Real-Time Parking Space Detection and Management Using ESP32 and IR Technology
Keywords:
ESP32, Infrared sensors, IoT, Mobile application, Real-time detection, Smart parking managementAbstract
This paper proposes a comprehensive, real-time parking space detection and management system based on internet of things (IoT) technology. The system harnesses the advantages of low-cost infrared (IR) sensors paired with the versatile ESP32 microcontroller platform, which is endowed with built-in Wi-Fi communication capabilities. This integration facilitates real-time detection of vehicle presence in individual parking slots and seamless communication of parking availability status to users via a mobile application. The proposed system not only detects slot occupancy but also empowers users with reservation options and navigation assistance to their reserved spots, significantly enhancing the overall parking experience.
The use of IR sensors is especially advantageous due to their cost-effectiveness, ease of installation, and relatively stable performance under varying environmental conditions. Unlike ultrasonic or inductive loop sensors, IR sensors do not require intrusive pavement installation, reducing deployment complexity and cost. The ESP32 microcontroller’s flexibility allows for easy scalability and integration into existing Wi-Fi infrastructure, making the system suitable for diverse parking environments, from small-scale lots to expansive multi-level garages.
The mobile application interface acts as the user’s gateway to the system, providing real-time updates on parking availability, reservation functionality, and alerts on slot occupancy changes. This not only minimizes the time and fuel wasted in searching for parking but also contributes significantly to reducing urban air pollution and traffic congestion.
Extensive testing in a controlled environment demonstrates that the system achieves a high detection accuracy rate of approximately 98%, with communication latency below one second, confirming its suitability for real-time applications. User feedback indicates a high level of satisfaction regarding usability and responsiveness, suggesting strong potential for practical deployment.
The system’s modular and scalable architecture lays the foundation for future enhancements, including integration with secure digital payment systems, advanced analytics through machine learning to predict parking demand, and enhanced security through AI-driven surveillance technologies.
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