Internet of Things Devices Management for Smart Cities
Nilson Rodrigues Sousa
1,2
, George P. Pinto
1,2 a
and Cassio V. S. Prazeres
1 b
1
Institute of Computing, Computer Science Department, Federal University of Bahia (UFBA), Salvador, Bahia, Brazil
2
Federal Institute of Bahia, Brazil
Keywords:
Internet of Things, Smart City, Management, Device.
Abstract:
In this work, we address the critical challenge of managing IoT devices within Smart City infrastructures. We
propose a comprehensive solution tailored to the specific requirements of IoT device management, different
from traditional network device management. Our approach integrates hundreds of devices across urban areas,
leveraging telecommunications and information technologies (ICT) to improve urban services and citizens’
quality of life. We reviewed existing architectures and platforms and developed a prototype to demonstrate
the practical application of our solution. Our prototype ensures consistent service availability and efficient
resource management. The insights gained from our work provide valuable guidance for future developments
and implementations of IoT device management strategies in Smart Cities.
1 INTRODUCTION
Smart Cities are urban environments that utilize
telecommunications and information technologies
(ICTs) to provide better public services and more ef-
ficient use of existing resources. According to Janani
et al. (RP et al., 2021), a Smart City combines ad-
vances in technologies such as the Internet of Things
(IoT), big data, social networks, and cloud comput-
ing with the demand for cyber-physical applications
in the public interest, such as health, public safety,
and mobility.
Many cities, such as Seoul (Joo, 2023), Barcelona
(Kadiri et al., 2023), Tokyo (Wolniak and Grebski,
2023) , Singapore (Ang-Tan and Ang, 2022), and
Dubai (Sahib, 2020), adopt ICT-based solutions to
address various urban challenges. Each solution em-
ploys dozens or hundreds of IoT devices and various
software and communication protocols (Jabbar et al.,
2024). In this context, the increasing number of de-
vices employed in various IoT solutions is a trend that
highlights the need for the management of such de-
vices.
According to Ashraf (Ashraf, 2021), numerous
IoT devices are revolutionizing urban environments,
transforming cities into smart cities where every event
becomes part of an interconnected network. A crucial
a
https://orcid.org/0000-0002-6082-9211
b
https://orcid.org/0000-0003-0197-0909
element in constructing these smart cities is the in-
tegration of wireless sensors within the IoT devices.
As highlighted by Gustin and Jasperneite (Gustin and
Jasperneite, 2022), management has become a signif-
icant research area within IoT, mainly due to the con-
nection of a large volume of heterogeneous devices
with limited resources.
In addition to the IoT device amount problem,
such devices can present limitations, such as stor-
age capacity, processing power, energy source, and
communication method (Sehgal et al., 2012; de An-
drade Junior et al., 2014; Mershad and Cheikhrouhou,
2023). Also, the geographical distribution of de-
vices throughout the city represents a significant chal-
lenge and underscores the importance of managing
and maintaining the IoT infrastructure in Smart Cities
(Biswas and Giaffreda, 2014).
However, traditional management solutions strug-
gle to handle these environments due to resource,
communication, device diversity, and scalability limi-
tations, as highlighted by Zhang et al. (Zhang et al.,
2022). On the subject, Zahoor and Mir (Zahoor
and Mir, 2021) emphasize developing lightweight and
straightforward approaches to transparently manage
the entire Smart City infrastructure, maintaining its
operation and performance. As a result, several so-
lutions have been proposed that aim to manage IoT
devices efficiently.
Nevertheless, management approaches often op-
erate independently within Smart City solutions. For
Sousa, N. R., Pinto, G. P. and Prazeres, C. V. S.
Internet of Things Devices Management for Smart Cities.
DOI: 10.5220/0013193700003944
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 10th International Conference on Internet of Things, Big Data and Security (IoTBDS 2025), pages 15-26
ISBN: 978-989-758-750-4; ISSN: 2184-4976
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
15

example, an intelligent parking management ap-
proach may not interact, at the management level,
with an intelligent traffic solution, even though both
are deployed in the same city or even in the same
neighborhood (Pacheco Pinto and Prazeres, 2019).
Given this context, this work presents a proposal for
managing devices that comprise the IoT solution in-
frastructure in the context of Smart Cities.
Our proposal consists of an approach for de-
vice management from two perspectives: first, local,
where management is carried out within the context
of a specific Smart City solution; second, grouped,
where two or more solutions can be managed to-
gether. Consequently, if any problem occurs in the
infrastructure of these solutions, it can be identified,
addressed, and corrected as necessary. In addition to
the developed proposal, we implemented an applica-
tion prototype to demonstrate how management can
generally occur in this context.
The remainder of this article is organized as fol-
lows: Section 2 presents the related works that have
proposals for device management, where a brief com-
parison between them is also made. Section 3 de-
scribes the monitoring and management proposal de-
veloped in this work. Next, Section 4 provides a gen-
eral description of the application environment of the
proposal, presents the organization of the simulated
environment used in this work, and describes the de-
veloped implementation. Finally, future works and
final considerations are presented in Sections 5 and 6,
respectively.
2 RELATED WORKS
Various platforms offer management solutions for
IoT devices to optimize their handling in differ-
ent contexts. In the context of Smart Cities, the
following sections highlight some platforms with
management solutions referenced by Gustin and
Jasperneite (Gustin and Jasperneite, 2022). The plat-
forms KAA, ThingsBoard, Eclipse Kapua, Open-
Balena, and Fiware appear as related works in this
study due to their characteristics, which align with
the proposed solution. These platforms share essen-
tial functionalities for efficient IoT device manage-
ment, such as scalability, interoperability, and real-
time monitoring, which are crucial for Smart City ap-
plications. Additionally, the study compares the dif-
ferent solutions in Section 2.6, including the proposed
approach.
2.1 KAA
Kaa is an open-source platform designed to assist in
developing IoT applications and connected services.
According to Agarwal and Alam (Agarwal and Alam,
2020), the platform offers features for managing IoT
devices, processing data, and integrating with other
systems. In device management, Kaa covers connec-
tion, configuration, and monitoring routines (KaaIoT,
2024). Kaa also provides interoperability with third-
party systems and security features that include au-
thentication, authorization, and encryption. Its modu-
lar and extensible architecture allows the platform to
adapt to the specific needs of projects. He also sup-
ports several communication protocols like MQTT,
CoAP, and HTTP.
2.2 ThingsBoard
According to Okhovat and Bauer (Okhovat and
Bauer, 2021), ThingsBoard is a flexible and scalable
open-source IoT platform. It offers a wide range
of features for IoT networks, such as provisioning
and managing both real and virtual sensors and de-
vices. The platform also supports data collection,
analysis, visualization, and device control. Further-
more, ThingsBoard can work with various IoT com-
munication protocols, including MQTT, CoAP, and
HTTP (ThingsBoard, Inc., 2024). Key functionalities
related to device management in ThingsBoard are de-
scribed in Table 1.
2.3 Fiware
Fiware is an open-source platform designed to cre-
ate intelligent IoT solutions in cities, industries, and
agriculture (Ahle and Hierro, 2022). It provides an in-
frastructure based on modular components that enable
data collection, processing, and management. Third-
party components can be added to the context broker
to enhance compatibility with various solution needs.
Fiware also offers components for IoT device man-
agement, each with specific functionalities aimed at
monitoring and controlling devices. Table 2 describes
some of the Fiware components.
2.4 Eclipse Kapua
Developed by the Eclipse Foundation, the Eclipse Ka-
pua platform, according to Nardis et al. (De Nardis
et al., 2022), is a modular platform focused on inte-
grating IoT devices into an infrastructure. It is scal-
able, and among its features is the management of
IoT devices, which provides an infrastructure adapted
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Table 1: ThingsBoard key functionalities.
Functionality Description
Support for Multiple Protocols ThingsBoard supports protocols like MQTT, CoAP, and HTTP, facili-
tating the integration of various devices
Remote Management Enables the configuration, monitoring, and remote control of IoT de-
vices
Device Provisioning Eases the addition and initial configuration of new devices
Real-Time Monitoring Offers customizable dashboards to visualize the status and performance
of devices
Automation and Rules Features a rule engine to automate actions based on data received from
devices
Table 2: Fiware components.
Component Description
Orion Context Broker The primary component for managing information, it collects, updates, and
distributes data from IoT devices
IoT Agent Interface that allows devices to connect to the Orion Context Broker using
different communication protocols (MQTT, HTTP)
Cygnus Connector used to persist historical data in various storage systems, such as
Hadoop, MySQL, and others
Quantum Leap Connector for storing data to aid in historical data analysis
Wilma PEP Proxy Security provider that manages authentication and authorization for accessing
services within the FIWARE ecosystem
for various operations. Kapua provides such manage-
ment through an open application protocol running
over MQTT, which allows one to control, configure,
start, and stop devices remotely from applications.
These features help in efficient and real-time manage-
ment of the performance of connected devices.
2.5 OpenBalena
OpenBalena is an open-source platform that facili-
tates the management of large-scale IoT devices, ac-
cording to Roda-Sanchez et al. (Roda-Sanchez et al.,
2023), it offers features such as device provisioning,
remote updates, continuous monitoring, application
management via Docker containers, secure commu-
nication, control access, and scalability. These fea-
tures allow for the efficient and secure administration
of many devices, ensuring they are always up-to-date
and operating correctly. However, configuring and
managing the platform can be complex depending on
the solution’s implementation level.
2.6 Comparison Between Works
In Table 3, it is possible to identify the type of pro-
posal for each work with frameworks, architectures,
and platforms presented. We have defined our pro-
posal as a management method.
Table 3 also describes the coverage of manage-
ment proposals. Coverage refers to managing dif-
ferent solutions as a single integrated solution. In
this case, only our proposal covers grouped manage-
ment solutions. The other solutions perform manage-
ment individually. The Managed Components col-
umn shows the types of entities the platforms manage.
It is possible to notice that they all deal with any de-
vice type. The routines column presents the general
functionalities of each platform. Except for Fiware,
the other platforms have the same functionalities. Fi-
nally, in the Protocol column, the different supported
protocols are presented. In this column, our proposal
is to have a manager adapt to the protocols provided
by the solutions infrastructure.
3 SCM: IoT DEVICE
MANAGEMENT
Smart City Management (SCM) is our proposal for
organizing and managing devices in the various IoT
solutions implemented for Smart Cities. The SCM
aims to facilitate the development of managerial solu-
tions in the complex context of smart cities.
The SCM has been structured into four fields,
each addressing an aspect necessary for device man-
agement. Figure 1 presents each field, which must
be well defined according to the management needs
Internet of Things Devices Management for Smart Cities
17

Table 3: Related works comparison.
Work Year Coverage Managed Components Routines Management Routines
KAA (Agarwal and Alam, 2020) 2020
Individual
Solution
Devices and
Gateway
Connection, configuration,
and monitoring routines
MQTT, CoAP,
HTTP, WebSocket
ThingsBoard (Okhovat and Bauer, 2021) 2021
Individual
Solution
Devices, Software
and Gateway
Maintenance of IoT networks,
management of real and
virtual sensors and devices
MQTT, CoAP, HTTP,
SNMP, LoRaWAN
Fiware (Ahle and Hierro, 2022) 2021
Individual
Solution
Devices
and Gateways
Monitoring and device control
MQTT, CoAP,
HTTP, AMQP,
LwM2M, LoRaWAN
Eclipse Kapua (De Nardis et al., 2022) 2022
Individual
Solution
Devices, Softwares
and Gateway
Performs control,
configuration of hardware
and software, startup and
shutdown of devices
MQTT, CoAP,
AMQP, HTTP,
WebSocket, LwM2M
OpenBalena (Roda-Sanchez et al., 2023) 2023
Individual
Solution
Devices, Software
and Gateway
Device maintenance,
remote updates,
continuous monitoring,
software management
MQTT, CoAP, HTTP,
AMQP, WebSocket
Our Proposal 2024
Grouped
Solution
Devices
and Gateways
Registration, monitoring,
remote control, device
maintenance, and management
data administration
The protocol
implemented
by the solutions
of the proposed solution. The definitions made will
guide the development of the management platform.
Figure 1: Fields defined in SCM.
3.1 Requirements
The SCM must cover several requirements to effec-
tively manage IoT devices in a Smart City. These re-
quirements are Heterogeneity, Scalability, Protocols,
Management, and Control.
Heterogeneity concerns the need for management
solutions to enable independent management of all in-
frastructure components. In this sense, each solution
must deal with different types of devices, sensors, ac-
tuators, and forms of communication, among others.
The scalability requirement dictates that a man-
agement solution must be capable of managing any
number of devices within the infrastructure’s forecast
without compromising overall performance. The pro-
tocol requirement states that management solutions
should support traditional IoT device protocols. This
requirement enables management to utilize communi-
cation optimization techniques, such as opportunistic
methods.
The device management requirement states that
one must clearly define the components and char-
acteristics that require management. In the case of
SCM, this entails specifying physical devices, their
configurations, states, and modes of operation. Fi-
nally, the device control requirement specifies that
both remote and non-remote access must be available
to enable maintenance to sustain the existing infras-
tructure’s full functionality.
3.2 Manageable Components
The manageable components refer to the assets that
need to be managed. In the context proposed by SCM,
management covers all the physical components of a
solution. It is worth noting that only hardware charac-
teristics, such as memory, processing power, energy
capacity, and geographic location of the device, are
being considered for management at the moment.
Figure 2 presents an example of an IoT solution
for public lighting in a Smart City. In this example,
we can identify various manageable hardware compo-
nents covered by SCM. This study’s identified com-
ponents of interest include devices, sensors, actuators,
and gateways.
A Device encompasses any hardware component
comprising sensors and actuators. A sensor is a more
straightforward component that can capture environ-
mental data. An actuator represents components that
can effect changes in the environment where they are
installed, such as opening and closing a door. Finally,
a Gateway is a component that facilitates communi-
cation between simple devices with limited commu-
nication capabilities, such as sensors and actuators.
The gateway may also perform certain types of sim-
ple data processing.
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Figure 2: Example of smart street light scenario. Obtained from Taiwan Sourcing Service Provider (Taiwan Sourcing Service
Provider, 2015).
3.3 Organization
A Smart City comprises solutions that cover dif-
ferent areas and meet the different needs of a city,
which means that each solution has its characteris-
tics. Therefore, each SCM-based management so-
lution must meet a specific set of requirements pre-
sented in Section 3.1 and the specific aspects of the
solution that are relevant to management. In this con-
text, each IoT solution will have its implementation
for managing its infrastructure based on SCM. This
management solution, defined as a Smart Manager
(SM), can make the management of different solu-
tions more flexible. In this context, flexibility refers
to the possibility of centralizing or not the manage-
ment of different solutions. To exemplify this situa-
tion, Figure 3 presents three IoT solutions for a Smart
City with their characteristics.
In Figure 3, the three examples of solutions for
Smart City present different characteristics regarding
communication protocols, types of existing devices,
communication methods, connectivity with the cloud,
and version of Smart Manager. These differences
mean that only the Smart Traffic and Smart Parking
solutions, for example, offer flexibility, with a central
controller capable of managing both solutions as if
they were one. On the other hand, the Water Quality
solution lacks a connection to the cloud, and although
it has its version of the Smart Manager, it can only be
managed separately from the others.
Figure 3: Smart City solutions – simplification.
3.4 Management Activities
In the context of this work, management activities en-
compass routines and practices aimed at monitoring,
controlling, and maintaining the efficiency and avail-
ability of devices connected to the infrastructure of
any Smart Solution. We have identified device regis-
tration, data storage management, device monitoring,
and device control as essential activities to achieve
this.
Device registration concerns the ease of register-
ing devices in management databases. Monitoring is
the activity responsible for maintaining supervision
over a device. The data storage management activ-
ity corresponds to executing routines for efficient data
storage management, including registration and mon-
itoring data. Device control refers to routines for con-
Internet of Things Devices Management for Smart Cities
19

trolling device parameters registered and managed by
a Smart Manager. Finally, device maintenance is the
activity that enables correcting problems and carrying
out updates.
4 ENVIRONMENT SIMULATION
AND IMPLEMENTATION
During the development of our management proposal,
we created an initial prototype of a Smart Manager for
managing IoT devices in IoT Smart City solutions (re-
ferred to as Smart Solution from this point forward).
Figure 4 provides an overview of how applications are
organized in the context of a Smart City.
Figure 4: Overview of management in smart cities.
As depicted in Figure 4, we structured the pro-
totype into four layers: physical, middleware, cloud,
and application layers. The quantity of functionalities
in each layer may vary depending on the specific or-
ganization of each solution. However, the presented
organization aligns with the standards in which appli-
cations, in this context, are implemented.
The physical layer encompasses the devices
present in all Smart City solutions, which may be ge-
ographically dispersed depending on the implementa-
tion. These devices exhibit a high degree of hetero-
geneity, possessing distinct characteristics.
The middleware layer facilitates communication
and interaction among diverse devices within a
Smart Solution and between different Smart Solu-
tions. The bundles are software units that include
management routines. These routines may be dis-
tributed across various components (devices, gate-
ways, servers, cloud) within a Smart Solution.
The database in the middleware layer encom-
passes all possible valuable forms of data persistence,
including temporary storage on devices, permanent
storage on local or distributed servers, and cloud stor-
age.
The cloud layer represents the utilization of inter-
net services, which can execute all processing for the
Smart Solution and/or the Smart Manager. An alter-
native is using the cloud only to perform more com-
plex tasks requiring greater processing power.
Lastly, the application layer embodies the essen-
tial software within the domain of Smart Solutions.
This software encompasses the specific solutions, the
Smart Manager, and any third-party software leverag-
ing resources via APIs.
4.1 Environment Simulation
We developed an initial version of a Smart Manager
as a proof of concept. It was used in a simulated en-
vironment, as illustrated in Figure 4. Furthermore,
Figure 5 illustrates the organization of the Smart City
environment along with its Smart Solutions.
Figure 5: Organization of the Simulation Environment.
As we can observe in Figure 5, there are three dif-
ferent layers. Each layer consists of virtual machines
with specific functions in the simulated environment.
The Smart Solutions layer is made up of virtual ma-
chines (VMs) that simulate different solutions within
a Smart City. Each solution comprises virtual sen-
sors, actuators, and gateways that simulate manage-
ment data.
Figure 6 presents an example of simulated man-
agement data representing a gateway in the simulated
solution. For simplicity, only data related to the MAC
address (used as the gateway identifier), IP, manufac-
turer, hostname, operational status, registration date,
associated solution, and location coordinates were
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Figure 6: Gateway data simulation.
used to represent the gateway in the prototype.
Figure 7: Gateway status data simulation.
Figure 7 shows the operational status of the simu-
lated gateway depicted in Figure 6. In the figure, the
”date” key allows the identification of the recorded
status at two different time points. The remaining
data represent battery level, total memory usage, and
processor usage rate (assuming the gateway has these
capabilities).
Just as we simulated the gateway, we also sim-
ulated the devices. Figure 8 presents the data used
to create a simulated device. Each device has seven
attributes: an identifier that distinguishes the device
from others connected to the same gateway; a loca-
tion that may differ from the gateway’s location; the
device type, which refers to its function; the category,
indicating whether it is an actuator or a sensor; the
status, representing the device’s state at a specific mo-
Figure 8: Device data simulation.
ment; and the registration date of the device on the
platform.
The simulation of the device’s operational status
uses data such as those presented in Figure 9. The
device attribute, with details omitted for space con-
siderations, includes the same information as in Fig-
ure 8; however, among the hidden data, the ’status’
remains the only value that may change. The ’date’
indicates when the system recorded the operational
data of the device, while the ’situation’ describes the
device’s mode of operation (whether it is in use, on
standby, in testing, among others).
Figure 9: Device status data simulation.
It is worth noting that the generated data include
only those relevant for executing management rou-
tines. Thus, standard data generated by sensors, such
as temperature, luminosity, and others, are not pro-
duced by this simulation. Additionally, cloud re-
sources were not used in the environment shown in
Figure 5.
4.2 Back-End Implementation of the
Smart Manager
The implementation of the Smart Manager, as shown
in Figure 5, utilizes Apache Karaf, according to (Ed-
strom et al., 2013), is a modular platform built on
Internet of Things Devices Management for Smart Cities
21

OSGi
1
(Open Service Gateway Initiative). It is de-
signed to run applications in projects that require run-
time flexibility and management—a crucial feature
for device management, as managed solutions de-
mand maximum availability. Karaf functions as an
OSGi container, managing modular software pack-
ages (bundles) and enabling distributed applications
to run with support for dynamic updates and configu-
rations without requiring a reboot.
Initially, each virtual machine representing a dis-
tinct Smart Solution runs a Smart Manager instance
(OSGi bundle). In the simulated environment, this
instance communicates with all simulated gateways;
however, in a real environment, each gateway should
have its own Smart Manager instance.
Figure 10: Details on Smart Manager’s Operation on Vir-
tual Machines.
Figure 10 illustrates the organization of Smart
Manager within each smart solution, specifically
within each virtual machine in the simulation. Each
virtual machine hosts a defined number of device and
gateway instances, with each device connected to a
single gateway. The data generator produces both the
management data for the gateways and devices, along
with their respective instances. The virtual machine
sends the management data from the gateway to the
local Smart Manager instance, which then directs the
data to the middleware layer.
A virtual machine representing the middleware
1
https://www.osgi.org/resources/where-to-start/
layer, as shown in Figure 5, runs a distinct in-
stance of Smart Manager. This instance receives data
from Smart Solutions, regardless of the communica-
tion protocol used, processes it, and stores it in the
database. The Smart Manager on the middleware
layer also provides an API for accessing data across
all Smart Solutions and enables sending commands to
devices (a feature not yet implemented in the current
simulation).
4.3 Front-End Implementation of the
Smart Manager
The prototype developed, as presented in the organi-
zation of the simulation environment in Figure 5, also
includes a web application
2
in the layer, responsible
for aggregating and displaying data from all managed
Smart Solutions.
The developed front end consists of a ReactJS ap-
plication that uses the Smart Manager API in the mid-
dleware layer to access device data for each Smart
Solution. It’s important to note that any application
designed to manage devices, regardless of platform,
only needs to connect to the Smart Manager API to
seamlessly access various types of devices within the
Smart Solutions.
Figure 11: Visualization of all solutions in one city.
Figure 11 shows the Smart Manager front-end
home screen. It displays eight different types of Smart
Solutions in operation, each with a specific number of
devices and gateways. It’s important to note the dis-
tinction made here between devices and gateways. A
device includes any hardware (in this case, sensors
and actuators) without direct network and/or internet
connectivity. A gateway, on the other hand, is hard-
ware with network and/or internet connectivity that
also allows devices to connect through it. The total
number of managed devices and gateways is also dis-
2
https://m2dashboard.netlify.app/
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played.
Figure 12 shows the specific gateways of a Smart
Solution, including their names, IPs, and owners. Ac-
cessing these details also provides further gateway in-
formation. Since the gateways enable network con-
nections for devices, each gateway has a set of con-
nected devices. In the developed interface, users can
select a gateway to view all devices connected to it,
including the number of sensors and actuators within
the solution.
Figure 12: List of gateways for a specific solution.
The screen in Figure 13 displays detailed informa-
tion about a selected gateway. It first lists the devices
connected to the gateway, allowing users to identify
the sensors and actuators linked to it and access their
details. There is also a map showing the approximate
location of the gateway under review. Next, a per-
formance graph displays memory usage and battery
level. This graph shows random performance values
since, as mentioned, both the gateways and their data
were simulated. Finally, Figure 13 includes a table
with detailed gateway performance data.
Figure 14 presents the detailed view for a single
sensor or actuator. This screen features three fields
with device information. The first field is a status his-
tory table, where users can see when the device was
active, its location, and the gateway it is connected
to. The second field is a map showing the device’s
current location, as it does not necessarily need to be
in the same place as the gateway it connects through.
The third field, located at the top of the page, iden-
tifies the device name, category (sensor or actuator),
type, and current status.
However, as previously mentioned, our imple-
mentation only covered the general monitoring func-
tions of devices, consolidating their monitoring into
a single interface, the presented front end. This im-
plementation represents one of the possible organiza-
tional approaches for managing Smart Solutions.
5 FUTURE WORKS
This section outlines key areas for expanding the IoT
device management system, focusing on evaluating
user acceptance through the Technology Acceptance
Model (TAM) and identifying broader research op-
portunities.
5.1 Planning for Future
Experimentation
The evaluation of the IoT device management plat-
form will be based on the Technology Acceptance
Model (TAM), as introduced by Davis (Davis, 1989).
This evaluation focuses on the constructs of Perceived
Usefulness (PU) and Perceived Ease of Use (PEOU)
(Davis, 1989). In summary, we aim to assess user
acceptance of the platform by measuring these con-
structs through a structured questionnaire followed by
statistical analysis.
As a result, we will measure the users’ feelings
about the usefulness and ease of use of our IoT man-
agement platform. We also aim to understand how
these perceptions affect their willingness to adopt and
continue using the platform to manage IoT devices in
Smart City environments.
We think that participants in the study should in-
clude professionals who manage IoT systems, such
as city infrastructure managers and technical staff in-
volved in Smart City operations. These individuals
will use the IoT management platform in a controlled
setting, where they will oversee IoT devices through-
out urban areas. To do that, we plan to use a simula-
tion/emulation tool based on the Mininet (Lantz and
O’Connor, 2015)
3
and developed in previous works
(Sousa and Prazeres, 2018; Coutinho et al., 2018;
Batista et al., 2022), where we can deploy our IoT
management platform. Afterward, the users will be
asked to complete a questionnaire designed to assess
their views on how helpful and easy the system is to
use.
The questionnaire will feature a Likert (Likert,
1932) scale (1 = Strongly Disagree, 5 = Strongly
Agree) to capture their responses and will focus on
two key areas: Perceived Usefulness (PU) and Per-
ceived Ease of Use (PEOU). There will also be a sec-
3
https://mininet.org/
Internet of Things Devices Management for Smart Cities
23

Figure 13: Details of a given gateway.
Table 4: Example Questions for Each Category.
Category Example Questions
Perceived Usefulness (PU) - The platform improves my efficiency in managing IoT devices.
- Using this platform enhances the quality of urban service manage-
ment.
- The platform helps me to monitor a large number of distributed de-
vices effectively.
Perceived Ease of Use (PEOU) - The platform is easy to learn and use.
- I can quickly and easily perform my tasks on the platform.
- The system’s interface is intuitive and user-friendly.
Intention to Use - I plan to continue using the platform to manage IoT devices.
- I would recommend this platform to others in the IoT management
field.
Figure 14: Details of a particular device connected to a gate-
way.
tion for general feedback and the participants’ inten-
tion to continue using the platform. The questions
also will evaluate how the system helps improve task
performance and how simple it is for users to oper-
ate. A summary of the survey design is provided in
Table 4.
After the survey, the collected responses will be
analyzed using statistical tools to determine average
PU and PEOU scores. Correlation analysis will ex-
plore how ease of use impacts perceived usefulness
and how both influence users’ willingness to keep us-
ing the system. Regression models will further eval-
uate the effect of these factors on overall user accep-
tance.
This approach ensures a thorough review of the
system’s usability and its real-world benefits. The
findings will offer valuable insights into both the
platform’s strengths and areas for improvement, ul-
timately helping to make it more effective for Smart
City applications. These insights will also guide fu-
IoTBDS 2025 - 10th International Conference on Internet of Things, Big Data and Security
24

ture development and refinements to the IoT manage-
ment system.
5.2 Other Future Works
This work also identifies some possibilities for future
research. First, researchers could expand Smart Man-
ager management routines by conducting more com-
prehensive investigations into potential management
activities within the discussed context. Second, there
is a need to formalize an architecture that describes
the management of existing solutions in Smart Cities.
Achieving this requires research into essential man-
agement aspects, including management routines, de-
vices, and solution operations. Lastly, there is a need
to develop a platform for simulating and generating
IoT devices capable of producing management infor-
mation. This necessity arises because existing simu-
lators focus solely on generating data like temperature
and humidity rather than data derived from the infras-
tructure.
6 FINAL REMARKS
In this study, we investigated device management
across different IoT solutions in the context of Smart
Cities. Throughout the work, we presented the man-
agement aspects of IoT devices and their implications
in the Smart Cities environment.
As a result, we developed a device management
method that covers various existing solutions in a
Smart City, setting it apart from other management
solutions. Additionally, we presented an initial ver-
sion of a device manager, described throughout the
work alongside the method. Also, as a contribution to
this study, we provided insights that can guide other
works in the development and implementation of ef-
fective strategies for device management in the con-
text of Smart Cities.
ACKNOWLEDGMENTS
The authors would like to thank FAPESB, CAPES,
and CNPq organizations for supporting the Graduate
Program in Computer Science at the Federal Univer-
sity of Bahia. This study was financed in part by the
FAPESB INCITE PIE0002/2022 grant.
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