IoT‑Enabled Energy Harvesting Sensor Network for Smart
Industries
G. Thirumalaiah, A. Reddy Ganga Vasantha Lakshmi, D. Shahid,
D. Sardhar Hussain and N. Poojitha
Department of ECE, Annamacharya University, Rajampet, Andhra Pradesh, India
Keywords: Wireless Sensor Networks (WSNs), Industrial Monitoring, Predictive Analysis, Real‑Time Monitoring,
Renewable Energy, Energy Harvesting.
Abstract: Wireless Sensor Networks (WSNs) serve as an advanced industrial monitoring solution because they obtain
real-time data for multiple industrial applications while optimizing these procedures. The networks include
distributed sensors and actuators that permit simple measurements of critical parameters ranging from
temperature to humidity along with vibration and pressure. Economical reliable continuous monitoring
requirements for industries adopting smart automation systems causes WSNs to adopt renewable energy
systems and energy-harvesting methods. Operating durations for these sensor networks become extended by
employing renewable solar energy sources and harvesting energy from wind power and mechanical vibrations
while reducing traditional power dependency. WSN devices enabled with environmental ambient energy can
establish independent power systems that deliver continuous industrial monitoring operations. This paper
investigates sustainable operation needs of wireless sensor networks with energy harvesting capabilities
required for smart industrial monitoring and describes ongoing developments and obstacles in renewable
power utilization for autonomous capability.The proposed Smart Self-Powered Wireless Sensor Network for
Industrial Monitoring using Machine Learning establishes a self-charging industrial monitoring system that
operates through a 12V battery powered by combinations of solar panel energy and piezoelectric sensor
energy. The system operates continuously through automatic battery and AC mains power switching managed
by relays. The central processing unit of this system operates on a Raspberry Pi while monitoring several
sensors including DHT11 for environmental temperature detection and humidity measurements and DS18B20
for machine interior temperature evaluation along with harmful gas detection through an MQ-135 gas sensor.
1 INTRODUCTION
The accelerated industrial development rate makes it
essential to have effective monitoring and
management systems which modern industry
demands. Industrial applications of today need
monitoring solutions beyond traditional human-
controlled methods because traditional systems lack
adaptability features. A wireless self-powered smart
sensor network introduced by the scientific project
addresses monitoring obstacles through its
autonomous operation system which draws power
from renewable sources.
This system enables real-time data acquisition
through machine learning algorithms by connecting
Raspberry Pi with temperature, humidity and gas
detectors which permits environmental condition
analysis and prediction. The developed technical
system achieved automated operations by producing
rapid notifications to improve industrial safety
standards. WSNs offer industrial monitoring
improved attractiveness since they deliver continuous
real-time data about temperature and humidity
measurements and determine pressure and vibration
values and analyze gas concentrations.
The steady execution of industrial operations
along with operational efficiency needs precise
parameters for optimal safety functions. Industrial
WSNs decide through data analytics instead of
exploratory methods to enhance operational
performance while fulfilling predictive maintenance
needs and environmental surveillance (Dahiya, R. S.,
& Shankar, P. 2020). Progressing reliable power
supply with uninterrupted power delivery to every
WSN node remains the major implementation
Thirumalaiah, G., Lakshmi, A. R. G. V., Shahid, D., Hussain, D. S. and Poojitha, N.
IoT-Enabled Energy Harvesting Sensor Network for Smart Industries.
DOI: 10.5220/0013882400004919
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 2, pages
321-329
ISBN: 978-989-758-777-1
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
321

challenge among other deployment obstacles (Zhang,
L., & Zhang, Y. 2018).
Sensor nodes obtain operating power through
energy harvesting techniques which extract power
from surrounding environmental sources such as
sunlight and wind variations as well as heat and
vibrations sources (Zhang, J., & Wang, Q. 2017).
Solar energy stands as one of the well-recognized
renewable energy sources operating WSNs while
simultaneously serving as their prime energy supply
method.
Outer applications benefit from extensive
research on WSNs powered by solar energy because
solar radiation supplies necessary power to sensor
nodes and other devices (Prabhu, R. S., & Kumar, K.
2019). The harvesting of vibration-based energy has
shown promise for powering WSNs in industrial
facilities because industrial machines produce usable
electrical energy from their vibrations (Khan, R. A.,
& Hussain, S. 2020). These energy-efficient
technologies function together to enable autonomous
WSN settings with extended operational periods
requiring minimal servicing thus being suitable for
industrial monitoring framework needs
(Bhattacharya, R., & Sharma, V. 2018).
Machines using power harvesting methods gain
two major benefits by allowing cheaper monitoring
systems and better environmental performance
during operations with extended operational time
(Zhu, Y., & Zhang, Z. 2019). The literature presents
three essential methods to enhance energy efficiency
of sensor nodes by balancing communication
operations and hardware power usage levels when
using supercapacitor-based recharge systems as
demonstrated in (Xu, et al,2017, Lee, et al, 2020). The
SWSN technology developed from combining data
analytics with machine learning offers operational
and predictive maintenance functions according to
(Yang, H., & Zhou, W. 2021).
The study investigates how renewable energy
systems together with energy harvesting influence
industrial control operations through Wireless Sensor
Networks implementations. The research observes
current industrial research patterns to analyze critical
integration issues prior to developing self-powered
monitoring systems for sustainable industrial
operations.
2 LITERATURE REVIEW
Zhang et al established an energy-efficient WSN for
industrial use in 2022 by enabling nodes using solar
energy harvesting for power supply. The authors
discovered that industrial facilities can implement
successful solar-powered WSNs which produce
sustainable light energy collection to operate sensors
autonomously from conventional utility sources. The
authors emphasized the need to develop advanced
energy storage systems because solar power
generation performs unpredictably when lighting
decreases.
In their work Lee et al provided vibration-based
energy harvesting as an effective power method for
Wireless Sensor Networks operating within industrial
sites that exhibit regular machine-generated
oscillations. The incorporated sensors operated by
converting mechanical vibration into electric energy
to power their sensor arrays. A continuous operation
mode without battery changes helped reduce
maintenance expenses for the proposed system.
Vibrational power generation showed limited
capacity according to the authors which restricted
sensor operation at the same time.
In 2020 Patel and Bansal investigated the
implementation of solar power integration with
vibration energy for improved WSN system
reliability. The practical implementation of this model
showed significant worth in industrial environments
getting steady solar illumination together with
vibrations. Research findings established that hybrid
energy systems provided extended reliable power
supply but researchers faced difficulties when
handling the combined energy flows.
During 2019 Sharma et al. conducted research on
smart factory monitoring system energy harvesting
solutions. This system contained solar panels along
with heat energy harvesting elements that let it
operate independently. Device temperature and
humidity and pressure level detection necessitated
power generation through heat from machines and
solar energy systems. System power utilization
decreased significantly according to the authors
though they accepted the unit's thermal energy
harvesting was less efficient than solar energy
collection in regions with minimal temperature
fluctuations.
In 2018 Singh and Gupta performed research on
machine learning applications for self-powered
WSNs in industrial monitoring. The researchers
optimized power management and sensor data
collection by using machine learning algorithms in
their research work. The implementation of predictive
models enabled forecasting of energy consumption
and system failures to increase the operational
duration of WSNs. The accurate operation of machine
learning algorithms proved difficult because
permanent training alongside specific adjustments
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were necessary to achieve precise predictions in
continuously changing industrial environments.
3 EXISTING SYSTEM
The current industrial monitoring system requires
connected traditional sensors for monitoring yet
control units need manual and extensive support for
maintenance. Diverse industries lack sufficient
support from basic industrial monitoring systems for
their operational requirements. Typical models
operating today use only basic warning alarms and
on-site data recording since predictive machine
learning analytics techniques are not implemented.
The main power grids experience standard power
supply breakdowns that result in electricity
interruption during utility outages. The dual power
structure of batteries with AC mains triggers
unnecessary double consumption of energy
throughout the system operations. The complete
environmental observation ends up degraded because
several devices do not come equipped with integrated
DHT11, DS18B20 and MQ-135 sensors. The present
models demonstrate several limitations in real-time
assessment while lacking intelligent response
abilities which results in the need for better industrial
safety system solutions.
4 PROPOSED SYSTEM
Modern sensor devices in combination with
renewable energy systems through artificial
intelligence achieve security enhancement of
industrial monitoring features based on sustainable
advanced designs within the Smart Self-Powered
Wireless Sensor Network. An autonomous power
supply results from integrating solar panels with
piezoelectric sensors because the system relies on a
12V battery to work during periods without solar or
electric power. The system develops higher reliability
when automated battery power switching operates
through relays. The Raspberry Pi collects machine
and environmental data through its sensors which
consist of combined DHT11 humidity and
temperature equipment along with DS18B20
thermometers and MQ-135 gas detection tools. The
system’s motor driver facilitates precise control over
machinery speed, with manual switches for fine-
tuning. When SMS automation activates the buzzer
produces notifications which play through the LCD
display before connecting to working personnel via
GSM. The system performs independent operational
predictions through sensor input while using
collected information to modify motor speed controls
to reduce operational failures. Peak energy efficiency
reaches its optimum through this integrated system
since it enhances operational procedures while
detecting impending system dangers for more
effective maintenance scheduling that reduces
operational risks. Machines under this system
maintain autonomous operation until they achieve
better specialized energy consumption levels while
remaining reliable and safe.
4.1 Proposed System Block Diagram
Figure 1: Proposed System Block Diagram.
The Smart Self-Powered Wireless Sensor Network
solution (SSWSN) proves its capability to construct
intelligent self-powered monitoring infrastructure for
industrial applications through combination of
renewable energy systems and next-generation sensor
technology and machine learning methods. Detailed
examination of system elements and functionalities
follows next in the below paragraph.
Figure 1 shows
the Proposed System Block Diagram.
4.2 Energy Supply and Management
A solar panel supports energy operations through
sustainable power resources which result in
affordable and green solutions. The system utilizes
piezoelectric sensors that turn mechanical energy into
electricity which serves as an enhanced charging
capability within vibration-driven industrial
conditions.
A 12V battery deployed as the primary power
source will be charged automatically through both
solar energy from the panels and the piezoelectric
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sensors to support system functionality in all
circumstances including dark conditions and power
failures.
A switching mechanism based on relays enables
the panel to switch power between the battery and AC
mains delivery while improving both reliability and
operational smoothness.
4.3 Data Acquisition and Sensor
Network
The central CPU functions of the Raspberry Pi system
gather and analyze sensor data for immediate real-
time monitoring.
The DHT11 sensor operates as an environmental
measurement device that evaluates both temperature
and humidity levels.
The internal temperature of the machine is
monitored by the DS18B20 Sensor for hot
temperature protection against equipment destruction
and burn damage.
The MQ-135 Sensor serves as a detection system
for harmful gases which helps safety operations
through its early identifying of hazardous situations
in workplaces.
4.4 Real-Time Monitoring and Control
Motor Control: Allows precise motor speed control
for optimal performance and energy efficiency.
The LCD touchscreen presents immediate sensor
readings to show system operational status combined
with temperature measurements along with humidity
levels and gas detections and motor operating
information.
4.5 Alerts and Notifications
Buzzer: Provides audible alerts for critical conditions,
such as high temperature or gas levels.
A GSM Module can transmit SMS alert messages
during emergencies or abnormal system conditions to
take swift actions from any distance.
4.6 Machine Learning Integration
The performance attributes in sensor-based system
designs enable users to merge defect detection
systems with output functionality.
The system develops automatic motor speed control
functionality because of its analysis capabilities.
By analyzing historical data predictive system
equipment failure models calculate the perfect
preventive maintenance periods needed to defend
components from damage.
4.7 Enhanced Safety and Efficiency
Automated processing of predictive analytics systems
enables organizations to achieve operational
excellence along with minimal waste production
through optimized energy system management.
Emergency response services achieve improved
safety through their enhanced operation efficiency
that permits gas level monitoring and incident data
collection of temperature and humidity metrics.
5 HARDWARE & SOFTWARE
COMPONENTS
5.1 12V Battery
• A rechargeable battery used for various
applications, offering long-term cost savings
and reducing waste.
• Types: Nickel-Cadmium, Nickel-Metal
Hydride, Lithium-Ion, Lithium-Polymer;
Voltage: 1.2V (Nickel-based), 3.7V-12V
(Lithium-based)
5.2 Piezoelectric Sensor
• Operates based on piezoelectricity, where
mechanical stress generates electricity, used
for measuring physical quantities like
pressure and acceleration.
• Measurement Range: Dependent on sensor
design; Impedance: ≤500Ω
5.3 Solar Panel
• Absorbs sunlight to generate electricity
through the photovoltaic effect, typically
used for residential, commercial, and off-
grid power generation.
• Efficiency: 15%-22% (silicon-based cells);
Voltage: 12V-24V
5.4 Power Supply
• Converts different forms of energy (e.g., AC,
solar, mechanical) into usable electrical
power, essential for devices like computers
and industrial machinery.
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• Input Voltage: 110V/115V or 220V/240V;
Output Voltage: 5V, 12V, depending on
device.
5.5 LED
• A semiconductor device that emits light
when current flows through it, known for
being energy-efficient and durable.
• Material: GaAs, GaP, GaN, InGaAlP;
Lifespan: Up to 50,000 hours.
5.6 Relay
• An electromagnetic switch used for
controlling circuits with low-power signals,
suitable for handling higher voltages or
currents.
• Types: SPST, SPDT, DPST, DPDT;
Applications: Logic functions, controlling
high voltage/currents.
5.7 Raspberry Pi
• A compact computer designed for DIY
projects and teaching computer science.
• Processor: Broadcom BCM2835
ARM1176JZF-S; Memory: 256 MB to 512
MB RAM.
5.8 DHT11 Sensor
(Temperature/Humidity)
• A basic, low-cost digital sensor used to
measure temperature and humidity.
• Humidity Range: 20% to 90% RH;
Temperature Range: 0°C to 50°C.
5.9 DS18B20(Dallas) Temperature
Sensor
• A digital temperature sensor with a wide
range and high accuracy, using a single-wire
protocol.
• Temperature Range: -55°C to +125°C;
Accuracy: ±0.5°C.
5.10 MQ135 Gas Sensor Module
• Detects gases like ammonia, CO2, alcohol,
and benzene.
• Detection Range: 10-10,000 ppm; Output
Type: Digital and analog.
5.11 Switch (Push Button)
• A mechanical switch used to turn devices on
or off, commonly found in industrial,
medical, and consumer electronics.
• Power Rating: Max 50mA, 24V DC;
Operating Temperature: -20°C to +70°C.
5.12 Motor Driver (L293D)
• Controls motors in robotics, commonly used
for dual DC motor operation.
• Pins Description: Enable, Input, Output,
Ground for motor control; Power: Vcc2 for
motor power.
5.13 DC Motor
• Converts electrical energy (DC) into
mechanical energy through rotation, used in
various applications like toys, vehicles, and
industrial machinery.
• Components: Stator, armature, commutator;
Applications: Electric vehicles, conveyors,
fans, power tools.
5.14 CPU Fan
• Ensures that the CPU remains cool by
dissipating heat, preventing thermal
throttling or failure.
• Components: Fan blades, motor, mounting
points; Types: May include heatsinks,
adaptive speed features.
5.15 LCD
• A thin, power-efficient display used in
calculators, monitors, and other devices.
• Display Size: 16x2 characters; Voltage: 5V
(4.7V – 5.3V).
5.16 GSM Module
• Enables wireless communication for voice
calls, SMS, and data transmission via
GSM/GPRS networks.
• Frequencies: 850MHz, 900MHz, 1800MHz,
1900MHz; Interfaces: RS-232
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5.17 Buzzer
• A signaling device used in alarms and
timers.
• Rated Voltage: 6V DC; Operating Voltage:
4-8V DC
5.18 Raspbian
• Official OS for Raspberry Pi, optimized with
over 35,000 software packages.
• Base: Debian-based; Software: 35,000+
packages
5.19 Python
• A versatile programming language
supporting multiple paradigms, used across
platforms.
• Type: Interpreted, high-level; Cross-
Platform: Windows, Linux, macOS
5.20 ThingSpeak
• IoT platform for aggregating and visualizing
real-time sensor data.
• Type: IoT analytics platform; Features: Data
aggregation, real-time visualization
5.21 VNC Viewer
• Remotely access and control computers over
a network, supporting various platforms.
• Protocol: VNC; Security: Password
protection, encryption
6 EXPERIMENTAL SETUPS
Install a solar panel and piezoelectric sensor to
generate electrical power, connecting them to a 12V
battery for energy storage. Use a relay module to
switch between the battery and AC power as needed,
and ensure a stable power supply for the system.
Connect the Raspberry Pi to the power unit, and
attach sensors like the DHT11, Dallas Temperature
sensor, and MQ135 for real-time monitoring of
temperature, humidity, and harmful gases. Install
switches for manual control, and integrate a motor
driver, relay, and CPU fan for cooling. Connect an
LCD display for system updates. Figure 3 shows the
Relay Shifts the Power Supply Between Ac Adapter
and Battery.
Integrate a GSM module for mobile
communication to send SMS alerts, and attach a
buzzer for audible notifications. Configure the
Raspberry Pi to connect to an IoT cloud platform for
remote monitoring and data analysis. Power up the
system and verify all components, ensuring that
sensors, actuators, and communication modules are
functioning properly. Figure 2 shows the Placement
of hardware components and their connections.
Figure 4 shows the Run the code. Figure 5 shows the
Data is updating time to time on the python shell.
Figure 6 shows the Think speak Will Represent the
Parameters in Graphical Format. Figure 7 shows the
Through GSM module received data messages sends
to mobile phone frequently or time to time.
Figure 2: Placement of Hardware Components and Their
Connections.
Figure 3: Relay Shifts the Power Supply Between AC
Adapter and Battery.
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Figure 4: Run the Code.
Figure 5: Data Is Updating Time to Time on the Python
Shell.
Figure 6: Think Speak Will Represent the Parameters in
Graphical Format.
Figure 7: Through GSM Module Received Data Messages
Sends to Mobile Phone Frequently or Time to Time.
6.1 Graphical Representation and
Comparative Analysis
This project displays fluid numerical data which
shows combined readings between temperature
sensors DHT11 and DS18B20 and environmental gas
sensory output of MQ-135 throughout time. The
temperature data from sensor accuracy tests appears
through both line graphs that show trends between
points while the gas concentration bar or line graphs
let workers identify critical periods. The analysis of
safety risks depends on visual data and non-compliant
environmental practices with simultaneous benefits
for operational security during industrial operations.
Figure 8(a),8(b) shows the Graphical Representation
of The System Parameters.
Using comparative analysis as a research
examination method allows professionals to study
multiple objects by observing similarities and
differences between them. Different social sciences
and business sectors and technological fields employ
the research method to create useful data that helps
decision-makers make better choices. Social
scientists use nation-to-nation political structures and
educational methods and economic conditions to
develop theoretical explanations of final results. The
process of system comparison allows researchers to
discover top-performing elements of different
approaches which later results in improved current
system strategies and better approaches. Research
through scientific comparison yields connections
between entities that become unobservable when
entities are studied individually.
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Figure 8: Graphical Representation of the System
Parameters.
7 CONCLUSIONS
Through its implementation the Smart Self-Powered
Wireless Sensor Network for Industrial Monitoring
advanced industrial automation practice substantially.
This system allows continuous oversight of
parameters along with efficiency through the
combination of renewable energy sources integrated
to sensor frameworks that process data intelligence.
The combination of operational safety and efficiency
gains occurs because of machine learning integration.
Modern smart industrial developments of sustainable
automated systems gain practical possibilities
through self-powered systems which combine
sustainable tracking frameworks.
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