IoT-based Waste Management System

Project Title

IoT-based Waste Management System

Project Area of Specialization Internet of ThingsProject Summary

Keeping the city clean has been an ongoing task which need laborious efforts of people working on ground level emptying and sorting the garbage bins whenever they are full. Garbage generally can cause sickness and illness through different germs and bacteria among humans as well as animals such as cats and dogs roaming around streets. Also, it is a big source of environmental pollution. The proposed IoT-based garbage management system is an extension of Smart Bin project that won funding from ignite last year. We are working on sorting the garbage into 3 categories:

• Plastic
• Stationery
• Green waste

We apply machine learning approach to sort garbage into three different parts. In 1st part all plastic bottles should be collected. In 2nd part stationary and papers should be stored that can be recycled and reused. In 3rd part food grade waste should be collected. Which we recycle and used in land for growth of plants and crops which is beneficial for us.

The app will give a graphical view of the garbage bins and highlight the garbage collected in order to show the level and type of garbage collected. The LCD screen shows the status in real time.

Project Objectives

The main objective of the project is to monitor and sort the garbage automatically into three categories:

• Plastic
• Stationery
• Green waste

Other objectives include monitoring and recording of

• humidity, temperature
• garbage level
• weight of garbage

The system will check these parameters by using sensors.

Other aims of this project is to reduce the time, and reduce the pests, garbage bins stay filled to the top and become a vital factor in increasing environmental and air pollution (which increases fungal and seasonal viruses to cause nausea, diarrhea, malaria, dengue, and causes death in young children and street animals.).

Project Implementation Method

The project will use data collected from various sensors & GRS, store the data into secure database, and display using interactive graphs. We are going to analyses the images captured through image sensor/camera to sort the garbage using machine learning techniques. The sensors that are required are:

DHT 11 sensors that measure the Temperature and Humidity locally based on esp32 microcontroller. Ultra sonic sensor that measures the distance to an object using ultrasonic sound waves. An ultrasonic sensor uses a transducer to send and receive ultrasonic pulses that relay back information about an object’s proximity.  High-frequency sound waves reflect from boundaries to produce distinct echo patterns. We also use ESP 32 cam for image processing and store our data in server from which we process our coming data and send it to its partitioning area by using servo motor. GPS will be able to tell the location of bin which bin is filled.

Benefits of the Project

This project is about developing a sensor-based system to monitor and sort garbage through smart waste bin. The system will sort garbage into three major categories: Plastic Stationery Green waste The system will monitor and analyse the following measures in the smart garbage bin Current Atmospheric Temperature Humidity Moisture Weight Separation Level of bin The system will use sensor-readings of these parameters and automate relevant parameters for waste management, including humidity identifier, automatic opening of the bin with sense detection .The main aim of this project is to reduce the human effort of emptying and sorting the garbage. By machine learning approach we can easily recycle our waste in useful manner. By this system, environmental pollution would decreased and we create a healthy environment in our home or city.

This project is about developing a sensor-based system to monitor and sort garbage through smart waste bin. The system will sort garbage into three major categories:

• Plastic
• Stationery
• Green waste

The system will monitor and analyse the following measures in the smart garbage bin

• Current Atmospheric Temperature
• Humidity
• Moisture
• Weight
• Separation
• Level of bin

The system will use sensor-readings of these parameters and automate relevant parameters for waste management, including humidity identifier, automatic opening of the bin with sense detection .The main aim of this project is to reduce the human effort of emptying and sorting the garbage. By machine learning approach we can easily recycle our waste in useful manner. By this system, environmental pollution would decreased and we create a healthy environment in our home or city.

Technical Details of Final Deliverable

We are proposing to develop an “IoT-based smart waste management system" with attached sensors and a friendly dashboard displaying graphical visualizations. The following sensors and techniques will be used to achive the goal of monitoring and sorting garbage in smart bin.

The details of the proposed sensors are:

DHT 11 sensors that measure the Temperature and Humidity locally based on esp32 microcontroller.

Ultra-Sonic SENSOR measures the distance of a target object by emitting ultrasonic sound waves, and converts the reflected sound into an electrical signal. Weight sensors It converts a force such as tension, compression, pressure, or torque into an electrical signal that can be measured and standardized. As the force applied to the load cell increases, the electrical signal changes proportionally.

Humidity sensor the RHT03 (also known by DHT-22) is a low cost humidity and temperature sensor with a single wire digital interface. The sensor is calibrated and doesn't require extra components so we can get to measure relative humidity and temperature. Magnetic belt will be complimentary as it will allow to separate all the metallic components and waste will be metal free for further recycling or composting.

We use ESP 32 cam for machine learning from which we processing images of our dataset and store it in server from which we process our coming garbage and send it to its partitioning area by using servo motor.

Dashboard:

The sensors dashboard provides the user with a center point where they can view and control the connected sensors and actuators.

Some of the features that this view offer is:

• Information about the currently connected sensors/actuators.
• Level bin filled.
• Partitioning of the bin
• A sensor/actuators property section.
• Weight of the bin filled or empty.
• Breadboard is the empty hub of all the sensors.

We configured the sensors in such a way that it gives us the understandable values that will be shown in the form of numbers and levels through mobile application in labeled form.

Final Deliverable of the Project HW/SW integrated systemCore Industry ITOther Industries IT , Health Core Technology Internet of Things (IoT)Other Technologies Artificial Intelligence(AI), Clean TechSustainable Development Goals Good Health and Well-Being for People, Industry, Innovation and Infrastructure, Sustainable Cities and CommunitiesRequired Resources

Elapsed time in (days or weeks or month or quarter) since start of the project	Milestone	Deliverable
Month 1	Requirement Gathering.	We will gather all the requirements needed for this project
Month 2	Requirement Gathering about Available Sensors	Sensors were able to detect the required parameters.
Month 3	Sensor Connectivity and Configuration	The Sensors will be able to provide us the parameters required for the further commencements of Project.
Month 4	Sensor Connectivity and Configuration	The Sensors will be able to provide us the parameters required for the further commencements of Project.
Month 5	Designing an android application for our system(Front End)	The structure of android app will be formed.
Month 6	After the completion of Front End, we will start to develop its back end	We will maintain the complete working of android app.
Month 7	Requirement gathering for second phase.	After connectivity with app we will start working on second phase which includes robotics and machine learning.
Month 8	Requirement gathering for second phase.	After connectivity with app we will start working on second phase which includes robotics and machine learning.
Month 9	Requirement gathering about sensors and hardware complications we must face ahead.	We will gather all the requirements needed for completion of this phase.
Month 10	Integrate the system with android app	As a result our system will show full functionality with android app
Month 11	Integrate our new sensor with the system and the app.	Our system will show complete functionality. And hopefully respond as required.
Month 12	Implementing this system in university for testing	Testing phase and improvement.