## 3 Proposed Model  
### Internal Components  
The components in the IoT smart garbage disposal system are a Bolt Wi-Fi module, a load cell, a servo motor, the HX-711 interface, an Arduino, an ultrasonic sensor, a bread  
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board, and junction cables. The ultrasonic sensor is placed on the inner lid toward the direction of solid waste. The live data are sent to the microcontroller for further processing and are sent to a web page via the Bolt Wi-Fi module. The module also automatically sends e-mails and alert texts to the client, stating that the trash is overflowing. The distance is measured using the ultrasonic sensor, which is integrated with the lid that indicates the trash can's size. Thema the Arduino Uno is the system's central component, containing Arduino's latest microcontroller, for easier processing of data sent by sensors and further transmission to a Bluetooth or Wi-Fi module for further processing or display. The fuzzy inference system with GA for optimization is integrated with the microcontroller. The load cell is a critical sensitivity module. It is a carefully designed metal structure that is loaded with precise locations in the system known as injury gauges, which are made up of smaller components. In this paper, Wheatstone bridge load cells are deployed, and these load cells have four balanced resistors with predetermined excitation voltages. If any resistor detects a change in value, it generates a voltage using Ohm's Law. The HX711 load cell amplifier interface, which is an electronic scale module, converts the measured changes in resistance, and it is a 24-bit analogue to digital converter. The Arduino is connected to the 5 kg load cell interface using the HX711 module. The change in resistance is used to measure the weight of a garbage object after calibration. The servo motor is controlled by the Arduino and requires more power for mechanical output. The small service motors are directly connected to the Arduino to control the shaft position accurately. The position of the shaft is precisely controlled using motors to obtain accurate results, and the swing action of the bin is adjusted according to the weight of garbage. A sensor on the trash determines the depth of the garbage in the total bin. When the level of waste is at its maximum, the managers in the office are alerted so that the staff can take the necessary steps to empty the garbage bin. The inclusion of a fuzzy inference with GA implementation on the Arduino Board is used to detect trash levels, weight, and so on. The GA rules are incorporated into the system design by downloading those rules onto the Arduino Board. This helps in the hardware setup for improvised decision making in the identification and segregation of waste. In addition, the Arduino Board is trained using fuzzy rules that can detect the level of waste and types such as plastic bottles, glass, etc. This technique can also help to identify the areas that need more awareness regarding sanitation and, in turn, locate which regions are the worst offenders in terms of poor sanitation and garbage maintenance. This effort can help clean up the environment in an efficient manner. Cloud middleware collects data from sensors, aggregates them, cleans them (i.e., discards or infers missing values), and sends them to an engine that is implemented in Open IoT. A fuzzy inference engine with GA is used for forecasting waste levels and to learn the selection of the daily waste bins based on historical data. The two primary components of the system are as follows:  
* Waste monitoring system: The waste monitoring system consists of an ultrasonic sensor. The sensor measures the proportion of the bin that is filled with waste. A Wi-Fi module is used to send information that can track the level of waste in the smart garbage bin so that overflow is prevented and smooth service is obtained. * Waste segregation system: The segregation system uses the load cell and the servo motor.