Revista de Ciencias Tecnológicas (RECIT). Volumen 3 (1): 10-22
Revista de Ciencias Tecnológicas (RECIT). Universidad Autónoma de Baja California ISSN 2594-1925
Volumen 5 (4): e234. Octubre-Diciembre. https://doi.org/10.37636/recit.v5n4e234.
ISSN 2594-1925
1
Research article
Energy consumption of an internet of things development
board
Consumo de energía de una placa de desarrollo de internet de las
cosas
Gabriel Lee Álvarez-Rosado1, Kevin Adrián Martínez-Hernández1, Mario Alberto Camarillo-
Ramos1, Verónica Quintero-Rosas1, Arnoldo Díaz-Ramírez1, Roberto López-Avitia2
1Tecnológico Nacional de México / Instituto Tecnológico de Mexicali, Department of Computer Systems.
Av., Tecnológico S/N CP 21376 colonia Elías Calles, Mexicali, Baja California, México.
2Department of Bioengineering, Universidad Autónoma de Baja California, Boulevard Benito Juárez S/N,
Parcela 44, CP 21280, Mexicali, Baja California, México.
Corresponding author: Mario Alberto Camarillo Ramos, TECNM/ Instituto Tecnológico de Mexicali,
Avenida Álvaro Obregón S/N, CP 21100 Colonia Nueva, Mexicali Baja, California, México. E-mail:
mario.camarillo@itmexicali.edu.mx. ORCID: 0000-0003-0700-1885.
Recibido: 30 de Agosto del 2022 Aceptado: 14 de Noviembre del 2022 Publicado: 23 de Noviembre del 2022
Abstract. - Internet of Things is a highly applicable technology due to its versatility in areas such as agronomy, health
applications, and industry. Besides, portability makes these devices affordable. IoT development boards communicate
through Wi-Fi transmitted messages via the Internet, depending on the inner workings of the IoT development board,
energy consumption can vary in such transmission. Furthermore, this consumption could change if the board is powered
by different power supplies and the quantity of Wi-Fi transmitted messages. This paper provides a methodology to
acquire an energy profile when sending data (byte) using Message Queue Telemetry Transport (MQTT) protocol on
DEVKIT V1 NodeMCU-32 (ESP32) development board. Three different power supplies were used for the board, a 3.7
LiPo Battery, 5v usb Power bank and 9V NiMh rechargeable battery. The higher current consumption obtained was
using a 3.7 battery, followed by 5v and the lowest current consumption was when using 9v. However, results demonstrate
that when using the 9v power supply the energy consumption is two times higher than using 3.7v. Therefore, the best
voltage source for transmission and energy consumption using a NodeMCU-32 development board will be 3.7 volts.
Keywords: IoT; Energy consumption; ESP32; Microcontroller.
Resumen. - El Internet de las Cosas es una tecnología de gran aplicación por su versatilidad en áreas como la
agronomía, las aplicaciones sanitarias y la industria. Además, la portabilidad hace que estos dispositivos sean
asequibles. Las placas de desarrollo de IoT se comunican a través de mensajes transmitidos por Wi-Fi a través de
Internet, según el funcionamiento interno de la placa de desarrollo de IoT, el consumo de energía puede variar en dicha
transmisión. Además, este consumo podría cambiar si la placa se alimenta con diferentes fuentes de alimentación y la
cantidad de mensajes transmitidos por Wi-Fi. Este documento proporciona una metodología para adquirir un perfil de
energía al enviar datos (byte) mediante el protocolo de transporte de telemetría de cola de mensajes (MQTT) en la placa
de desarrollo DEVKIT V1 NodeMCU-32 (ESP32). Se utilizaron tres fuentes de alimentación diferentes para la placa,
una batería LiPo 3.7, un banco de energía USB de 5v y una batería recargable NiMh de 9V. El mayor consumo de
corriente obtenido fue al utilizar una batería de 3.7, seguido de 5v y el menor consumo de corriente fue al utilizar 9v.
Sin embargo, los resultados demuestran que cuando se usa la fuente de alimentación de 9v, el consumo de energía es
dos veces mayor que cuando se usa la fuente de alimentación de 3,7v. Por lo tanto, la mejor fuente de voltaje para
transmisión y consumo de energía utilizando una placa de desarrollo NodeMCU-32 será de 3,7 voltios.
Palabras clave: IoT; Consumo de energía; ESP32; Microcontrolador.
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1. Introduction
Internet of things (IOT) is an emerging
technology in which data transmission and
information access among any objects or devices
becomes easier, thus making life easier in many
aspects [1]. This technology has grown
exponentially. According to a report by Cisco,
the number of internet-connected devices will be
more than triple that of the global population. [2]
Such rise in IoT devices will lend itself to a
diversity of applications such as smart energy,
smart building and health monitoring equipment.
IoT is nothing but communication between
devices all over the globe. This essentially
upsurges inter-connectivity and sharing of data
between devices and people. This leads to
various issues which makes the IoT framework
vulnerable in terms of scalability, security,
privacy, energy efficiency, etc. [3]. Although IoT
technologies are in the forefront of research and
development [1], battery technologies are not
evolving as fast to keep up with these emerging
technologies. In addition, security requirements
of IoT devices strains energy consumption,
making optimization a necessity. Unfortunately,
most programmers and developers lack the
knowledge regarding software/energy
dependence, failing on energy-friendly algorithm
development. Researchers are continuously
looking for ways to improve the energy
efficiency of software running on embedded
systems. Energy efficiency has a great impact
on choosing the right MCU for a particular
application [4]. Power management is a topic for
static and mobile systems. In static systems, a
well power management will reduce generated
heat and electricity bills, and for mobile systems
provide longer battery life. Power consumption
of an MCU depends on operating voltage and
current [5]. Different techniques can be
implemented to reduce the power consumption,
particularly in microcontrollers [6-8].
IoT devices have offered improvement for
development in multiple areas, but these benefits
might be limited by hardware and electronic
design. There is a small list of advantages and
disadvantages:
● Advantages:
○ High portability.
○ Low Cost.
● Disadvantages:
○ Limited memory
○ Limited processing
○ Limited energy
Most IoT devices are battery powered, hence
these are battery restricted. Furthermore, these
devices become useless or inoperable until a
battery replacement is performed, which can
represent a high operating cost for the IoT device.
To reduce cost, it is necessary to maximize the
use of energy and reduce battery replacement as
much as possible.
Considering that IoT devices are wireless, there
exists various ways to communicate with another
device (M2M), thus sharing data becomes crucial
and significant about energy consumption for
IoT applications [9, 10], since it is necessary a
certain procedure to send data, which will take
resources from the board, a wireless technology
such as Bluetooth, WIFI, Zigbee, Lora, among
others, must be necessary. However, when using
WIFI, it is possible to standardize the process in
how information is sent using the following
protocols:
1. The MQTT (MQ Telemetry Transport) is
a simple Internet of things
communication protocol. It is based on
passing messages between clients
through the central server. The client can
be of the publisher type, sending their
messages to defined topics (address,
topic). The publisher can be represented
by a sensor or meter [11].
a) CoAP (Constrained Application
Protocol) is a specialized web
transfer protocol for use with
Revista de Ciencias Tecnológicas (RECIT). Volumen 5 (4): e234
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constrained nodes and constrained
networks in the internet of things.
It is generally used for machine-
to-machine communication
(M2M) [12].
b) In addition, HTTP (Hypertext
Transfer Protocol) is an
application protocol for
distributed, collaborative,
hypermedia information systems
that allows users to communicate
data on the World Wide Web [13].
These communication protocols are some of
those used for the development of IoT
applications, also called application layers. A
study by Eclipse in 2021 [14] states that the top
three communication protocols used by
developers are MQTT with 44%, HTTP with
26% and REST with 23%.
When using IoT devices, it is beneficial to know
how much energy is consumed when
transmitting data using a protocol. Then, we will
be able to optimize the number of messages that
are necessary to transmit a byte, increasing the
amount of running battery time that application
requires.
2.- Related work
People have been concerned with analyzing
energy consumption in IoT devices,
implementing a similar approach to the one
shown in this work.
In [9], an analysis of the current used in a
development IoT device is done, making
emphasis on the analysis of current consumption,
using the ESP8266 development board for its
cost, this article analyzes, Device on and off
power task (Wake up, sleep mode), DHT22
sensor data acquisition, as well as the energy
required to establish connection to the internet
and a MQTT server.
They compare MQTT and aMQTT, a small
variant of the protocol, yielding a very similar
current consumption in both variations.
Furthermore, the execution time is less when
using aMQTT variant, thus representing a lower
energy consumption.
In [15], a comparison of the different quality
levels of the MQTT protocol (light QoS) is done
with the ESP32 device, using a multimeter. This
performs an analysis of battery voltage drop with
respect to time. They state that the quality level
Qos1 has a lower consumption compared to other
levels.
In [16] a comparison of quality levels (QoS
levels) has been done with the ESP8266
microcontroller, where a digital multimeter is
used to monitor energy consumption. Results
show that energy consumption with a higher
quality level generates a very high energy
consumption compared to the lowest level.
An ESP8266 development board is used in [17],
which performs the task of using two different
sensors, DTH22 and TSL2561, they observe
battery life with the number of messages per
MQTT sent in a time of one second and a half
second. They conclude that when sending a
message every second, you get approximately
twice the battery life that when sending messages
every 0.5 seconds.
In [18], several protocols used for the IoT
industry are compared. They perform a
simulation between them using the software
WANem [19], based on simulations they obtain
the energy consumption calculated by the
number of packages. They conclude that the
MQTT protocol consumes more energy than the
COaP protocol.
A comparison of different communication
protocols evaluated on the same development
board, COaP, MQTT, HTTP is done in [20] they
performed 25 read cycles for each protocol with
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a shunt resistance of 5.5 Ohm using 7.5 volts as
their power supply. An average of the energy
consumption is acquired and comparison is
made, thus the protocols MQTT and COaP, are
the most suitable for IoT devices.
2.- How MQTT works
MQTT is a lightweight, fast communication
protocol designed for IoT. This allows
communication between different devices using
a Publish/Subscribe model [21, 22]. To do this
you need to use a broker which works as a server.
The IoT device can request data and send it
directly to the broker. Publications are made by
topic, and to read this information you need to
subscribe to the correct topic. Figure 1 shows an
example of how IoT communication and
brokering works.
MQTT has three levels of quality (QoS):
QoS0 (At most once):
● Message not confirmed by
receiver if received
QoS1 (At least once):
● The transmitted message may
arrive one or more times until the
recipient returns a confirmation
response.
QoS2 (Exactly once):
● Each message transmitted is
received only once and is
confirmed by the receiver.
The broker can be created locally, or remotely,
you can observe a few brokers that can be
implemented locally:
● Mosquitto [23]:
• MQTT version 3.11 y 5.0.
• QoS levels, QoS0, QoS1,
Qos2.
● Mosca MQTT [24]:
• MQTT version 3.1 and 3.11.
• Qos levels, QoS0 and QoS1.
● ACTIVEMQ [25]:
• MQTT version 3.1.
• QoS levels, QoS0, QoS1,
Qos2.
3.- Materials and methods
MQTT is analyzed as it is of the most used
protocols by developers, using a broker
mosquitto implemented in windows with the
quality level QoS0.
Many companies produce devices for the
development of applications with IoT. One such
device is the arduino Yun [26], which offers wifi
and ethernet connectivity for IoT applications.
Microchip has several devices [27, 28]. These
include wifi connectivity for IoT application
development.
ESPRESSIF, also develops the ESP32 [29] and
ESP8266 [30] boards, which are ready for IoT
applications.
Figure 1.- MQTT Pub/Sub Architecture
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Table 1. Recommended IoT devices.
Devices
Connectivity
Cost
ESP32
wifi &
bluetooth
18 dlls
ESP8266
wifi
16 dlls
PIC IoT
wifi
95 dlls
Arduino yun
wifi & ethernet
95 dlls
Raspberry pizero
wifi
68 dlls
This article uses a DEVKIT V1 NodeMCU-32
(ESP32) development board. This is a 32-bit
MCU with Wi-Fi and Bluetooth dual mode,
which makes it suitable for Internet of Things
applications.
The development board is used with three power
supplies at different voltages. Table 2 provides
the voltages as well as the type of power supply.
Table 2. Three different power supplies used for
NodeMCU-32.
Voltage
Type
9 v
NiMH rechargeable battery 450
mAh
5 v
USB Power Bank 5000 mAh
3.7 v
Li-Po Battery 500mAh
The code used to evaluate the energy
consumption when sending data using the Wi-Fi
module is the one provided by the manufacturer.
First, basic configuration of the Wi-Fi module is
set to establish network Wi-Fi communication.
Afterwards, the ip address and port are defined.
Then a publication is performed, therefore the
ascii character "A" with the topic "test" is sent,
this for MQTT (QoS0) message requirements.
After sending the information, the algorithm goes
into deep sleep via the WDT (WatchDog Timer)
set to 1 millisecond; After the WDT finishes, it
wakes up the NodeMCU-32 and the process
restarts. The same algorithm is used to evaluate
the energy consumption with every voltage
source. Figure 2 describes the implemented
Algorithm.
Figure 2.- Proposed algorithm to measure power
consumption on MQTT
Current consumption of the Algorithm is
measured using an USB Oscilloscope (Analog
Discovery 2 [31]) and a Current Ranger [32].
The latter allows us to take precise current
measurements, and also avoids the problem of
burden voltage present in most Digital
Multimeters (DMMs), whilst the former is used
to measure the voltage provided by the current
meter.
Figure 3 illustrates the measurement setup used
for the oscilloscope. Time division is set until two
periods are shown on screen (50 ms/div),
resulting in a sample frequency of 5.1613KHz
for Channel 1, taking 8192 samples. The voltage
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range was set to 100mV/div, offset set to 0 V and
the sample mode as Average. It's important to
note that oscilloscope input channels are
equipped with Differential inputs. The current
ranger has a hardware 7kHz RC Low Pass Filter
at the output and will produce a clean smooth
trace on the oscilloscope, very effective for
current MCU measurements.
Figure 3. Measurement setup.
DIAdem [33] (National Instruments) is used to
analyze the acquired signal. A waveform’s
period is integrated to obtain energy (Joules), and
also multiplied by its respective voltage source to
obtain the period’s electric charge (Coulomb). A
close view of the waveform signal in deep sleep
mode just before restarting is shown on Figure 4,
the red signal corresponds to a voltage of
3.7volts, the blue signal is when using 5 volts,
and the green signal is at 9 volts.
Figure 4.- Current waveforms at different supply voltages
in Deep Sleep Mode.
Figure 5, shows one period of the waveform. The
NodeMCU-32 runs the algorithm and starts on
sleep mode, notice that before going to sleep a
higher energy spike can be observed. The signal
has an approximate duration of 300 milliseconds.
Figure 5. Period of data transmission
3. Results and discussions
Tables 3 through 5 show the mean current (in
mA), mean energy (in mJ) and mean charge (in
mC) obtained for every power source applied.
The mean current consumption applying 5v is
4.35% less than using a 3.7v supply, something
similar happened when comparing 3.7v and 9v
sources, the current consumption is 7.86% less
than 3.7v. This current variation may be caused
by the voltage regulator. But evaluating energy
(Joules), when using a 5v source yields 27.98%
more energy than 3.7 battery source. Using a 9v
source, the energy consumption is 119.7% higher
than 3.7v. Furthermore, analysis of electric
charge (Coulombs), shows that electric charge
when using a 9v source will be 9.8% less than
3.7v, and 5v will be 5.8% less than 3.7v.
Table 3 shows the real current consumption,
electric charge consumption and energy
consumption for sending data through the
internet of the NodeMCU-32 with every power
supply. It is important to note that the internal
linear regulator is operating when using 5v and
9v power sources. For 3.7v the regulator is not
active.
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Table 3. Current consumption of the NodeMCU-32
Voltage
Current
Mean
Min
Max
3.7v
57.03 mA
24.28 mA
254.4 mA
5v
54.55 mA
8.4 mA
381.2 mA
9v
52.55 mA
19.89 mA
295.6 mA
Table 4. Coulombs consumption of the NodeMCU-32
Voltage
Electric charge (Coulomb)
Mean
Min
Max
3.7v
7.41 mC
0 mC
17.03 mC
5v
6.99 mC
0 mC
16.20 mC
9v
6.69 mC
0 mC
15.66 mC
Table 5. Joule’s consumption of the NodeMCU-32 using
the algorithm.
Voltage
Energy (Joules)
Mean
Min
Max
3.7v
27.43 mJ
0 mJ
27.43 mJ
5v
34.98 mJ
0 mJ
81.31 mJ
9v
60.28 mJ
0 mJ
140.99 mJ
By finding the electrical constants, we can
estimate the duration of the device in operation,
we can use the following equation:
𝑡 = 𝑏𝑎𝑡𝑡𝑒𝑟𝑦(𝑚𝐴ℎ)/𝑙𝑜𝑎𝑑 (𝑚𝐴) (1)
Equation 1. Estimated time of work
We perform the analysis for each voltage source,
using the same capacity of 500 mAh, we can
observe in table 6 the estimated time in which the
algorithm could be running.
Table 6. Estimated time of execution
Voltage
mAh
Duration
3.7v
500 mAh
8.76 hrs
5v
500 mAh
9.16 hrs
9v
500 mAh
9.51 hrs
Using the electric charge, we can give an
estimate of how many messages are possible to
transmit through MQTT, where we first need to
find the charge in the batteries we are using, as
shown in table 7. We perform the analysis
assuming all power sources with a capacity of
500 mAh.
Table 7. Conversion from mAh to Coulomb
Battery
mAh
Coulomb
Messages
3.7v
500
mAh
1800 c
242.91k
5v
500
mAh
1800 c
257.51k
9v
500
mAh
1800 c
269.05k
Looking at table 7, we can determine that it is
possible to send approximately 242.91k data in
messages with a battery of 3.7v and 500mAh, this
may or may not be enough depending on the
application. Using the other power sources, it's
possible to send 257.5k data messages using a
battery of 5v and 500 mAh, and 269k data
messages using a battery of 9v and 500mAh.
The Developer/Designer has to contemplate the
amount of data to send. This information could
help estimate the time of duration of the
developed application.
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ISSN: 2594-1925
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3. Future work
For further research, we plan to analyze the
NodeMCU-32 when it Wakes Up. During tests,
we notice that some peripherals (input/output)
have been initialized into a logical high state,
which represents an energy consumption issue. If
there is an alternative to optimize energy
consumption without modifying the initial
algorithm, it will be the first step for a proper
energy efficiency analysis. Furthermore, we also
plan to apply the methodology of this paper to
analyze different IoT MCU Boards, to see if there
is a significant difference in energy consumption.
4. Conclusion
The microcontroller NodeMCU-32 suitable for
IoT applications has been tested sending data
through the internet using three different
commercial power supplies, 3.7v, 5v and 9v. The
higher current consumption was using 3.7
battery, followed by the 5v and the lowest current
consumption was when using 9v. More voltage
yields less current consumption; however, this
does not mean less energy consumption, results
demonstrate that when using 9v the energy
consumption is two times higher than using 3.7v.
The best option for energy optimization without
modifying the algorithm for NodeMCU-32 when
sending data will be using a 3.7v Li-po Battery.
5.- Authorship acknowledgements
Gabriel Lee Álvarez Rosado: Analysis of
experiments, algorithm design. Kevin Adrián
Martínez Hernández: Documentation and
database. Mario Alberto Camarillo Ramos:
Project Management, Instrumentation. Verónica
Quintero Rosas: Project Management,
Instrumentation. Arnoldo Díaz Ramírez:
Analysis of results, Documentation. Roberto
López Avitia: Documentation, data analysis.
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10
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Derechos de Autor (c) 2022 Gabriel Lee Álvarez Rosado, Kevin Adrián Martínez Hernández, Mario Alberto Camarillo
Ramos, Verónica Quintero Rosas, Arnoldo Díaz Ramírez, Roberto López Avitia
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