A model is proposed to save the lives of many workers who die every year in sanitizing work. This monitoring system using IoT is going to cover up all the loopholes faced in sewage monitoring department. The proposed idea will save lives of many innocent people.

1. - AN INSIGHT TO SAVE THOUSANDS OF LIVES USING IOT
By Rishabh Chakraborty

2. INTRODUCTION
• Large number of sanitation workers die every year due to lack of
facilities available.
• The harmful toxic gases leads to death of many workers.
• Unpredictable depth of potholes also leads to accidents and many
workers die every year.
• Absence of information leads to death rise of innocent workers.

3. • More than 600 people have died while
cleaning septic tanks and sewers across
the country in the past 25 years, according
to data collated by the National
Commission for Safai Karamcharis
(NCSK).
• Annual average has already been
outstripped in 2019—India’s sewage
system has killed 90 people so far. By
contrast, 54 security personnel died
combating militants.
• According to the data, there have been
634 deaths related to manual scavenging
since 1993, with Tamil Nadu reporting the
highest number of such deaths at 194,
followed by Gujarat 122, Karnataka 68
and Uttar Pradesh 51.
FACTS AND FIGURES
0
20
40
60
80
100
Militants Sewage worker
People died in 2019 in India
0
100
200
300
400
500
600
700
India United States of America
People died in Sewage System since 1993

4. As we have seen the challenges faced in sanitation of sewages and lots of innocent
people die every year, so we proposed a safety device.
The proposed model will be work as Duplex Mode of communication
The main idea behind doing this project is to:
1. Build a smart monitoring system which can detect harmful gases like CO,
smoke.
2. Send information to the Control room in fraction of seconds before
abnormalities.
3. Safety measures and information should be sent back to the worker as well as
the person monitoring him.
4. Efficient and economical device so that easily deployed.
ABSTRACT

5. SENSORS USED
MAX30102
For
Pulse rate
oximetry
LM34
For
Temperature
inside Sewage
MQ4
For
Harmful gases like
Carbon monoxide,
methane, smoke inside
sewage

6. OTHER ADDITIONAL SENSORSTHAT
CAN BE USED
Water level depth sensor
to check the depth of
contaminated water inside
the manhole
Ultra sonic sensor for
detection of lid of manhole
opening and measuring
depth

7. IoT Module
NodeMCU ESP8266
Cloud used to store data
ThingSpeak by Mathworks

8. CONDITIONSTO IMPROVE
S. No Parameters Range for normal
1 Pulse Rate > 55 bpm and < 115 bpm
2 Blood Oxygen 93-95%
3 Temperature 8 degC to 15 degC
4 Carbon Monoxide concentration <200 ppm

9. DEPLOYMENT IDEA
Fig. Inside the manhole
Fig. Monitoring side

10. continued…..
Fig. Proposed band for worker

11. • To aware the worker using sound (buzzer) or colour of LEDs.
• The band will continuously monitoring the health of the worker and the
SOS button will be used for emergency.
• IoT is enabled for fast transfer of data and saving it to the cloud for
enhancing the monitoring system.
• The Threshold value is coded in MCU in such a way that it will alert before
abnormalities.
• LED colour inside the manhole will judge whether human or machine is
able to sanitise it. If it is Red, condition is critical and only Machine should
be used. If it is Blue, worker will be allowed with proper monitoring and if
green everything is normal.
• The hardware size is kept small with only three sensors and MCU. Only
essential sensors used.
Unique Selling Point

12. • Abnormalities information received before emergency situation occurs.
• A band is proposed which will continuously monitoring worker’s health.
• The setup inside the manhole is properly going to be shielded with acrylic
materials.
• The worker won’t be allowed to go inside if any one of the parameter is
abnormal. In such cases machines will be used for sanitisation.
• The setup is economical and very small with only two three sensors and a
Micro Controller.
• The project can be enhanced by future aspirants by getting more data
from the setup after one or two months of usage and apply Machine
Learning to it. This is going to increase the predictability and more lives
can be saved.
• The same setup and idea can be used in Mining sectors as well.
CONCLUSION