased survival rates, and increased pressure on emergency departments. Therefore, optimizing ambulance allocation is essential for enhancing the overall effectiveness and equity of the EMS system, particularly under conditions of increasing demographic and spatial complexity [5]. To address these challenges, there is an urgent need to transition from traditional population-based deployment policies toward data-driven strategies that incorporate real-time variables such as traffic flow, hospital capacity, response history, and sociodemographic indicators. A scientifically grounded, adaptive allocation model will be crucial in building a resilient EMS system capable of meeting both current and future public health demands. Figure 1: Population by fire jurisdiction (**left**) and ambulance allocation by fire station (**right**). Emergency medical services (EMS) serve as critical systems that directly contribute to saving lives, with ambulance deployment functioning as a key operational asset that determines both the speed of on-site response and the quality of initial medical intervention. Effective ambulance allocation is not merely a matter of increasing the number of emergency vehicles; rather, it must be grounded in a quantitative strategy that considers demographic characteristics, spatial accessibility, and temporal variations in demand [6,7]. However, most current deployment policies remain predominantly based on static variables, such as population density, administrative area size, and historical dispatch frequency. As a result, they fail to adequately account for dynamic factors such as real-time traffic conditions, hospital capacity, patient severity, and the incidence of episodic events [8]. As a result, persistent ambulance shortages are observed in certain regions.