Description:Description of the Invention:
The proposed system is a smart bin that uses a combination of IoT technology, camera sensors and machine learning to detect, classify and sort waste automatically. It is designed to solve both the technical and behavioural problems associated with waste disposal. The bin features a camera that captures images of the waste as it is deposited. These images are processed by AI based software that identifies the type of waste- plastic, metal, glass, etc..- based on predefined categories. Once identified, the system activates mechanical actuators to place the item into the correct compartment within the bin. To encourage public participation, a reward system is integrated. Users who deposit plastic waste properly can earn points or rewards, which help to develop responsible disposal habits. This not only ensures better segregation but also increases user engagement with the system.
The system architecture of a smart bin encompasses a complex network of hardware components, software systems, and communication protocols designed to facilitate efficient waste management processes, user interactions, and data analysis. This architecture serves as the backbone of the smart bin system, enabling seamless integration of various technologies to achieve optimal performance and functionality. At the core of the system architecture are the hardware components, which include sensors for detecting fill levels and weight, microcontrollers or single-board computers for data processing, actuators for controlling mechanical functions such as lid opening, and communication modules for data transmission. These hardware elements work in tandem to collect, process, and transmit data related to waste levels, user interactions, and system operations.
The sensors play a crucial role in monitoring the fill levels of the smart bin, providing real-time data on waste accumulation and triggering alerts when the bin reaches capacity. Ultrasonic sensors are commonly used for distance measurement and object detection, emitting high-frequency sound waves to calculate the distance to objects based on the speed of sound in air. By accurately measuring fill levels, these sensors enable efficient waste collection and management. In addition to sensors, microcontrollers like the ESP32 Dev Kit serve as the brain of the smart bin system, managing communication between sensors, actuators, and other devices. These microcontrollers receive data from sensors, process information, and control various functions of the smart bin, ensuring smooth operation and coordination of system components. Programming for the microcontrollers is typically done using C/C++ languages in development environments like Arduino IDE or Platform IO. Furthermore, communication modules enable connectivity within the smart bin system, allowing data transmission to a central server or cloud platform for further analysis and storage. Communication protocols such as Wi-Fi, Bluetooth, LoRa, or GSM facilitate seamless data exchange between the smart bins and external systems, enabling remote monitoring, management,
and optimization of waste collection processes. Data processing and storage occur either locally within the smart bin or in the cloud, where initial analysis may take place to derive actionable insights from the collected data. Cloud infrastructure supports scalability, real-time data processing, and remote access, ensuring efficient management of waste disposal operations and enabling data-driven decision-making. Users interact with the smart bin system through a user interface, typically a web application(later a mobile application can be developed for real-time use), which allows them to monitor bin status, receive alerts, and engage with interactive features. The user interface provides a platform for users to access real-time information on waste levels, reward status, and system notifications, fostering user engagement and promoting responsible waste disposal practices.
Components
1. Camera
The camera helps in accurately identifying the waste level and in some cases the type of waste. Camera provide visual data, which can be processed to enhance monitoring. The captured images can also be sent to cloud platform or server through the IoT module for remote observation and better waste management decisions.
2. Python
Python is used to implement the core logic for image processing and facial recognition. It integrates libraries such as OpenCV for real-time video processing, calculates EAR and MAR values, and communicates with the ESP8266 microcontroller via serial communication. Python also enables cloud integration for remote monitoring of drowsiness incidents.
3. OpenCV
OpenCV is used for real-time facial landmark detection. It processes video frames, detects the eyes and mouth, and calculates EAR and MAR values to determine the level of drowsiness. It also overlays warnings and alert messages in the video feed for debugging and user feedback.
4. ESP32 dev kit
The ESP32 dev kit is a popular microcontroller that offers both Wi-Fi and Bluetooth connectivity, make it suitable for projects with wireless communication.
Hardware Components
1. ESP32
The NodeMCU ESP32 is a powerful microcontroller development board equipped with a dual-core Tensilica processor and integrated Wi-Fi and Bluetooth capabilities. It features multiple digital and analog I/O pins, PWM outputs, and serial communication protocols like I2C, SPI, and UART. These features make it ideal for IoT and embedded system projects where wireless connectivity and multitasking are essential. With its compact form factor and efficient power usage, the ESP32 acts as the brain of the system, managing all inputs and outputs including motor control and display updates.
2. LCD display
The LCD (Liquid Crystal Display) is used to provide real-time visual feedback to users and system operators. In an IoT-based smart bin, the LCD typically shows information such as the bin's fill level (e.g., "Bin is 80% full"), status messages (e.g., "Bin is empty" or "Bin is full"), and network or sensor status (e.g., "Wi-Fi Connected", "Sensor Error"). A common choice is a 16x2 or 20x4 character LCD, often interfaced using an I2C module for easier connectivity with a microcontroller like Arduino or ESP32.
, C , C , Claims:We claim that,

• Efficient route planning, resource allocation, and cost-effectiveness
• Automatic notifications facilitate timely waste collection, reducing overflowing bins and mitigating environmental hazards
• Real-time monitoring, remote management, and optimization of waste collection processes