International Journal of Science, Engineering and TechnologyAuthors: Shalini S, C. Nandini, Geetha Shree R, M. Yasaswini, Neetha Jain, Anusha P
Abstract: Emergency medical services (EMS) play a critical role in saving lives during critical situations such as accidents and cardiac arrests. However, conventional ambulance systems face significant challenges including traffic delays, poor route planning, and the lack of real-time patient monitoring. This survey paper explores the evolution and integration of Smart Ambulance Systems that combine Internet of Things (IoT) sensors for patient vitals monitoring and Machine Learning (ML) algorithms for intelligent route optimization. The paper reviews various technologies such as GPS, GSM modules, biomedical sensors, and cloud-based APIs that enable real-time data acquisition and transmission to hospitals. It also investigates recent advances in ML-based routing using algorithms like Reinforcement Learning for dynamic, energy-efficient, and time-optimized ambulance navigation. The survey highlights key contributions from current literature, compares system architectures, and discusses open challenges and future directions for deploying scalable, reliable, and AI-driven EMS solutions
DOI: http://doi.org/
