Enhancement of a Real-Time IoT-Based Emergency Vehicle Alert System for Improved Road Safety
Abstract
Emergency vehicles require effective communication with nearby road users to support timely awareness of their approach, particularly in congested urban environments. This study develops and evaluates an IoT-based emergency vehicle alert system that provides near-real-time location monitoring and proximity-based warnings. The proposed system integrates a NEO-6M GPS module, ESP32 microcontroller, Wi-Fi communication, Firebase Realtime Database, and a web-based monitoring interface. GPS coordinates acquired by the vehicle-side prototype are transmitted through the ESP32 to Firebase and subsequently retrieved by the web interface. The road user’s location is obtained through browser-based GPS, while the Haversine formula is used to calculate the separation distance between the two locations. A predefined 1 km geofence is used as a design and experimental threshold to distinguish between normal monitoring and alert conditions. The system was evaluated through hardware and cloud database integration testing, followed by alert response testing using two vehicles representing the emergency vehicle and road user. The results demonstrated successful GPS data transmission and storage in Firebase, retrieval of the latest vehicle location, and simultaneous visualization of the emergency vehicle and road user locations through the web interface. The system maintained the normal monitoring condition at a separation distance of 1.770 km and activated the alert condition at 0.177 km. Ten experimental trials recorded alert response times ranging from 4.3 to 5.6 seconds, with an average of 4.99 seconds. A positive relationship was observed between separation distance and alert response time, with Pearson’s correlation coefficient of r = 0.751 and R2 = 0.565. The findings demonstrate the technical feasibility of the proposed system for near-real-time emergency vehicle monitoring and geofence-based proximity alerting under the tested conditions. However, the 1 km threshold was not evaluated through alternative distance settings and should not be interpreted as an optimal warning distance for all road conditions. The proposed approach extends the previous cloud-based system by providing simultaneous visualization of both locations and an explicit distance-based alert mechanism.