Introducing a cutting-edge smart weather monitoring system
ready to completely transform the availability and use of
weather data. This effortlessly connects weather parameter
reporting directly to consumers, beyond the traditional
reliance on weather forecasting services. Fundamentally, this
contemporary system makes use of the potential of cutting-
edge innovations like cloud computing and the Internet of
Things (IoT).
By incorporating temperature, humidity, light and rain
sensors, the system continuously monitors and delivers real-
time weather statistics. This dynamic sensor array enables
users to obtain accurate weather information without any
delay. IoT principles reinforce the system's architecture,
linking a range of devices to the internet and enabling the
effortless transfer of data to the Cloud. This cloud-based
infrastructure works as a central hub for collecting,
processing, and publishing weather data.
This useful and adaptable technology does more than just
gather data. It is a clear example of an Internet of Things
application, enabling the easy gathering, processing, and use
of various meteorological data. With ease, users may receive
warnings, set up alerts for certain weather events, modify
appliances, and carry out comprehensive long-term analysis.
Additionally, graphical interpretations improve data
presentation by making it easier to read.
Main components of this system include the Arduino
Nano board, a microcontroller board with versatile
capabilities, and the DHT11 temperature and humidity sensor,
involved in detecting and reporting these essential parameters.
A WIFI module ESP32 is employed to transmit the collected
data to a web server, ensuring real-time updates and
accessibility from anywhere across the globe.
In a rapidly evolving world, the Internet of Things is
prepared to reshape environmental monitoring, allowing for
the capture, handling, and transmission of weather parameters
through a network of sensors and devices. The Cloud aspect
of the system provides necessary resources like data storage
and computing power, all with minimal user involvement.
This collaboration between IoT and Cloud technologies leads
to a new era of weather monitoring and reporting.
The applicability of this approach spans numerous
different fields, such as environmental research, urban
planning, and agriculture. Users in various geographic places
can effortlessly and remotely monitor the weather. Users are
kept informed and ready thanks to the system's continuous
data transmission, which guarantees that real-time
information is quickly sent to the web server.
The system adds a layer of proactive functionality by
enabling users to customize alerts for specific weather events
in addition to its reporting capabilities. In today's connected
world, our Internet of Things (IoT)-based Weather Monitoring
and Reporting system offers a thorough, effective, and user-
friendly method to weather data collecting and propagation.
The Internet of Things (IoT) represents a cutting-edge
computerization and analytical framework that leverages
networking, sensing capabilities, big data analysis, and
artificial intelligence technology to offer comprehensive
solutions for products or services. These integrated systems
provide enhanced readability, control, and operational
efficiency when installed in various industries and systems.
IoT systems exhibit significant versatility and adaptability
across diverse industries, making them compatible for
deployment in any type of environment. They modernize data
gathering, automation processes, and operational techniques
by employing the capabilities of intelligent devices and robust
enabling technologies.
Utilizing IoT and Cloud Computing for Weather-Health Monitoring
Application
ASIF GULRAIZ1, HASEEB GULRAIZ2, MOHIUDDIN ZIA3, SHAHNILA BADAR4,
SYED SAJJAD HAIDER ZAIDI5
1Electrical Engineering Department DHA Suffa University / National Univeristy of Science & Technology Karachi,
PAKISTAN
2Computer Engineering Department Sir Syed University of Engineering & Technology Karachi, PAKISTAN
3Electrical Engineering Dpartment DHA Suffa Univerity Karachi, PAKISTAN
4Electrical Engineering Department DHA Suffa University Karachi, PAKISTAN
5Electrical and Power Engineering Dept. National University of Sciences & Technology Karachi, PAKISTAN
Abstract: - In an era defined by increasing climate changes and an increasing importance of accurate weather information,
there is a need for a smart IoT- based weather monitoring system. This paper proposes an innovative system that uses
Internet of Things (IoT) technology to advance weather monitoring and global data accessibility using Cloud computing.
By seamlessly connecting multiple devices, such as sensors, electronic gadgets, and automotive electronics, to the internet,
this system offers a complete solution for monitoring environmental conditions such as temperature, relative humidity,
pressure, and rain levels. Specialized sensors collect data, which is then transformed into graphical interface using an app,
enabling real-time weather information access worldwide. This technology has the ability to rethink how we convey and
analyze environmental data with optimal efficiency and reach in industries including agriculture, urban planning, and
environmental research.
Key-words: - Cloud computing, Electrical measurements, health monitoring, Internet of Things (IoT), Intelligent sensors,
Weather Monitoring.
Received: March 9, 2024. Revised: August 19, 2024. Accepted: September 11, 2024. Published: October 9, 2024.
1. Introduction
2. Literature Review
2.1 Internet of Thing
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2024.6.18
Asif Gulraiz, Haseeb Gulraiz, Mohiuddin Zia,
Shahnila Badar, Syed Sajjad Haider Zaidi
E-ISSN: 2769-2507
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•Artificial Intelligence (AI): IoT essentially infuses everyday
objects with intelligence, enriching various aspects of life by
hitching the potential of data gathering, AI algorithms, and
interconnected networks.
•Connectivity: With promising networking technologies,
including those specific to IoT, networks are no longer limited
to major providers.
•Sensors: Sensors are integral to IoT, serving as pivotal
instruments that advance IoT from a passive network of
devices into an active system capable of seamless integration
with the real world.
•Active Engagement: Much of the interaction with connected
technology today is passive in nature. IoT introduces a fresh
perspective, emphasizing active engagement with content,
products, or services.
•Compact Devices: As anticipated, devices have increasingly
evolved to become smaller, more affordable, and more
powerful. IoT influences purpose-built, compact devices to
deliver precision, scalability, and adaptability to its
functionalities.
In this paper [1], the author discusses the increasing
significance of weather prediction systems, particularly in the
context of extreme weather events that have adverse impacts
on both lives and property. The paper emphasizes the critical
challenge of improving the accuracy of weather data to
enhance predictive capabilities and bolster resilience against
detrimental weather conditions. Developing countries, such as
Uganda, and others face difficulties in generating timely and
precise weather data due to limited weather observation
resources and the high costs associated with developing
automated weather monitoring systems. The inadequate
financing available to national meteorological services in
these countries intensifies this challenge.
To address these issues, the author recommends the
development of an Automatic Weather Monitoring Station
based on a wireless sensor network. The plan implies creating
three generations of prototypes, with each iteration targeting
to enhance functionality and utility based on the specific needs
of its generation. The author also underlines the importance of
improving non-functional aspects such as power efficiency,
data accuracy, reliability, and data transmission while
simultaneously decreasing the cost to make such technology
more robust and affordable. The intended outcome of this
proposed work is to enable developing nations, like Uganda,
to acquire AWS systems in sufficient quantities, ultimately
improving weather forecasting capabilities.
In a different research paper [2], the author introduces an
IoT-based weather monitoring system. This system controls
various sensors to collect environmental parameters,
including humidity, temperature, pressure, rain levels, and
light intensity using an LDR sensor. Additionally, the system
calculates the dew point value from temperature data. The
implementation includes an SMS alert system triggered when
sensing parameters exceed predefined thresholds, enhancing
the system's usefulness. In addition, the author integrates
email and tweet alerting systems into the weather monitoring
process. The hardware components of this system include the
Node MCU 8266 and a range of sensors.
Another research paper [3], describes a low-cost live
weather monitoring system incorporating an OLED display.
The author highlights the transformative potential of IoT in
various fields and describes this advanced system for real-time
weather condition monitoring. The live weather monitoring
system is placed as a valuable tool for farmers, industries,
daily activities, and educational institutions, simplifying
weather-related decision-making. The system utilizes an
ESP8266-EX microcontroller-based WeMos D1 board,
executed with Arduino, to retrieve data from the cloud. This
board, equipped with 4MB of flash memory, is programmed
with Node MCU and Arduino IDE. The system collects
weather data using only two components: WeMos and OLED.
Data is stored on the ThingSpeak cloud platform for
accessibility and is simultaneously displayed on the OLED
screen. The primary aim of this system is to present live
weather information through the OLED display.
In a unique context, the author of reference [4], proposes a
comprehensive weather monitoring and prediction system that
can aid people in their day-to-day activities, particularly in
sectors such as agriculture and industry. This system involves
two stages: sensing weather conditions and utilizing deep
learning technology for real-time reporting on stations and
buses. Weather forecasting is accomplished through a friction
model, with multilayer perception models and long-term
memory used for training and verification. The system's
performance is assessed against data from environmental
safety agencies and observation systems. The author stresses
the reliability of this system in monitoring weather conditions
and its potential to provide one-day weather forecasts.
Finally, in reference [5], the author implements an IoT-
based weather monitoring system with a focus on using IoT
technology to monitor weather conditions and detect climate-
changing patterns. The system employs various sensors to
collect climate data, which is then stored in the cloud for
analysis and dissemination. The algorithm, known as the
swarm algorithm, is used to enhance data accuracy. This
project aims to raise awareness of climate condition changes
and provides an accurate and efficient output. Rain detection
is achieved using a rain sensor, which measures voltage
changes when raindrops contact its strips.
These research papers collectively highlight the
importance of weather monitoring and prediction systems in
various contexts and present innovative approaches to address
the associated challenges. They showcase advancements in
sensor technology, data analysis, and communication
methods, ultimately aiding to improved weather forecasting
and awareness.
The Arduino Uno serves as the main component of the
Internet of Things-enabled weather monitoring system, and it
is responsible for measuring four crucial weather parameters.
These parameters, which are each precisely monitored by their
own dedicated sensors, include temperature, humidity, light
intensity, and rainfall levels. The Arduino Uno's built-in
Analogue to Digital Converter (ADC) capabilities make it
easy to integrate these sensors with the board and streamline
the data acquisition process.
This weather monitoring system's primary goal is to
provide unmatched precision and dependability in the fields
of climate observation and weather tracking. By utilizing
2.2 Key Features (IoT)
2.3 Work done in past
3. Methodology
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2024.6.18
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renewable energy sources—mainly solar panels for charging
the attached battery—it accomplishes this feat.
One of the system's standout features is its ability to access
real-time weather information and data via the World Wide
Web. This dynamic connectivity opens up a world of
possibilities, allowing users to stay informed about current
weather conditions and any ongoing climate changes at their
fingertips. Moreover, the system is designed to communicate
seamlessly through WIFI networks, further enhancing its
accessibility and reach.
The system is comprised of a microcontroller serving as
central processing unit that organizes the entire operation is
shown in figure-1. The microcontroller acts as a hub, allowing
seamless connections with a variety of sensors and devices.
These interconnected sensors are under the control of the
microcontroller, which efficiently extracts data from them. It
takes the responsibility of collecting data from these sensors,
harnessing their capabilities to monitor and analyze key
environmental parameters. These parameters include
temperature, humidity, atmospheric pressure, and rainfall.
Through this detailed data collection process, the system gains
a type of understanding of the current environmental
conditions.
Data is transmitted on the internet which is achieved
through a Wi-Fi module that is seamlessly integrated into the
system. This connection is managed with an app, a user-
friendly platform that streamlines the route of sharing the
sensor data with the online world.
By harnessing this connectivity, the system transcends
geographical boundaries, making it possible for users to
access real-time information about temperature, humidity,
pressure, and rainfall from anywhere with an internet
connection. This data becomes a valuable resource for various
applications, including weather forecasting, environmental
monitoring, and even smart home automation.
In figure 2, a project flow chart is shown. Initially data is
read from sensors by the controller, which is forward to the
JSON packets, then the data is transmitted to ESP32 controller
which further process it to the firebase station where the data
is logged and transmitted to cell phones via an android app.
Start
Arduino Nano
Initialization
If
time>=10
sec
Read Sensors Data
Write Data in
JSON Packet
Transmit Data to
ESP32 MCU
No
Start
ESP32 MCU
Initialization
If data
received from
Arduino Nano
Read Data from
JSON Packet
Set Data to
Firebase
End
End
No
Yes
Yes
Figure 2: Project Flowchart
The Arduino Nano is a compact yet powerful
microcontroller board, built around the ATmega328P
microcontroller chip. At its core, the Arduino Nano features
14 digital input/output pins, with six of them capable of
serving as PWM (Pulse Width Modulation) outputs.
Additionally, there are six analog input pins, which are useful
for interfacing with sensors and analog devices. The board
operates with a 16 MHz ceramic resonator for precise timing.
The ESP32 features a powerful dual-core Xtensa LX6
microprocessor, which provides substantial processing power
for a wide range of tasks. This microcontroller board further
distinguishes itself by offering a built-in Wi-Fi and Bluetooth
connectivity module. This feature opens the door to seamless
wireless communication and control, making it an ideal
choice for IoT applications. It’s a highly versatile
microcontroller board renowned for its advanced capabilities
and broad applicability in the realm of embedded systems and
IoT (Internet of Things) projects.
The DHT11 is a basic but reliable temperature and humidity
sensor. Its sensor provides two essential measurements:
temperature and humidity. It uses a capacitive humidity
sensor and a thermistor for temperature measurement. This
compact sensor module consists of a sensor element and a
small circuit that converts the analog sensor data into digital
signals, making it easy to interface with microcontrollers. The
Mobile
Temperature /
Humidity Sensor
Pressure Sensor
Light Sensor
Rain Sensor
Arduino
Nano
ESP32
MCU
Cloud
Figure 1: Project Block Diagram
4. Project Hardware
4.1 Arduino Nano
4.2 ESP32 MCU
4.3 DHT11(Temperature & Humidity sensor)
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2024.6.18
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DHT11's output is digital, and it communicates over a single-
wire interface, simplifying its integration into various
projects.
The BMP180 is a precise sensor that offers in measuring
atmospheric pressure and temperature. BMP180 consists of a
a robust and sensitive sensor element that can provide precise
measurements of both barometric pressure and temperature.
This sensor is paired with an integrated circuit that processes
the data and delivers it in a convenient digital format,
facilitating unified integration with microcontrollers and
other electronic devices.
To detect moisture and rainfall, a rain sensor module is used.
The sensor board houses the rain-sensitive component
typically a hygroscopic substance that collects moisture
makes up the rain sensor module. The electrical conductivity
or resistance of the sensor is altered when moisture or
raindrops come into contact with its surface. The rain sensor
module's ability to generate a binary output that indicates
whether or not it is raining at the moment is one of its main
uses. The module normally generates a digital signal to
indicate the presence of rainfall when rain is detected. This
signal frequently changes from a high state to a low state.
The photoresistor module, called the Light Dependent
Resistor (LDR) module, is a crucial component that makes
ambient light level sensing possible. Many application are
exist for this sensor, including environmental monitoring and
automated lighting control. The LDR module's unique feature
is its real-time light level feedback, which enables automation
and dynamic modifications in response to shifting lighting
circumstances. Because of this feature, it is useful in
applications such as smart homes, security systems, street
lighting management, and weather monitoring.
Figure 4: Hardware in the box
A proactive approach to environmental protection is achieved
through a smart weather monitoring station using Wi-Fi-
connected sensors as shown in figure 4. This embedded
system ensures cost-effective environmental monitoring,
transmitting data to the cloud for analysis and sharing. Its
adaptability extends to tracking pollution in urban and
industrial areas, safeguarding public health. Realtime
databases are designed to have all the updates of the weather
in real time. Cloud computing is enabled to process all the
data and maintain database on remote location. This database
will be accessible through an android app as shown in figure
6 and using firebase link as in figure 5.
Figure 5: Realtime Database at Firebase Cloud Computing
Figure 6: Weather health monitoring app on Android
Figure 3: Project PCB Diagram
4.4 BMP180 (Pressure Sensor)
4.5 MH-RD (Rain Sensor)
4.6 LDR Module (Light Sensor)
5. Results
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Figure 7: Pressure Vs Time graph
Figure 7 depicts the temporal progression of monitored
pressure levels, showing a consistent trend characterized by
relatively minor fluctuations in atmospheric pressure. The
graphical representation underscores the stability typically
observed in atmospheric pressure variations. Notably, the x-
axis presents time intervals during which readings are
documented, highlighting the frequency of data logging to be
approximately once per second. This real-time data stream is
seamlessly transmitted to cloud storage, facilitating its
potential visualization on weather monitoring applications.
Figure 8: Smoke Vs Time graph
Figure 9 clearly represents a notable decrease in smoke levels
as the night progresses, showcasing a dynamic trend that is
continuously updated in real-time across both the website and
the corresponding application platforms. This visual
representation not only highlights the continuous patterns in
smoke concentration but also highlights the seamless
synchronization of data propagation across digital interfaces,
ensuring users are promptly updated about environmental
conditions.
Figure 9: Sunlight Vs Time graph
The values shown in Figure 9 are related to light intensity,
showcasing the present positioning of the device within an
environment illuminated by artificial light sources. This
relative backdrop underscores the known fluctuations
observed, directly influenced by the intermittent switching of
lights. Consequently, these alterations apply an evident
impact on the recorded dataset, interpreting the dynamic
interplay between environmental factors and measured
parameters.
Figure 10: Humidity Vs Time graph
Humidity, as depicted in Figure 10, is presented in percentage
format, suggesting relatively stable variations. Nonetheless,
upon examining the X-axis, it becomes apparent that the time
periods for all these parameters are updated simultaneously.
Figure 11 depicts the trend in temperature value changes over
a span of 1 hour, demonstrating the effectiveness of the
temperature sensor and the efficiency of data logging.
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Figure 11: Temperature Vs Time graph
The establishment of a continuous weather monitoring
station within the natural environment serves as a form of
proactive environmental protection, essentially giving rise to
what can be described as a "smart environment." This
endeavor entails the strategic deployment of sensor devices
throughout the environment, enabling the collection and
analysis of critical data. This innovative approach effectively
bridges the natural world with the digital realm, facilitating
real-time data access for users through Wi-Fi connectivity.
The paper introduces a highly efficient and cost-effective
embedded system tailored for intelligent environmental
monitoring. Moreover, it streamlines the transmission of
sensor parameters to cloud storage, ensuring data availability
for future analysis and sharing with a broader audience.
Significantly, the model's adaptability extends to monitoring
burgeoning urban areas and industrial zones for
comprehensive pollution tracking, offering an economical and
efficient solution for ongoing environmental monitoring
aimed at safeguarding public health from pollution-related
hazards.
There are multiple opportunities to grow and improve this
system in the future. A viable path would be to install more
sensors and develop satellite connectivity to make it a
platform for monitoring the environment on a global scale.
This expansion can include monitoring more environmental
data, such as air pressure, concentrations of oxygen, and CO2
levels, among others. Moreover, the system has great promise
in fields where real-time data is critical, like navigation,
aviation, and military operations. Its application can also
reach medical research centres and hospitals, enabling
research on the "Effect of Weather on Health."
6. Conclusion
7. Future Scope
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
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DOI: 10.37394/232027.2024.6.18
E-ISSN: 2769-2507
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