# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 2 Background  
### Importance of Ambulance Deployment  
Ambulance deployment plays a critical role in emergency medical services (EMS) by ensuring timely responses and safeguarding patient survival in emergency situations. An effective deployment strategy significantly influences patient outcomes, as rapid response times are directly associated with improved treatment results. Particularly in time-sensitive emergencies such as cardiac arrest, severe trauma, or stroke, providing care within the first 10 min can substantially increase the likelihood of survival [11]. The efficiency of ambulance deployment is influenced by multiple factors, including population density, traffic flow, hospital location, and capacity, and the frequency of emergency incidents. In urban areas, heavy traffic congestion can delay ambulance travel time, while in rural regions, the broad coverage area per ambulance often results in extended response times. These regional characteristics must be carefully considered in the development of deployment strategies. However, most existing ambulance deployment systems rely on static allocation methods, which fail to account for dynamic factors such as real-time traffic conditions, hospital bed availability, and anticipated fluctuations in emergency demand. Static models are often inadequate for responding effectively to variations in urban traffic flow or sudden changes in emergency department capacity. To address these limitations, recent studies have highlighted the potential of machine learning and data-driven approaches in optimizing ambulance deployment. By analyzing historical dispatch data in combination with real-time traffic and hospital resource information, these methods can offer more sophisticated and adaptive deployment strategies. For example, dynamic ambulance redeployment models utilizing genetic algorithms have been proposed, demonstrating effectiveness in adjusting vehicle locations in response to real-time demand changes, thereby reducing average response times [12].  
The critical role of ambulance deployment has been underscored in numerous recent studies. It extends beyond the simple transportation of patients to medical facilities, serving instead as a foundational mechanism for the effective distribution of emergency medical resources and the optimal utilization of personnel during time-critical situations. Improper deployment may lead to delays in delivering necessary treatment, potentially resulting in life-threatening consequences for patients [13]. The function of an ambulance in emergency care includes not only rapid diagnosis and initial treatment but also the stabilization of the patient and safe transport to the hospital. Thus, proper deployment contributes to both short-term emergency response performance and the long-term operational efficiency of the emergency medical system. An optimized deployment strategy can alleviate the burden on emergency departments and prehospital care providers, thereby improving the overall efficiency of the healthcare system [14]. For instance, well-distributed ambulance resources can reduce overcrowding in emergency rooms, leading to a more favorable treatment environment for incoming patients. Furthermore, ambulance deployment affects not only survival rates but also overall healthcare costs. Timely intervention can prevent the deterioration of a patient's condition, reducing the need for expensive, prolonged treatment [15]. For example, early cardiopulmonary resuscitation (CPR) and defibrillator use for cardiac arrest patients have been shown to improve survival outcomes while shortening hospital stays and lowering associated costs.