jacob5412 / smart-waste-management

Smart Waste Management

Smart Waste Management

A smart waste management system that monitors and predicts waste disposal patterns of an area.

Research Paper

1. John, J., Varkey, M. S., Podder, R. S., Sensarma, N., Selvi, M., Santhosh Kumar, S. V. N., & Kannan, A. (2021). Smart Prediction and Monitoring of Waste Disposal System Using IoT and Cloud for IoT Based Smart Cities. Wireless Personal Communications, 1-33. Link

Monitoring using the Arduino

Components required

1. Arduino Geniuno Board
2. LED x 6 - for displaying the fill level and ON/OFF indication
3. 1K Ω Resistors x 6 - for the LEDs
4. ESP8266 - WiFI module for sending data to Firebase
5. ADXL-335 - Accelerometer for topple detection
6. Ultrasonic Sensors x 2 - one for fill level, one for opening and closing bin
7. DHT22 - Temperature and Humidity Monitoring for safety

Codes for the Arduino

1. Arduino Sensor Code
2. Firebase Code

Webpage for displaying statistics

.
├── docs
   ├── index.html           // main page
   ├── input.html           // page for manually inputting data (if required)
   ├── render_bar.js        // script to display bin statistics
   ├── render_charts.js     // script to display time series graphs
   └── script.js            // main script to load

1. Ensure that you go to console.firebase.com
2. Create an App
3. Produce your API key/secret token
4. Edit the firebaseConfig object given in script.js.

Prediction model using Python

The code for the LSTM prediction model can be found in this notebook

Requirements

Python3.6+
cycler==0.10.0
h5py==2.10.0
joblib==0.14.0
Keras==2.3.1
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
kiwisolver==1.1.0
matplotlib==3.1.1
numpy==1.17.3
pandas==0.25.3
pyparsing==2.4.4
pypi==2.1
python-dateutil==2.8.1
pytz==2019.3
PyYAML==5.1.2
scikit-learn==0.21.3
scipy==1.3.1
six==1.13.0
sklearn==0.0

Dataset

Epoch Loss for models