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2022-IEEE International Interdisciplinary Humanitarian Conference for Sustainability (IIHC-2022), November 18th & 19th 2022
978-1-6654-5687-6/22/$31.00 ©2022 IEEE
349
IoT Based Risk Monitoring System
Salna Joy
Dept. of Electronics and Communication
New Horizon College of Engineering
Bangalore,India
salnamaryjoy17@gmail.com
Neethu P S
Dept. of Electronics and Communication Engineering
School of Engineering and Technology,
CHRIST (Deemed to be University) Bangalore,India
R.Babychithra
Dept. of Electronics and Communication
New Horizon College of Engineering
Bangalore,India
Anju M I
Dept. of Electronics and
Communication Engineering
New Prince Shri Bhavani College of Engineering and Technology
Chennai ,India
Abstract—The Internet of things (IoT) aims at connecting
different objects, things using internet. The IoT is expanding
rapidly and this motivates to apply for the food preservation
domain such as preserve the standard of the veggies and
fruits. In this paper we have worked on a cold storage system
to analyze the environmental conditions under which the
food item is being stored. The proposed system senses the
temperature, moisture, gas parameters of surrounding
environment as these parameters affect nutritional values of
food items. An Arduino-based system is created and put into
operation; it serves as both a central hub and a network
layer for the refrigerated holding tank. It is also linked to the
cloud, where an open-source application server supports
digital storage functions. By establishing a connection to the
database (DB) via its IP address, the measured variables are
delivered to the base station (BS) from the cloud and stored
there. Then, a cooperative sensing model that uses many
observed information as input and one merged informational
item or action to be performed as output is tried. As a result,
numerous inputs, such as temperature and humidity, were
combined and averaged to provide a tightly integrated
result. Last, the system integrated an android mobile
application which is used to facilitate user interaction and
connect through IoT based system that is station or gateway
and the internet. GPS is Used to track the remote cold
storage and transport container live locations.
Keywords—IoT, sensors, climate changes, Arduino
I. INTRODUCTION
It is critical to address specific supply chain (SC) risks,
such as ensuring good state of the environment and
guaranteeing worker safety in the temperate winters,
because managing ecologically sensitive products needs
careful supervision within specified settings all through the
SC [1]. Smart security of information, though, depends on
a unified place, making it vulnerable to manipulation. In
this research, we build a multi-sensors (WSN) monitoring
system using blockchain to gather performance
specifications and validate data given for enhancing
confidence and openness in frozen storage. Programs for
performance analysis used the K-means and SVM
algorithms to categorise or forecast the quality
deterioration of refrigerated seafood [2]. The impact of diet
on human wellbeing has emerged as one of the biggest
significant issues facing cultures over the recent ten years.
In order to meet this problem, European nations, and
particularly the European Union, are put in place
regulations and guidelines that enable countries to trace
and control meals throughout the entire manufacturing
cycle till ingestion. The EU has required food and feed
producers to establish surveillance and control systems for
its goods by implementing certain regulations. The
delivery network is among these networks' more crucial
components [3]. Refrigerated operations are challenged by
the increasing demands for fragile goods around the world,
which is driven by a fundamental shift in client
expectations. Findings indicate that a lack of transparency
throughout the distribution chain contributes to an elevated
number of losses. To increase service quality, it is essential
to concentrate on operations and electronic connectivity
within all participants. The purpose of this article is to
present a concept, to continuously track effectiveness, and
to improve judgement.
Fig 1. Supply Chain
With its extremely low cost, simple deployment,
ease with different networks, high energy efficiency, and
efficient information assessment highlight, the suggested
cold storage approach based on NB-IoT is a tangible value
improver and is perfect for a temperature-sensitive final
part to end distribution network tracking. The pallet's
integrated NB-IoT device incorporates heat and other
ambient fluctuation detectors. Heavy coverage ability at a
reasonable price will help to enhance communication and
upgrade the refrigeration for best experience [4].
Among the new blockchain uses for product testing and
anti-counterfeiting in current history has indeed been
product tracking. Current systems for tracking foodstuffs
don't really provide a significant degree of network
dependability, flexibility, or informativeness. In addition,
tracing procedures in contemporary networks of SC are
time-consuming and challenging. Distributed ledger
technology holds out the possibility of developing a novel
paradigm for SC traceability, allowing these worries.
However, as technological innovation was primarily
created for cryptocurrencies rather than SC traceability, it
2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC) | 978-1-6654-5687-6/22/$31.00 ©2022 IEEE | DOI: 10.1109/IIHC55949.2022.10060418
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350
is impossible to simply adapt blockchain technology to
food traceability.
A blockchain IoT-based food traceability system
(BIFTS) is suggested in [5] to combine the unique
installation of blockchain, IoT technology [6–7], and
probabilistic reasoning [8–9] into a complete traceable rack
control method for monitoring perishables. Compact and
vaporised qualities are included in the chain to meet the
needs for product traceability [10-11], where as a unified
decentralized network that takes into account shipping
travel times, customer evaluation, and shipping is
integrated. [12-14].
II. RELATED WORKS
Most small producers do have low finances, none of
them can pay for enough RFID tags to affix them to each
packaged food. Consequently, the study takes into account
a practical strategy to aid poor farmers and imposes the
least degree of financial pressure on individuals [15]. The
flow of the work is depicted in Fig.2. They suggest that
such simple food packages used for the interaction among
producers and the logistics system contain a sheet
pedigree which has been approved by the producer as well
as the wholesaler. Each batch is repackaged by the
wholesaler onto RFID-tagged containers, and information
first from printed pedigree is kept inside the EPCIS
database. The ePedigree Collection and ePedigrees
Inquiry programmes are employed to create and validate
the ePedigress. The EPC activity occurrence is appropriate
to keep the delivered and accepted ePedigrees because
they are related to the activity of the item.
To determine the exact environmental moisture and
temperature throughout refrigerated temperature, a sensor-
based surveillance was created in [16]. Fish quality was
determined using digital sniff information. Electronic nose
readings (ENS) being clustered using principal component
analysis (PCA), while key attributes such as material,
colour, smell, and pH are evaluated and assessed. The
quality degree of fish specimens held in a particular
refrigerated storage is clustered using the CNN-SVM
method based.
Fig 2. Work flow in [15]
A thorough analysis and evaluation of the concept and
program's capabilities was conducted. The refrigerated
cycle abiotic factors might be effectively monitored by the
IoTMS. PCA can separate various distinctive smells using
ENS. The CNN-SVM method approach to determining
ripeness class seems to have a better predictive prediction
accuracy. About 90% of the instructional dataset's
reliability is accurate, as well as the total prediction
accuracy is at 95.6%. In order to manage heat and
minimise wastage throughout fish refrigerated temperature,
grade sensors and a spoiling surveillance system are
implemented.
III. PROPOSED SYSTEM
In this study, we developed and put into use a tracking
that employs WSN ZigBee and Firebase to track a number
of characteristics that define the chilled bins. Package
characteristics like climate and humidity are updated in
real-time on a website, as well as the segmentation. The
information is sent to a web computer using a 4G network
in this setup. Further examinations involving moisture and
temperature readings were conducted. The exactness was
assessed together with the calculation of the relative error.
In order to improve the geolocation, we additionally
analyse the GPS Logger Shield-acquired geolocation. The
future focus will be on creating a scheduling mechanism
that will use less power and prolong the battery
performance of the destination node. The proposal's goal is
to manage insider data for the cargo containers and keep
track of the moisture, and air somewhere inside them. The
IoT is used in this method to accomplish the development
of a smart surveillance system that can check the heat,
moisture, and gas levels within refrigerated vehicles as
well as monitor the whereabouts of those vehicles in real-
time during the course of transit. The designed scheme
made use of a GPS module, an ESP32 central control
device, an LM35 for temperature monitoring, and a MQ-2
gas sensor.
Global production, distribution, storage, and
transportation of thousands of tons of heat items each day
makes climate and humidity management of these products
crucial. Manufacturers, distributors, logistical selection,
and purchasers are becoming increasingly concerned with
safety control and tracking of items even during cold
storage facilities.
Fig 3. Flow of cold storage mechanism in [17]
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351
Using a mix of RFID and Sensor gadgets, a series of
investigations were carried out in three industrial retail
rooms totaling 1848 m3 with various steady states and
items. The findings are presented in this study. From each
compartment, up to 90 semi-passive RFID data recorders
was implanted concurrently along with 7 specks over the
course of 7 days [17]. The flow of this work is depicted in
Fig.3.
As a whole, the achievement of the Radio
frequency identification as well as the Sensor endpoints
has been good. The information failures were manageable
and tiny enough to allow it to analyse the variables and
produce pressure - volume illustrations and charts of heat
transfer. However, this configuration is really only
practical during the trial phase. A arbitrary variant of this
technology needs to be created for every situation because
using this many Tags in just this situation would be
feasible given how they were placed. Because RFID
recorders are relatively inexpensive, it is possible to apply
them densely and get precise data on temperature
gradients within compartments. With a suitable tool, such
as specialised Matlab applications, the quantity of
information gathered from across all tags is reasonable.
On the contrary side, given the location and also the
principal constraints of such recorders, that are their
scanning distance or monitoring devices, everything was
challenging to collect the data from a significant number
of these units in the chilly chambers that were the study's
focal point. While it is possible to replace a few of the
recorders with heat estimating approaches, other recorders
placed in crucial locations shouldn't be done so due to the
continually shifting situations that seem to be impossible
to forecast, like the mobility of the boxes within the space.
By creating a complicated system made up of Edge
devices and Rf modules, additional tests may result in a
tighter use of the two platforms. The advantages of this
execution are demonstrated in this job.
It is understood that botanical food' antioxidant
activity (AOA) serves as a proxy for the human exposure
advantages associated with its intake. Plant - based foods
are primarily eaten or used as semi - processed owing to
their high wastage and periodicity, while one of the
methods employed in the production of high-quality foods
is chilling. Nevertheless, cell breaks that occur after
thawing and cold preservation might cause reactive
molecules to be released and then degraded as a result of
both enzymatic and chemical oxidation events, which
might lead to a decreased antioxidant capacity than the
equivalent new stuff [18]. The current understanding on
the individual and cumulative effects of chilling and
cryogenic processing conditions on the antioxidant
capacity of veggies and fruits in addition to the function of
freeze drying, is compiled and presented in [19]. One of
most popular techniques for assessing the oxidative action
in vitro are indeed provided, along with categories of
chemicals essential for the antioxidant function of plant
diets. To emphasise their impact on the AOA of fruits and
vegetables, the freeze concepts as well as the impacts of
crystallisation and crystallisation on fruits, veggies,
including its principal constituents is discussed. A number
of unique features (also including size and structure)
affected how chilling and refrigerated keeping affected the
AOA of plant meals, although the significance of
exogenous manufacturing variables, including such
chilling and retention temps, remains unclear. According
to the analysis methodology utilised for the AOA
assessment and information presentation, numerous
findings, with a significant degree of heterogeneity, are
described. The aforesaid disparities are caused through
other inherent source microstructures (such as varietal and
level of ripeness), post-harvest circumstances, in addition
to refrigerating techniques that really are generally
underreported by research.
Grocery outlets' electricity usage efficiency was
way greater than that of office towers and motels because
of their small footprints but high installation
concentration. As a consequence, it's essential to evaluate
or manage the condition of the quick shop to get a low
EUI and use less electricity. This study makes use of a
grocery shop to assess energy usage and run a Cfd model
to observe how cold storage showcase (CSS) equipment
impacts the environment. A study of preliminary data via
information gathering and power comparing information
has indeed been supplied and thoroughly reviewed
utilizing field tests and on-site internet information [19].
IV. OVERALL SYSTEM DESCRIPTION
The ESP32 is the main controller which collects
the sensor data like temperature and humidity from
DHT11. A unified 2.4 GHz Wi-Fi and Bluetooth
combination device called the ESP32 was created using
TSMC's amazingly 40 nm tech. A microcontroller called
Arduino UNO is built on the Atmega328P. It can be
powered by an AC to DC adapter or attached to a
computer using a USB wire. Additionally, it also doesn't
call for costly or specialist wi-fi test rig.
A temperature sensor is an LM35. The voltage level of
the LM35 line accuracy incorporated temperature
measurement is directly equal to the heat in degrees
Celsius. In comparison to conventional thermometers
measured in Kelvin, the LM35 sensor has a benefit
because it does not necessitate the user to deduct a
significant voltage level from the outputs in order to gain
easy Celsius scale. The moisture of the air is measured and
reported by a humidity sensor.
Digital hygrometers can be used to monitor humidity.
Ruthenium oxide, a thick film conductor of precious
metals like gold, is printed and shaped into the form of a
honeycomb to create an electrodes. This is then covered
with a polymer membrane that, because it contains mobile
ions, functions as a humidity sensor. The shift in the
number of mobile ions causes an impedance shift. The
MQ-2Gas sensor can detect gas and other flammable
streams as well as Liquid petroleum gas, propane, and
hydrogen with excellent sensitivity. The delicate sno2
substance used in the construction of the MQ-2 gas sensor
has a lower thermal conductivity in fresh air. The device's
conductivity increases in the presence of the desired
gaseous fuel. The shift in resistivity is converted using a
signal conditioning device so that the signal matches the
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352
source measured value. By establishing a connection to the
DB via its IP address, the sensor values are delivered to the
BS from the cloud and kept there. And, finally it produces
single consolidated output which contains the co-ordinates
of the container along with the humidity, temperature and
gas detected outputs.
The Arduino Integrated Development Environment, often
known as the Arduino IDE, is available along with a word
processor for coding, a user's experience, a text terminal, a
sidebar with icons for frequently used functions, and a
variety of menus. The IDE is used to develop computer
programmes called sketches. These sketches are made in
the text editor and saved solely with the filename uno. The
language for coding is embedded C. The android app is
created with Android Studio. The accountable staff can use
the software to remotely and whenever necessary track the
temperature, moisture, and occupancy in the deep freeze
and containers. Real time values of parameters can be
obtained from cloud database. GPS shows the exact
location of the container. Temperature, Humidity and Gas
detected or not are displayed on the screen and recorded in
the cloud. Alert messages are popped out on the android
application. The aforementioned factors are shown on the
Android screen so that judgments can be made in the long
term. In Fig. 4, the block diagram is displayed.
Fig 4. Overall system structure
V. RESULTS
The trial is performed with tomatoes as it’s a very
common rapid perishing commodity. Tomatoes are kept in
a cold environment and checked with the proposed system
sensors. Ethylene is produced by rotting tomatoes which
causes a foul smell is detected by the system. The IDE is
also provided, along with a word processor for coding, a
messaging area, a text terminal, a sidebar with icons for
often used functions, and a variety of menus. This IDE is
used to generate computer programs called sketches. The
software application was used to create these drawings,
which were then stored with the. ino filename. The
language for coding is embedded C. The android app is
created with Android Studio. The accountable staff can use
the app to remotely and whenever necessary track the
temperature, moisture, and occupancy in the deep freeze
and box. Real time values of parameters can be obtained
from cloud database. GPS shows the exact location of the
container. Temperature, Humidity and Gas detected or not,
are displayed on the screen and recorded in the cloud. Alert
messages are popped out on the android application.
The container serves as the primary importer and
exporter of products as the principal logistical vector of
global transportation. Transportation management control
systems emphasize the use of containers in their
operations. Foodstuffs and medications can be transported
safely thanks to the container's ability to keep temperature
and humidity levels inside a set range. Therefore, it is
crucial to continuously monitor and transmit the container's
temperature and humidity in order to guarantee the security
of any goods being carried.
Fig 5.Output Screen of IoT Risk Monitoring System
CONCLUSION
Fruits, vegetables, meat, chicken, and other dairy
products are examples of perishable commodities that have
a short shelf life after harvest and are more likely to
degrade if not stored properly. This degradation in
quality is the result of the storage unit's and the
transporting container’s incapability to preserve and
monitor key environmental characteristics. The Risk
Monitoring system helps to keep temperature, humidity
and freshness of these commodities from storage to
grocery shops without much manual supervision.
The system can be adapted to smart refrigerators as
well with necessary modifications. It helps to measure and
control ambient parameters so as to keep the commodities
fresh and nutrient. Decision making capability can be
incorporated with the help of Artificial Intelligence
algorithms based on the sensor values. Light density and
CO2 level can be controlled for freshness of commodities.
Thus, we can able to prevent the loss of perishable goods
by utilizing real-time monitoring system and the
information gathered over time will assist in identifying
the ideal storage conditions and will help to preserve
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353
the quality of goods throughout the course of a long-term
supply.
REFERENCES
[1] Afreen, H., & Bajwa, I. S. (2021). An IoT-Based Real-Time
Intelligent Monitoring and Notification System of Cold Storage.
IEEE Access, 9, 38236–38253. doi:10.1109/access.2021.3056672
[2] H. Feng, W. Wang, B. Chen and X. Zhang, "Evaluation on Frozen
Shellfish Quality by Blockchain Based Multi-Sensors Monitoring
and SVM Algorithm During Cold Storage," in IEEE Access, vol. 8,
pp. 54361-54370, 2020, doi: 10.1109/ACCESS.2020.2977723.
[3] M. H. Ahmadzadegan, M. S. Mohammadzadeh, G. Eftekharnejad
and H. Ghorbani, "Intelligent Monitoring Systems for
Transportation of Perishable Products based Internet of Things
(IoT) Technology," 2020 IEEE 9th International Conference on
Communication Systems and Network Technologies (CSNT),
2020, pp. 130-133, doi: 10.1109/CSNT48778.2020.9115745.
[4] L. Wanganoo and V. K. Shukla, "Real-Time Data Monitoring in
Cold Supply Chain Through NB- IoT," 2020 11th International
Conference on Computing, Communication and Networking
Technologies (ICCCNT), 2020, pp. 1-6, doi:
10.1109/ICCCNT49239.2020.9225360.
[5] Y. P. Tsang, K. L. Choy, C. H. Wu, G. T. S. Ho and H. Y. Lam,
"Blockchain-Driven IoT for Food Traceability With an Integrated
Consensus Mechanism," in IEEE Access, vol. 7, pp. 129000-
129017, 2019, doi: 10.1109/ACCESS.2019.2940227.
[6] A. S. . Bale, S. . Tiwari, A. . Khatokar, V. . N, and K. . Mohan M
S, “Bio-Inspired Computing-A Dive into Critical Problems,
Potential Architecture and Techniques”, Trends Sci, vol. 18, no. 23,
p. 703, Nov. 2021
[7] Bale, A.S., Kumar, S.S., Kiran Mohan, M.S., Vinay, N. (2022). A
Study of Improved Methods on Image Inpainting. In: Johri, P.,
Diván, M.J., Khanam, R., Marciszack, M., Will, A. (eds) Trends
and Advancements of Image Processing and Its Applications.
EAI/Springer Innovations in Communication and Computing.
Springer, Cham. https://doi.org/10.1007/978-3-030-75945-2_15
[8] B. C. R, S. Joy, A. S. Bale, A. S. Naidu, V. N and V. S N,
"Advanced Computing in IoT for Door Lock Automation," 2022
International Conference on Electronics and Renewable Systems
(ICEARS), 2022, pp. 565-569, doi:
10.1109/ICEARS53579.2022.9752140.
[9] S. Joy, R. Baby Chithra, A. S. Bale, N. Ghorpade, S. N. Varsha and
A. S. Naidu, "A Comparative Study on Recent Trends in Iris
Recognition Techniques," 2022 International Conference on
Electronics and Renewable Systems (ICEARS), 2022, pp. 1521-
1525, doi: 10.1109/ICEARS53579.2022.9752355.
[10] S. A. Huddar, B. G. Sheeparamatti and A. S. Bale, "Study of pull-
in voltage of a perforated SMA based MEMS Switch," 2017
International conference on Microelectronic Devices, Circuits and
Systems (ICMDCS), 2017, pp. 1-4, doi:
10.1109/ICMDCS.2017.8211584
[11] Ajay Sudhir Bale, Suhaas V. Reddy, Shivashankar A. Huddar,
Electromechanical characterization of Nitinol based RF MEMS
switch, Materials Today: Proceedings, volume 27, Part 1, 2020,
Pages 443-445, ISSN 2214-7853,
https://doi.org/10.1016/j.matpr.2019.11.263.
[12] J. Aditya Khatokar, N. Vinay, Ajay Sudhir Bale, M.A. Nayana, R.
Harini, V. Suhaas Reddy, N. Soundarya, T.Y. Satheesha, A.
Shivashankar Huddar, A study on improved methods in Micro-
electromechanical systems technology, Materials Today:
Proceedings, Volume 43, Part 6, 2021, Pages 3784-3790, ISSN
2214-7853, https://doi.org/10.1016/j.matpr.2020.10.993.
[13] Bale, A. S., Reddy, S. V., Tiwari, S. (2020). Effect of residual
stress on resonant frequency in Nitinol based thin film resonator.
IOP Conference Series: Materials Science and Engineering, 872,
012008. doi:10.1088/1757-899x/872/1/012008
[14] Ajay Sudhir Bale, Naveen Ghorpade, Muhammed Furqaan
Hashim, Jatin Vaishnav, Zahra Almaspoor, "A Comprehensive
Study on Metaverse and Its Impacts on Humans", Advances in
Human-Computer Interaction, vol. 2022, Article ID 3247060, 11
pages, 2022. https://doi.org/10.1155/2022/3247060
[15] Farooq, U.; Tao, W.; Alfian, G.; Kang, Y.-S.; Rhee, J. ePedigree
Traceability System for the Agricultural Food Supply Chain to
Ensure Consumer Health. Sustainability 2016, 8, 839.
https://doi.org/10.3390/su8090839
[16] Feng, H.; Zhang, M.; Liu, P.; Liu, Y.; Zhang, X. Evaluation of IoT-
Enabled Monitoring and Electronic Nose Spoilage Detection for
Salmon Freshness During Cold Storage. Foods 2020, 9, 1579.
https://doi.org/10.3390/foods9111579
[17] Badia-Melis, R.; Ruiz-Garcia, L.; Garcia-Hierro, J.; Villalba, J.I.R.
Refrigerated Fruit Storage Monitoring Combining Two Different
Wireless Sensing Technologies: RFID and WSN. Sensors 2015, 15,
4781-4795. https://doi.org/10.3390/s150304781
[18] Neri, L.; Faieta, M.; Di Mattia, C.; Sacchetti, G.; Mastrocola, D.;
Pittia, P. Antioxidant Activity in Frozen Plant Foods: Effect of
Cryoprotectants, Freezing Process and Frozen Storage. Foods
2020, 9, 1886. https://doi.org/10.3390/foods9121886
[19] Kusnandar; Permana, I.; Chiang, W.; Wang, F.; Liou, C. Energy
Consumption Analysis for Coupling Air Conditioners and Cold
[20] Storage Showcase Equipment in a Convenience Store. Energies
2022, 15, 4857. https://doi.org/10.3390/en15134857
Authorized licensed use limited to: NATIONAL INSTITUTE OF TECHNOLOGY TIRUCHIRAPALLI. Downloaded on March 18,2023 at 11:18:31 UTC from IEEE Xplore. Restrictions apply.
