This document proposes a smart waste management system using IoT for developing countries. It discusses motivations like environmental pollution from untreated waste. The proposed system features include being automated, time efficient, reducing losses, and creating jobs. It will use algorithms like linear regression, decision trees, and K-means clustering. Two algorithms are described: one uses a Decreasing Time Algorithm to assign smart bins and prioritize waste collection. The other uses DTA to assign waste collection trucks. The system was tested in a closed area experiment and showed improved efficiency over a normal system. Future plans include expanding the pilot program and generating revenue through compost sales and exporting treated waste.

1. Smart Waste Management
System using IoT

2. Presented by:
RAFEE MIZAN KHAN CHOWDHURY-16341020
B.M RAKIB HASAN – 12321065
LABIB MD ISTIAQUE - 16141003
A.M.M.GOLAM YEAZDANI-13101215
Supervised by:
DR. AMITABHA CHAKRABARTY

3. INTRODUCTION

4. MOTIVATION
 Environment is highly polluted by untreated waste
 Smart waste management systems are present in developed countries
 No smart system is present in developing countries
 To increase job opportunity

5. FEATURES
1. Automated process
2. Time efficient
3. Less system loss
4. Predictable output
5. Automated work assignment
6. Creating more job opportunity

6. Related Algorithms
 Linear Regression
 Functional Regression
 Decreasing Time Algorithm (DTA)
 Logistic Regression
 Decision Tree
 SVM
 Naive Bayes
 KNN
 K-Means
 Random Forest
 Dimensionality Reduction Algorithms
 Gradient Boost & Adaboost

7. ALGORITHM :1
Regional
Smart Bin
Mirpur
Regional
Waste
collection
office
Mirpur
Data
process
using DTA
algorithm
Data
updates in
System &
creating
priority list
Assigning
the waste
collection
on the basis
of DTA
result &
priority list
Fig: DTA for Employee
Assign

8. ALGORITHM :2
Regional
office
Mirpur
Central
control
center
Data
process
using
DTA
algorith
m
Data
update in
system
and
creating
priority
list
Assignin
g the
trucks on
the basis
of DTA
results &
priority
list
Recycle
partner
Fig: Truck Assign

9. Research area
1. Gulshan-1.
2. Mohakhali.
3. Banani.

10. Data Collection
No Parameter 1992
In %
2005
In %
1 Glass/Metal/Or
ganic
6.38 8.17
2 Plastics 1.74 4.1
3 Textile 1.83 4.57
4 Paper/cardboard 5.68 4.29
5 Food waste 84.37 78.87
Table: MSW composition (in %) at Dhaka between 1992 and
2005(Concern, 2009)

11. Data Collection
Waste Composition Bangladesh (Dhaka) (% By
Weight)
Food and Vegetable 70
Paper Product 4
Plastics 5
Metals 0.13
Glass and Ceramics 0.25
Wood 0.16
Garden Waste 1.1
Other (Stone dirt etc) 5
Moisture 65
Table : Nature of Waste composition in
Bangladesh

12. Data Collection
Year Total
population
Urban
population(
%
Total)
Waste
Generation
rate(KG/ca
p/day)
Total Waste
Generation(
Tone/day)
1991 20872204 20.15 0.49 9873.50
2001 28808477 23.39 0.50 11695.00
2004 32765152 25.08 0.50 16382.00
2025(estima
ted)
78440000 40.00 0.60 47064.00
Table : Growth in Solid Waste Generation in
Urban Cities of Bangladesh

13. Data collection
Education Level of the Workers Percent (%)
Uneducated 45
Primary school pass 30
Under SSC pass 9
SSC pass 6
HSC pass 5
HSC pass to Under graduate 3
Graduated 2
Total 100
Table : Education Level of the Workers

14. Recent MSW management
Fig: Flow of waste, recyclables and money in
MSW management in Dhaka (adopted from
Matter et al. (2013))

15. System implementation

16. Closed Area Experiment
We are assuming 1 employee
Can collect 5 units of waste/hour
Including human system loss

17. Results
Smart System at 9am

18. Results
Normal System at 9am

19. Results
Smart System at 12pm

20. Results
Normal System at 12pm

21. Results
Smart System at 3pm

22. Results
Normal System at 3pm

23. Results
Smart System at 6pm

24. Results
Normal System at 6pm

25. Visual Simulation of the System

26. Future work
1. We are already in talks with the local cantonment authority where they are al-
ready planning to impose our plan in Dhaka Cantonment as a pilot project. After
this, within 3 years, we want to implement this in Gulshan, Baridhara, Banani,
Bashundhara R/A, Uttara.
2. We will sell the compost waste to the fertilizer producers and also bio gas treat-
ment plants. We also have plans to export some of the treated waste and earn
for- eign currency.
3. We also have plans to export some of the treated waste and earn foreign curren-
cy. So, this is our revenue generation policy for the social business.
4. We will also have educational facility for the people under 18 and the children
of the people working for us. We are planning to merge with Jaago Foundation,
BRAC NGO to be our education facility provider.

27. Conclusion

28. Thank You