An IoT-Based 360° Ultrasonic Obstacle Detection System for Real-Time Occupational Safety Monitoring

Occupational safety remains a significant challenge in industrial and agricultural environments due to limited visibility and obstacle-related accidents. This study presents the development of an IoT-based 360° ultrasonic obstacle detection system for real-time hazard monitoring. The proposed system integrates an ESP32 microcontroller, an HC-SR04 ultrasonic sensor mounted on a 28BYJ-48 stepper motor, and the Blynk IoT platform to perform continuous 360° environmental scanning while enabling remote monitoring. A multi-level alert mechanism consisting of green, yellow, and red LEDs, together with an audible buzzer, was implemented to notify users based on predefined obstacle distance thresholds. The prototype was evaluated under Safe, Warning, and Danger operating conditions to assess its obstacle detection performance, response time, and system reliability. Experimental results demonstrated that the system successfully performed continuous obstacle detection, correctly activated the corresponding notification mechanisms, and maintained synchronized real-time monitoring through the Blynk dashboard and radar visualization interface. The system also achieved a response time of less than 500 ms, enabling timely hazard notification. Overall, the proposed system offers a low-cost, reliable, and practical solution for improving situational awareness and enhancing occupational safety in construction, warehouse, and agricultural environments.

Internet of Things (IoT), Obstacle Detection, Ultrasonic Sensor, Occupational Safety

1. Ashton, K. (2009). That "Internet of Things" thing. RFID Journal. https://www.rfidjournal.com/articles/view?4986 [Google Scholar] [Crossref]

2. Borenstein, J., & Koren, Y. (1988). Obstacle avoidance with ultrasonic sensors. IEEE Journal of Robotics and Automation, 4(2), 213–218. [Google Scholar] [Crossref]

3. Cheng, T., & Teizer, J. (2011). Real-time resource location data collection and visualization technology for construction safety and activity monitoring applications. Automation in Construction, 34, 3–15. [Google Scholar] [Crossref]

4. Joseph, A. M., Kian, A., & Begg, R. (2023). State-of-the-art review on wearable obstacle detection systems developed for assistive technologies and footwear. Sensors, 23(5), 2802. https://doi.org/10.3390/s23052802 [Google Scholar] [Crossref]

5. Lovella, N., & Yuhendri, M. (2024). Control and monitoring system of railway level crossings for traffic safety based on IoT. Journal of Industrial Automation and Electrical Engineering. https://doi.org/10.24036/x5bq5n96 [Google Scholar] [Crossref]

6. Sivaraman, S., & Trivedi, M. M. (2013). Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Transactions on Intelligent Transportation Systems, 14(4), 1773–1795. [Google Scholar] [Crossref]

7. Syahrani, F. P., Saputra, H. K., Anori, S., Agustiarmi, W., Ayasrah, F. T., & Thanh, P. V. (2025). IoT-enabled smart fence: Remote security and monitoring using NodeMCU ESP32 and Blynk. Journal of Hypermedia & Technology-Enhanced Learning, 3(1), 1–15. https://doi.org/10.58536/j-hytel.158 [Google Scholar] [Crossref]

8. Xu, P., Kennedy, G. A., Zhao, F. Y., Zhang, W., Zhang, W. J., & Van Schyndel, R. (2023). Wearable obstacle avoidance electronic travel aids for blind and visually impaired individuals: A systematic review. IEEE Access, 11, 66587–66613. https://doi.org/10.1109/ACCESS.2023.3285396 [Google Scholar] [Crossref]

9. Zikas, S., Panagiotakopoulos, T., & Kiouvrekis, Y. (2026). Internet of Things technologies for occupational health and safety: A review. Internet of Things, 36, 101886. https://doi.org/10.1016/j.iot.2026.101886 [Google Scholar] [Crossref]

• Smart Iot Device for Weather And Health
• Merlarchive: A Web-Based Academic Hub for UDM With AI-Powered Natural Language Processing
• Enhanced Social Network Security System: Integrating Biometric Authentication for Improved User Verification and Privacy Protection
• Smart Budget Allocation in Public Policy: A Data-Driven Approach for Equitable Resource Distribution
• Decision Support System for Faculty Selection, Promotion, and Reclassification Using Predictive Analytics