An IoT-Enabled Smart Navigation Aid Integrating Ultrasonic and GPS Technologies for Visually Impaired Mobility

Visually impaired individuals face persistent challenges in achieving safe and independent mobility. Conventional aids such as white canes and guide dogs provide basic assistance but lack obstacle detection, navigation support, and real-time monitoring. The objective of this study was to design and evaluate a low-cost IoT-enabled navigation aid that combines obstacle detection, GPS-based location tracking, and cloud connectivity to enhance the safety and independence of visually impaired users. The system was developed using an Arduino Uno R3 microcontroller as the central unit, interfaced with HC-SR04 ultrasonic sensors for obstacle detection, a NEO-6M GPS module for real-time location tracking, and a NodeMCU ESP8266 Wi-Fi module for wireless transmission. A buzzer and vibration motor were incorporated to deliver multimodal feedback to the user, while the Arduino IoT Cloud enabled continuous caregiver monitoring. The software architecture was programmed in Arduino IDE, with control logic handling sensor acquisition, threshold-based obstacle detection, GPS updates, and IoT transmission. Experimental validation was conducted through indoor and outdoor trials. Obstacle detection accuracy reached 98% within 20–150 cm, with reliable detection up to 200 cm and an average latency of 180 ms. Outdoor GPS tests achieved an average accuracy of ±3 m in open environments and ±6 m in semi-obstructed areas, while IoT monitoring demonstrated a mean communication delay of 1.2 seconds with >98% event logging success. Power profiling confirmed a battery endurance of 6.5 hours on a 2200 mAh lithium-ion pack. A pilot usability study with five visually impaired volunteers reported positive feedback on the system’s intuitive multimodal alerts. In conclusion, the proposed system achieves a practical trade-off between affordability, accuracy, and IoT-enabled monitoring, distinguishing it from prior standalone or cost-intensive solutions. Future enhancements will focus on hybrid indoor positioning, solar-assisted power, and machine learning-based object classification to expand functionality and autonomy.