IoT-Enabled Multi-Sensor Automatic Accident Detection and Alert System Using LabVIEW: Architecture, Implementation, and Performance Evaluation

Abstract

Around 1.35 million people die in road traffic crashes globally every year of which 50 to 70 percent occur due to delayed emergency response. This paper presents the design implementation and experimental validation of an Automatic Accident Detection and Alert System AADAS using the LabVIEW graphical system design platform based on Internet of Things IoT technology. Coordinated by an Arduino Uno microcontroller ATmega328P the system integrates data from five complementary sensors including the ADXL335 tri axial MEMS accelerometer MPU 6050 six degree of freedom inertial measurement unit SW 420 vibration sensor Hall effect wheel speed sensor and MQ 3 semiconductor alcohol sensor. Twelve fundamental equations describe the physics of sensing signal conditioning accident decision logic GPS positioning and communication reliability. A compound Boolean AND gate is used in LabVIEW to distinguish between normal and accident conditions. The ESP8266 Wi Fi module transmits emergency alert messages via HTTP MQTT within 2.5 to 4 seconds. Experimental validation conducted through 120 controlled crash simulations demonstrated 97.2 percent detection accuracy less than 5 percent false alarm rate and a packet delivery ratio PDR of 97.8 percent while maintaining 35 to 40 percent lower hardware cost compared to FPGA based solutions.