# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 2 Background
### Key Factors Affecting Ambulance Deployment  
Among the key factors influencing ambulance deployment, population density and regional characteristics are among the most critical. Out-of-hospital cardiac arrest (OHCA) survival rates have been found to correlate strongly with population density, independent of EMS response time [16]. Accordingly, areas with higher population densities may require proportionally greater ambulance coverage. Urban and rural areas differ substantially in their emergency response needs and should therefore be considered separately in deployment strategies. In rural or remote areas, special measures such as increasing the number of emergency personnel in underserved regions may be necessary to ensure adequate coverage. Traffic conditions and road infrastructure directly affect ambulance travel time to the scene. In areas with high congestion or poor road quality, response delays are more likely, highlighting the need for deployment strategies that consider real-time traffic patterns and roadway accessibility. In addition, the process of selecting a destination hospital must consider not only geographic proximity, but also facility capacity, clinical specialization, expected patient outcomes, and characteristics of the incident location [17]. These factors underscore the importance of dynamic modeling that integrates transportation and infrastructure data into ambulance allocation strategies. The geographic distribution and operational capacity of nearby hospitals also play a significant role in deployment planning. The size and occupancy rates of medical facilities can influence the frequency and necessity of ambulance diversion [18], which occurs when hospitals are unable to accept incoming patients. Therefore, strategic deployment must holistically account for hospital distribution, specialization, and admission capacity to ensure that patients are rapidly transported to the most appropriate care facilities. In particular, the location of major hospitals and specialized treatment centers can exert a substantial influence on the effectiveness of regional deployment policies. The analysis of emergency dispatch records and patient characteristics plays a critical role in developing effective ambulance deployment strategies. For example, identifying the factors associated with non-transport ambulance calls can offer valuable insights for refining deployment decisions [19]. Such data enable a better understanding of the spatial and temporal distribution of ambulance demand and support the design of customized allocation strategies that consider patient types and severity levels. Furthermore, studies have revealed a strong association between ambulance density and out-of-hospital cardiac arrest outcomes, suggesting that the allocation of ambulance resources has a direct impact on patient survival [20]. More recent research has proposed ambulance deployment models that integrate machine learning with optimization techniques. These models are capable of optimizing ambulance placement in real time by incorporating historical dispatch data, traffic information, and demographic indicators. For instance, [PERSON] et al. (2020) presented an approach using integer programming formulations that jointly determine the redeployment of existing ambulances and the placement of new units [21]. Their model effectively minimizes ambulance travel distances while improving on-time arrival rates within the golden hour. Technological interventions such as priority signal systems for emergency vehicles have also shown promise in enhancing deployment efficiency. Municipalities equipped with such systems have demonstrated higher rates of timely arrivals compared to those without them [22], underscoring the importance of considering infrastructure readiness in deployment planning.