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IoT based biomedical waste classification, quantification and management
Conference Paper · July 2017
DOI: 10.1109/ICCMC.2017.8282737
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Proceedings of the IEEE 2017 International Conference on Computing Methodologies and Communication
(ICCMC)
IoT Based Biomedical Waste Classification,
Quantification and Management
Dr. Pooja Raundale∗ , Sachin Gadagi† , Chinmay Acharya‡ ,
∗ Prof.
and Head of Dept, MCA
Email: poojaraundale@gmail.com
† Student, Third Year MCA
Email: sgadagi@gmail.com
‡ Student, Third Year MCA
Email: chinmay1993@gmail.com
∗ † ‡ Sardar Patel Institute of Technology,
Andheri (West), Mumbai, Maharashtra 400058
Abstract—Biomedical waste management and treatment is one
of the critical process for organizations (HCF and CBMWTF)
because if not handled properly would lead to hazardous effects like mass infection. Bio-Medical Waste (Management and
Handling) Rules, 1998 was published vide notification number
S.O. 630 (E) dated the 20th July, 1998, by the Government of
India.[4]. 60 per cent of secondary care and 54 per cent of tertiary
care health facilities were in the RED category i.e. absence of a
credible BMW management system in place or ones requiring
major improvement. [5] Amount of Biomedical waste generated
every year is more than 8% as compared to previous per year
. [6] Efforts are being taken to automate waste management by
introducing wireless systems. Segregation plans are proposed in
order to maximize the recycling of waste and proper handling
of non-recyclable waste. That classification would not sustain in
wide variety of wastes such as biomedical waste . Hence this
type of waste is treated differently. Therefore, such a waste
has its own management unit. This study goes over current
followed practices that are undertaken by countries and also
studies various available technologies to automate such processes
and carefully handling biohazardous waste automatically.
Fig. 1.
Current System
III. T ECHNICAL BACKGROUND
I. I NTRODUCTION
Problems faced by government authorities is to keep surveillance on HCF and organizations that produce biomedical waste
for 1. Understanding the day to day quantity of waste being
generated 2. Understanding the ratio & proportion of type of
waste being generated 3. Understanding the status of disposal
of waste (collection to final processing) 4. Collecting and
analysing the periodic reports
While currently the reports are being filled, submitted
and analysed manually, it is difficult to understand which
organization produces certain waste above the cap/tolerance
limit.
II. L ITERATURE SURVEY
Efforts are being taken to waste management but deal with
automating generic waste [1][2][3]. Our proposal has focused
apporoach in biomedical waste management.
Bio-Medical Waste (BMW) refers to any waste, which is
generated during the diagnosis, treatment or immunization of
human beings or animals or in research activities pertaining
thereto or in the production or testing of biological and
including categories mentioned in Schedule I of the BioMedical Waste (Management and Handling) Rules, 1998. [7]
While currently the reports are being filled, submitted
and analysed manually, it is difficult to understand which
organization produces certain waste above the cap/tolerance
limit.
A. Categories of BMW
1) Current System: Current System
Biomedical waste generated at sources which include hospitals, factories and chemical laboratories is segregated manually
in various types color coded as yellow, red, blue/white, black
by employees of the organization [8]. Those simple color
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Proceedings of the IEEE 2017 International Conference on Computing Methodologies and Communication
(ICCMC)
coded waste is further segregated into waste categories 1, 2, 3
and so on. At a high level, the sources that generate the waste
transport it to central facilities called Common Biomedical
Medical Waste Treatment Facilities (CBMWTF) throgh vans.
These CBMWTF which receive waste in mammoth quantity
are responsible for quantifying, record-keeping and proper
treatment of waste. A governing body is authorized to keep
a close contact with such facilities and monitor their daily
functioning. The Governing body also demands reports every
year from these CBMWTF as well as sources every year called
annexure to analyse
1. How much, what kind of waste was generated
2. Are the CBWTF abiding the rules by which they
can : Ask sources to change the way they handle the
operations to reduce the amount of wastage they make
Ask
sources
to
change
the
way
they
tune
the
CBMWTF
to
handle
the
waste
properly.
Carry out actions under legally for violation of provisions
Understand how many times the CBMWTF/HCF
has been violating the rules in order to make
decisions
of
validating/cancelling
their
licences.
Fig. 2.
Monthly breakup of expenses
Limitations of existing system: Even though the existing
system in place has been working well for years, there
are flaws that are potentially hazardous to environment
and life of human beings. Some of the flaws include:
1) Unnecessary cost incurred in transportation contributing to 43% (fig.2)
2) Weak decision making capabilities & algorithms to
route the transportation
3) Human error: As currently the process of quantification
and segregation is completely human based, a chance of
human error is more.
4) Fraud: Sources like hospitals could hide the actual facts
and lie about them to fool the authorities to extend or
get the license.
5) Delay: as the mechanisms are human oriented it takes
time for segregating correct data and measuring it
6) Bureaucracy: As with any other government managed
system, the hierarchy of authority and Bureaucracy is a
cause of delay
7) Corruption: corrupt employees could take bribery and
destroy the entire purpose of the system.
8) Not real time: The data is not real-time as its collected
annually, which is does not provide the authorities with
the instant action plan.
2) Proposed System: The proposed system by making
utilization of recent advancements in technology and wireless
connectivity could help to fully automate this system. For
purpose of automating this system IoT devices can easily
be used as they are very cheap to invest in and setting
them up is easy. Generally the IoT COTS components are
used. The existing internet infrastructure is used for real-time
data transfer. At a high level the proposed system would be
Fig. 3.
centre
Distribution of beds in 75, 50 and 25 km radii with CBMWTDF in
implemented at sources, the color coded bags will be marked
with RFID tags which are automatically issues and indexed by
a system. The bins in which these bags are put into would have
weighing sensors which would immediately trigger the Van
that carries the waste to CBMWTF. This quantified weighted
value for a particular color coded bag will be sent to the
government authorities directly by IoT based microcomputer
connected to internet. This would instantly record the correct
and non-fraudulent data on governments servers. It can also
help government to analyze and fetch the data every month
or even instantly every second. Big data analyzing techniques
can further be used to classify. As this system is completely
distributed and based on real-time data, it can overcome every
possible flaw of the previous decentralized system, the server
arrangement in case of iot based architectured devices allows
individual devices to connect to their local servers and also
peer to peer with each other, Such a smart and instantaneously
reactant network could help prevent frauds in the data as the
data at any point is available on many nodes before it is
synchronized into main server as a backup.
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Proceedings of the IEEE 2017 International Conference on Computing Methodologies and Communication
(ICCMC)
Notification Server
Local Database
The CBWTF
• Smart Bins (classified as processing bins) with capability
to weigh the contents and send information to the local
server
• Microcomputer/Raspberry Pi
• Application Server
• Notification Server
• Local Database
The Central governing authority
• Application Server
• Notification Server
• Local Database (Altogether termed as central system)
At each of the places, the commnication will operate and
follow standard tcp based communication protocols (eg. xmpp)
The smartbins, processing bins will sense the change in
weights and send the data to local system
Certain part of local system will be available to CBWTF
to understand the amount of biomedical waste generated and
collection status . which will be achieved by syncing the
collection status with CBWTF and in turn with the smart
trucks
•
•
Fig. 4.
Proposed System
Functionality
Information about waste
storage in bins
Information about collection status of bins
Segregation of waste
Having transparency in
total quantity of waste
collected vs actual waste
submitted
Components
Smart bins (based on
weight & full sensors)
Sensor networks
Self identifying objects
Use of master & slave
bins
Waste
generation
&
storage
at
hospital
Generation of bmw waste and collection into slave bins.
1.The waste will be stored in disposable color coded bags
which act as self identifying objects. These bags will be
attached with qr code . The slave bins will be locked and
won’t open until the disposable bags matches it’s own color
code, and will be connected to sensor network which will
read the self identifying bags and open respective slave bins.
This will ensure the appropriate segregation and quantification
of waste. Each smart bin will have filler sensors & weighing
capabilities . 2. Collection from slave bins to master bins
Before the time of point of collection, the bmw will be moved
from slave to master bin which will calculate and identifying
whether all the generated Waste is collected. 3.Notifying
collection van about status of capacity of collection. The vans
will have cheap smartphone device that will be notified about
capacity of various healthcare facilities 4. Collection from
master bin to van 5. Collection from van to treatment centers
The hospitals CBWTF will have the following technical
architecture:
• Self identifying disposable baggages
• Smart Bins (classified as slave and master) with capability
to weigh the contents, open close according to certain
parameters, send data to local server
• Microcomputer/Raspberry Pi
• Application Server
B. Data flow
All the slave bins are connected with a master bin
through900-1900 MHz (2G spectrum) network. As this network has the most wide area coverage these bins will easily
be able to connect to internet from most places. This way
the challenge of connecting at remote places is achieved as
the 2G spectrum is available at most places. The current
available sensors use the least amount of power to connect
to this spectrum. Although, 2G spectrum comes with a tradeoff of having less bandwidth available. This is where the IoT
based specialized protocols can help us transfer data in an
extremely fragile and slow internet connectivity as they are
meant to work in very less bandwidth. Challenges of using
existing application level protocols: HTTP: This protocol uses
40 bytes of header, which is a complete overhead to actual
payload and completely beats our purpose. TCP: this protocol
makes it sure that the message is delivered, in fragile network
like 2G. Following is the section wise list of IoT protocols
that can be used: Discovery: The master bin in rural area
would require a list of bins registered under it so that it
can keep track record of the status of those bins also the
quantity of particular material in it. For which purpose The
following two discovery protocols can be made use of: mDNS
(multicast Domain Name System): slave bins will be able to
figure out the ip of the master bin to report to HyperCat: An
open, lightweight JSON-based hypermedia catalogue format
for exposing collections of URIs. Can be used to by slave
bins. To figure out where to report the data about
C. Data Protocols
MQTT : To transfer the real time data from slave bins
to master bins we use MQTT (Message Queuing Telemetry
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Proceedings of the IEEE 2017 International Conference on Computing Methodologies and Communication
(ICCMC)
Transport) , it is a publisher subscriber based serialized data
publishing mechanism, therefore we can pump events into
queue to which all the subscribers would react, hence when a
slave bin is full, slave bin can queue an event of full to the
queue to which the van would react by coming to the bin.
LLAP (lightweight local automation protocol)
[13] http://www.indiaenvironmentportal.org.in/content/426952/biomedicalwastemanagementrules2016/
[14] Waste management principles http:www.apiindia.orgpdfmedicine update 2007162.pdf
[15] Categorizarion of waste http://www.biologydiscussion.com/wastemanagement/biomedicalwastemanagement/biomedicalwastemanagementprocessrulesandcostecology/71000
D. Cost/ Expense
1)
2)
3)
4)
5)
IoT Microcomputer $5
Weight sensor $1
Tilt Sensors $1
Central Application/Database Servers $15
Local Application/Database Server $5 RFID Tags &
Reader kit $2
IV. C ONCLUSION
The solution proposed by the system aims to successfully
solve various problems as described. It aims to reduce human
interaction and fully automate the waste management system
for hospitals, laboratories and pathological labs. The system
designed is capable of completing entire plaster management
plan without a single user input or manipulation. Therefore, it
can be thought of as a Blackbox system which can’t be altered
to create wrong data. The data that is generated though the IoT
systems are generated based on real-time sensor information,
which is constantly fed to authorities at their server which in
turn allows full automation of the data monitoring system. The
system also reduces the data transportation cost by optimizing
the van timings as an when required. This project entirely
touches and solves the management biomedical waste by
introducing automation in the field.
R EFERENCES
[1] S Thakker,R Narayanamoorthi, Smart and Wireless Waste Management,
ICIIECS15
[2] Fachmin F olianto,Y ong Sheng Low, Wai Leong Yeow, Smartbin: Smart
Waste Management System, 2015 IEEE Tenth International Conference
on Intelligent Sensors, Sensor Networks and Information Processing
(ISSNIP) April 2014
[3] Md. Abdulla Al Mamun, Mahammad A. Hannan, Integrated Sensing Systems and Algorithms for Solid Waste Bin State Management Automation,
IEEE SENSORS JOURNAL, VOL. 15, NO. 1, JANUARY 2015
[4] http://www.indiaenvironmentportal.org.in/files/file/BMW%20Rules,%202016.pdf
[5] Bio-medical waste management: situational analysis & predictors of
performances in 25 districts across 20 Indian States , Page 1
http://icmr.nic.in/ijmr/2014/january/0115.pdf
[6] Press
release,
Central
Pollution
Control
Board
(CPCB)
http://pib.nic.in/newsite/PrintRelease.aspx?relid=123986
[7] http://www.indiaenvironmentportal.org.in/content/426952/biomedicalwastemanagementrules2016/
[8] Bio-medical waste management: situational analysis & predictors of
performances in 25 districts across 20 Indian States , Page 9 http://icmr.nic.in/ijmr/2014/january/0115.pdf
http://mpcb.gov.in/images/pdf/Status BMW MahJune2011.pdf
[9] http://cdn.biologydiscussion.com/wp-content/uploads/2016/12/clip image004126.jpg
[10] S
Pulavarthi,
Srinivasulu
Pothireddy,
Review
Article
BioMedical
waste
management,
International
Journal
of A J Institute of Medical Sciences 12012 67-74
http://journal.ajmedicals.in/ResearchDocuments/Biomedical635244451415672757.pdf
[11] https://www.researchgate.net/publication/228361977 Biomedical Waste Classification and Prevailing Management Strategies
[12] Aravindan ACHUTHAN, Vasumathi AYYALLU MADANGOPAL A
Bio Medical Waste Identification and Classification Algorithm Using
Mltrp and Rvm https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5149491/
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