Revista de Ciencias Tecnológicas (RECIT). Volumen 3 (1): 10-22

Revista de Ciencias Tecnológicas (RECIT). Universidad Autónoma de Baja California ISSN 2594-1925

Volumen 7 (3): e350. Julio-Septiembre 2024. https://doi.org/10.37636/recit.v7n3e350

ISSN: 2594-1925

Review

Metanalysis of the development of artificial intelligence and the

internet of things: the transformation of work and life

Metaanálisis del desarrollo de la inteligencia artificial y el internet de los

objetos: la transformación del trabajo y la vida

Manuel Baro Tijerina1, Manuel Román Piña Monárrez2, José Manuel Villegas Izaguirre3, Cinthia Judith

Valdiviezo Castillo1

1Instituto Tecnológico Superior de Nuevo Casas Grandes, Av. Tecnológico 7100, Centro, 31700 Nuevo Casas Grandes,

Chihuahua, México

2Instituto de Ingeniería, Universidad Autónoma de Ciudad Juárez, Cd. Juárez, Chihuahua, México

3Facultad de Ciencias de la Ingeniería y Tecnología, Universidad Autónoma de Baja California, Unidad Valle de las

Palmas, Blvd. Universitario 1000, Unidad Valle de Las Palmas, 22260 Tijuana, Baja California, México

Corresponding author: Manuel Baro Tijerina, Instituto Tecnológico Superior de Nuevo Casas Grandes, Av. Tecnológico 7100, Centro,

31700 Nuevo Casas Grandes, Chihuahua, México. E-mail: mbaro@itsncg.edu.mx; ORCID: 0000-0002-3202-9276.

Received: March 28, 2023 Accepted: June 21, 2024 Published: July 14, 2024

Abstract. - Artificial Intelligence (AI) and the Internet of Things (IoT) are changing the way we live and work by enabling

seamless technology integration in our daily lives. This study explores the literature on the integration of AI and IoT to

create intelligent systems that can autonomously make decisions and perform tasks based on real-time data from

connected devices. This paper presents a meta-analysis of the integration of Artificial Intelligence (AI) and the Internet

of Things (IoT) in decision-making processes, as well as in Industry 4.0 and 5.0. The study analyzed relevant records

from the Web of Science database, evaluating research output, authorship, collaboration, institutional and geographical

distribution, and impact. The results indicate that China has the highest number of total publications and total citations,

followed by the USA and India. The study offers valuable insights into the scientific and technological advancements of

various regions, their level of international collaboration, and their impact on the field of AI-IoT. The trend of

publications indicates that Computer Science, Engineering, and Telecommunications are prominent and steadily

growing fields. However, there has been a recent emergence and increase in Chemistry, Instruments & Instrumentation,

and Material Science, which are contributing to the development of AI-IoT.

Keywords: Metanalysis; Artificial intelligence (AI); Internet of things (IoT); Industry 4.0; Industry 5.0.

Resumen. - La Inteligencia Artificial (IA) y el Internet de las Cosas (IoT) están cambiando nuestra forma de vivir y

trabajar al permitir una integración perfecta de la tecnología en nuestra vida cotidiana. Este estudio explora la literatura

sobre la integración de IA e IoT para crear sistemas inteligentes que puedan tomar decisiones de forma autónoma y

realizar tareas basadas en datos en tiempo real de dispositivos conectados. Este trabajo presenta un meta-análisis de la

integración de la Inteligencia Artificial (IA) y el Internet de las Cosas (IoT) en los procesos de toma de decisiones, así

como en la Industria 4.0 y 5.0. El estudio analizó registros relevantes de la base de datos Web of Science, evaluando la

producción de la investigación, la autoría, la colaboración, la distribución institucional y geográfica, y el impacto. Los

resultados indican que China tiene el mayor número de publicaciones totales y de citas totales, seguida de EE.UU. e

India. El estudio ofrece información valiosa sobre los avances científicos y tecnológicos de varias regiones, su nivel de

colaboración internacional y su impacto en el campo de la IA-IoT. La tendencia de las publicaciones indica que la

Informática, la Ingeniería y las Telecomunicaciones son campos destacados y en constante crecimiento. Sin embargo,

se ha producido una reciente aparición y aumento en Química, Instrumentos e Instrumentación y Ciencia de los

Materiales, que están contribuyendo al desarrollo de la IA-IoT.

Palabras clave: Metaanálisis; Inteligencia artificial (IA); Internet de las cosas (IoT); Industria 4.0; Industria 5.0.

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1. Introduction

The Internet of Things (IoT) refers to a network

of physical items, such as machines, cars,

buildings, and other objects, that are equipped

with sensors, software, and connectivity features

to enable data collection and exchange. The

concept of IoT has been around since the 1980s,

but it gained momentum in the early 2000s due

to the development of low-power sensors and the

widespread use of wireless networks and the

internet [1]. It is expected to grow in the coming

years as more products and devices connect to the

internet. In the early 2000s, there was a notable

surge in interest in the Internet of Things (IoT)

due to technological advancements such as the

creation of low-power and affordable sensors, as

well as the widespread adoption of wireless

networks and the internet. This allowed for a

range of devices and objects to be connected to

the internet [2]. The emergence of the IoT was

largely due to the increasing availability of low-

cost, low-power microcontrollers and wireless

networking technologies, which facilitated the

addition of connectivity to various devices. This

enabled the development of smart appliances and

devices that could communicate with each other

and the internet. Additionally, the increased

access to and adoption of cloud computing

played a significant role in the development of

the IoT. The data generated by IoT devices can

be stored and analyzed, allowing for more

advanced applications such as real-time

monitoring and predictive maintenance [3].

The IoT, however, may potentially have some

disadvantages, such as: 1) Concerns about

privacy and security: As more things and devices

link to the internet, there is a higher chance of

hacking and data breaches. Data and personal

information could be in danger if the right

security measures are not implemented. 2)

Complexity: IoT systems can be complicated

since they require the integration and

management of numerous different technologies

and devices. 3) Dependence on technology:

Because IoT systems can become essential to

operating some systems and processes, they are

susceptible to outages or other issues if the

technology fails. 4) Cost: Setting up and

maintaining IoT systems can be costly, and they

might not offer a good enough return on

investment. 5) Limited standardization: Due to

the sheer variety of IoT-related devices and

technologies, there is currently a lack of

standardization, making it challenging to

guarantee system compatibility and

interoperability. 6) Lack of regulation: IoT is a

very new technology, and there may be security

and privacy concerns because there are no

regulations at present [4]. Overall, even though

IoT has a lot of potential benefits, it's necessary

to think about any negative effects and put the

right security and privacy precautions in place to

reduce the dangers [5].

On the other hand, Radanliev et al. [6] reviewed

literature from 2010 to 2021 on the fourth

industrial revolution, commonly referred to as

Industry 4.0, and the integration of AI and IoT.

They contrasted this with qualitative study

findings that align with the most important

Industry 4.0 frameworks. The authors then

identified existing and emerging methods for

boosting automation in cyber-physical systems.

The essay discusses the impact of integrating AI

and IoT into cyber-physical systems on

cybersecurity requirements. The paper uses

grounded theory methodology to investigate and

model the relationships and interdependencies

between edge components and automation in

cyber-physical systems. It presents a technical

and social analysis of the increasing automation

in cyber-physical systems.

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Other studies have focused on the AI aspects of

IoT. For instance, [7] explores the idea of AI,

including machine learning, computer vision,

fuzzy logic, and natural language processing, in-

depth. The study suggests that combining AI

with IoT could lead to the development of

engaging and significant applications. The

authors discuss the long-standing question of

whether machines can replicate human thought

processes and deceive individuals into believing

they are conversing with a real person. Despite

significant advancements in AI and its

integration with IoT, the authors assert that

further research is required in this field.

According to some researchers, the use of AI in

IoT could lead to the creation of more intelligent

machines and programs that can assist us in our

daily lives. Additionally, integrating various

sectors could create new opportunities, as

demonstrated by related research. Other studies

have focused on the AI aspects of IoT, such as

machine learning, computer vision, fuzzy logic,

and natural language processing, which are

explored in-depth in [8]. The study found that

combining AI with IoT could lead to the creation

of compelling and meaningful applications. The

authors then discussed the long-standing

question of whether machines can replicate

human thought processes and deceive individuals

into believing they are conversing with a real

person. Despite the significant progress made in

integrating AI with IoT, the authors conclude that

further research is necessary in this field. The

literature review in this study shows that previous

research has analyzed various aspects of the IoT,

such as the general concept [9]. However, there

has been a lack of research on the topic of IoT in

conjunction with artificial intelligence. The

current study aims to fill this gap by conducting

a thorough analysis of the growth and recent

trends in AI-IoT [10].

The structure of this paper is as follows, in

section three the methodology is presented;

section four shows the common terms; in section

five presents the results and discussion; section

six shows the areas of the web of science; section

seven shows the conclusions and finally, section

eight provides the references.

2. Methodology

A total of 15,957 records were found, of which

526 were related to 2023 and excluded from

further analysis. The remaining 15,431 records

were then grouped by publication type. Most of

the publications are articles, which make up

57.2% of the total, followed by proceedings

papers at 34.4%. Review and Editorial Material

account for a smaller proportion of the total, at

6.2% and 0.9%, respectively. The remaining

publication types constitute less than 1% of the

total, with the lowest being Retraction, Meeting

Abstract, Correction, and Editorial Material;

Book Chapter and Article; Retracted Publication

and Review; Book Chapter, Book, News Item,

Letter, and Book Review, each representing 0%

of the total number of publications.

3. Common Terms

The following terms are commonly used in

bibliometrics to measure the research output and

impact [11], [12]:

 TP: refers to the number of publications

(such as journal articles, conference papers, book

chapters, and others) in a particular field or on a

particular subject.

 PR%: refers to the proportion of total

publications that a particular subgroup or

category represents relative to the entire

collection of publications.

 Number of authors participating (AU):

represents the number of authors who have

published on a specific subject or field.

 Number of participating institutions

(Inst): indicates the number of institutions with

which the publication's authors are affiliated.

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 Number of participating regions

(Regions): indicates the number of nations to

which the authors of the publications belong.

 Total citations (TC): refers to the number

of times publications in a specific field or on a

specific topic were cited by other publications.

 Total citations per total publication

(TC/TP): indicates the average number of

citations per publication in a specific field or

topic area.

 Total participating authors per total

publication (AU/TP): reflects the average

number of authors per publication in a specific

field or topic.

 H-index (HI): refers to a metric that

assesses the productivity and influence of a

researcher or group of researchers based on the

number of publications and the number of

citations those publications earned.

It is important to note that these terms are widely

used in bibliometrics to evaluate the research

output, authorship, collaboration, institutional

and geographical distribution, and impact.

Moreover, the following terms are frequently

used in bibliometrics to measure authorship and

collaboration patterns:

 Number of publications with the first

author (AU1): indicates the number of

publications that contain the first author. The first

authorship is typically viewed as an indicator of

the author's leading role in the research and its

significance.

 Number of publications where the

corresponding author is listed (AUC). The

corresponding authorship is typically viewed as

a measure of the author's contribution to the

research and its dissemination.

 Number of independent (Indep) and

collaborative (Collab) publications refers to the

number of publications written by a single author

versus multiple authors. Independent

publications are written by a single author,

whereas collaborative publications have multiple

authors.

 The percentage of collaborative

publications relative to the total number of

publications (Collab/TP%) represents the ratio of

collaborative publications to the total number of

publications. This metric can indicate the level of

research field collaboration.

Noting that these terms are widely used in

bibliometrics to measure the authorship pattern,

the authors' leading and corresponding roles, and

the collaboration pattern of research is essential.

All these terms can be applied to any other

research object, including institutions, regions,

keywords, and subject areas. In addition, the

following terms were also employed to analyze

the publication sources and interconnected

objects:

 Impact coefficient: The frequency with

which the "average article" in a journal was cited

during a specific year or period. It is frequently

used as a proxy for a journal's relative importance

within its field.

 Connectivity system: A type of network

graph that illustrates the connections between

various elements (such as articles or authors). It

can be used to analyze the relationships between

various elements, such as the co-citation of

articles and the author collaboration network.

 WOS subject area: WOS is a

bibliographic database that provides access to

scholarly literature in the sciences, social

sciences, arts, and humanities. It utilizes a system

of subject areas to classify articles.

 Author Keywords: Keywords assigned

by the authors of an article that can be used to

indicate the article's primary topics and aid in

finding relevant articles.

4. Results and discussions

The data reveals a consistent rise in the number

of regions involved over time, from one in 2008

to 121 in 2022. This suggests a growing global

interest and activity in the field of AI-IoT. In

2019, the number of involved regions increased

to 102, a 17% rise from the previous year. In

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2020, the number of involved regions decreased

to 99, a 3% drop from the previous year. In 2021,

the number of involved regions increased to 107,

an 8% increase from the previous year. In 2022,

the number of involved regions increased to 121,

a 13% increase from the previous year. These

results indicate that AI-IoT is an active and

productive area of research with strong growth

potential. The consistent growth in the number of

publications, authors, and institutions, as well as

the TC/TP ratio, and the increasing global

interest and activity in the field, indicate that

research in this area is gaining recognition and

impact. The notable increase in the number of

authors and institutions involved in certain years

suggests significant developments or

breakthroughs in the field that have attracted

more researchers and institutions.

In conclusion, Table 1 presents a comprehensive

analysis of the scientific publications and

collaborations of various regions. It is evident

that China has the highest number of publications

and citations, as well as the highest number of

collaborations and independent publications. The

USA and India also have significant numbers in

these categories. Singapore has the highest

TC/TP ratio, indicating that its publications are

cited more frequently on average. On the other

hand, the UAE has the lowest citation ratio,

indicating that its publications are cited less

frequently on average. Pakistan, the UAE, and

Saudi Arabia have the highest percentage of

collaborations in total publications, while India

has the lowest percentage. The data also shows

that China and India have the highest number of

publications with at least one author from their

respective regions and the highest number of

publications with a corresponding author from

their respective regions. China has the highest H-

index, indicating that its researchers are highly

productive and have a significant impact on their

publications. The tables offer valuable insights

into the scientific and technological

advancements of various regions, their

international collaboration, and impact levels.

Table. 1. WOS regions statistics analysis: publications, citations, collaborations, first author, corresponding author, and H-

index. WOS Regions with more than 500 publications were reported.

WOS

Regions

(%)

TC/TP

Collab

Indep

Collab/TP

(%)

AU1

AUC

China

3461

24.3

45434

13.1

1514

1947

43.7

3063

2934

USA

2484

17.5

37276

1229

1255

49.5

1621

1612

India

1989

16736

8.4

772

1217

38.8

1734

1569

South Korea

956

6.7

9696

10.1

407

549

42.6

685

758

Saudi Arabia

832

5.9

7825

9.4

669

163

80.4

375

385

England

811

5.7

13700

16.9

617

194

76.1

371

377

Italy

637

4.5

5611

8.8

292

345

45.8

469

473

Canada

626

4.4

9593

15.3

415

211

66.3

335

327

Australia

625

4.4

12151

19.4

443

182

70.9

339

332

5. Web of Science Research Areas

Figure 1 shows the top twenty subject areas

across four distinct periods. The first period,

from 2008 to 2016, is considered a single period

due to the small number of publications. The

remaining six years are divided into three

additional periods. Computer Science,

Engineering, and Telecommunications are the

dominant subject areas in terms of the number of

publications across all periods. Furthermore, the

number of publications in these categories

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increases over time. Figure 1 shows that the last

stage of research in the field of AI-IoT

experienced a surge in the subject areas of

Chemistry, Instruments & Instrumentation, and

Material Science [13]. Computer Science,

Engineering, and Telecommunications have

remained prominent and stable, while newer

topic areas have recently emerged and gained

interest. This trend highlights the

interdisciplinary nature of AI-IoT research and

the potential for contributions from other

scientific fields in this sector [14].

Figure 1. The time-history of the most productive subject areas related to AI-IoT.

The figure 1 shows a significant triangular

relationship between the subject areas of

Computer Science, Engineering, and

Telecommunications, indicating a high number

of joint publications and a strong correlation.

Furthermore, there is also a moderate triangular

relationship between Engineering, Chemistry,

and Instruments & Instrumentation. In the figure,

Engineering is the largest circle and acts as a hub

between other categories due to its numerous

connections. Energy & Fuel, on the other hand,

only has two connections: one to Science &

Technology - other Topics and one to

Engineering. This suggests a lower degree of

correlation between Energy & Fuel and other

subject areas in the field of AI-IoT.

In their article, Wang et al. [15] proposed an

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adaptive federated learning algorithm for edge

computing systems with limited resources.

Federated learning is a technique used to train

machine learning models on data that is

distributed across multiple edge nodes without

transmitting raw data to a central location. This

technique is advantageous in situations where

centralized data storage is impractical due to

bandwidth, storage space, or privacy concerns.

The authors investigate the convergence bound

of distributed gradient descent in machine

learning models that focus on gradient descent.

They propose a control algorithm based on this

analysis that determines the optimal balance

between local parameter updates and global

parameter aggregation to minimize the loss

function within a given resource budget. The

proposed algorithm is evaluated in a networked

prototype system and a simulated environment.

The results suggest that it performs optimally

with various machine learning models and data

distributions. Future research could focus on

utilizing heterogeneous resources for distributed

learning and studying the convergence of non-

convex loss functions in deep neural networks.

Edge Intelligence (EI) is the combination of AI

with edge computing. Zhou et al. [16] provided a

comprehensive overview of research on EI. The

authors argue that with the proliferation of

mobile computing and the IoT, there is an

increasing demand to bring AI capabilities closer

to the network edge to fully utilize the vast

amounts of data generated at the edge of the

network [17]. Edge computing, which involves

transferring processing activities and services

from the network core to the periphery, is seen as

a potential solution to this issue. The authors

provide an overview of the background and

motivation for AI at the network edge [18]. They

also discuss the underlying architectures,

frameworks, and key technologies for deep

learning models used in training and inference at

the network edge. Additionally, they address

open challenges and future directions for this

field [19].

Tao et al. [20] investigated the importance of big

data in facilitating smart manufacturing, a novel

approach in the manufacturing industry that aims

to enhance competitiveness and efficiency

through data-driven methods. The authors

provide a historical overview of the evolution of

industrial data, from the era of handicrafts to the

current age of big data. Additionally, the authors

present a conceptual framework for data-driven

smart manufacturing. This framework details

common application scenarios and includes a

case study to illustrate its implementation. The

authors acknowledge that data collection

technologies are not yet fully prepared for smart

data perception, especially when dealing with

heterogeneous devices. There are still unresolved

issues with cloud-based data storage and

analytics. However, there is potential for future

work in incorporating key technologies such as

IoT gateways, fog computing, edge computing,

and digital twin technologies into the framework

for data-driven smart manufacturing. These

technologies can expand the manufacturer's data

computation, storage, and networking

capabilities, lower the required bandwidth and

latency time, and enable a high level of cyber-

physical integration. This work is considered a

preliminary examination of data-driven

intelligent manufacturing and its potential

applications.

AI and the IoT are increasingly important in

healthcare delivery as they can assist in the fight

against and prevention of emerging diseases,

such as COVID-19 [21]. In a study conducted by

Vaishya et al. [33], the role of AI in evaluating

and planning for the COVID-19 pandemic was

examined. The study conducted a rapid literature

search using COVID-19 and AI-related

keywords on databases such as Pubmed, Scopus,

and Google Scholar [22]. The study identified

seven significant uses of AI and IoT for the

COVID-19 pandemic. These include using AI to

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detect clusters of illnesses, evaluating historical

data to anticipate the virus's future spread,

tracking the transmission of the infection,

monitoring the health of affected patients, aiding

in the development of a COVID-19 vaccine, and

assisting healthcare organizations in their

decision-making processes [23]. The study

concludes that AI and IoT are useful tools for

detecting early infections, monitoring patient

health, and facilitating virus research.

Additionally, they can assist in creating treatment

protocols, prevention methods, drugs, and

vaccines [24].

The Internet of Things (IoT) has rapidly

expanded, resulting in an explosion of data

collected by sensors and smart devices. As a

result, computer resources have been relocated

from the cloud to the network's edge to manage

the massive amounts of data being generated.

Edge computing is an attractive solution to this

issue since it enables lower latency and more

effective resource utilization [25]. However, the

increasing use of AI and deep learning (DL) has

highlighted the inadequacy of the current cloud

computing service architecture in providing AI

services to individuals and organizations. As a

solution, edge intelligence has emerged, which

focuses on deploying DL services through edge

computing [26].

6. Conclusions

Parameters such as total publications, percentage

of total publications, number of involved authors,

institutions, and regions, total citations, and H-

index were used to evaluate the research output,

authorship, collaboration, institutional and

geographical distribution, and impact. This study

provides an overview of the current state of

research in the field of AI-IoT.

This paper analyzes the scientific productivity

and impact of various regions in the field of AI-

IoT. The data indicates that China has the highest

number of total publications and total citations,

followed by the USA and India. Singapore has

the highest ratio of total citations to total

publications, indicating that its publications are

cited more frequently on average. The UAE has

the lowest ratio, indicating that its publications

are cited less frequently on average.

Additionally, China has the highest number of

collaborations, independent publications, and H-

index, indicating a high level of productivity and

impact in its researchers' publications. Regarding

collaborations, Pakistan, UAE, and Saudi Arabia

have the highest percentage of collaborations in

total publications, while India has the lowest

percentage. The data provides valuable insights

into the scientific and technological

advancements of different regions and their level

of international collaboration and impact, with

percentage data provided.

parameters such as total publications, percentage

of total publications, number of involved authors,

institutions, and regions, total citations, and H-

index were used to evaluate the research output,

authorship, collaboration, institutional and

geographical distribution, and impact. This study

provides an overview of the current state of

research in the field of AI-IoT.

This paper analyzes the scientific productivity

and impact of various regions in the field of AI-

IoT. The data indicates that China has the highest

number of total publications and total citations,

followed by the USA and India. Singapore has

the highest ratio of total citations to total

publications, indicating that its publications are

cited more frequently on average. The UAE has

the lowest ratio, indicating that its publications

are cited less frequently on average.

Additionally, China has the highest number of

collaborations, independent publications, and H-

index, indicating a high level of productivity and

impact in its researchers' publications. Regarding

collaborations, Pakistan, UAE, and Saudi Arabia

have the highest percentage of collaborations in

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total publications, while India has the lowest

percentage. The data provides valuable insights

into the scientific and technological

advancements of different regions and their level

of international collaboration and impact, with

percentage data provided.

The top twenty subject areas across four distinct

periods showed that Computer Science,

Engineering, and Telecommunications dominate

in terms of the number of publications across all

periods, with an increase in the number of

publications as time progresses. The connectivity

network of the top ten subject areas in the field of

AI-IoT showed a strong triangular connection

between Computer Science, Engineering, and

Telecommunications.

Telecommunications are still the most popular

and fastest-growing fields, these newer fields are

getting much attention in recent years. This trend

could mean a shift toward multidisciplinary

approaches, with fields like Chemistry,

Instruments and Instrumentation, and Material

Science all contributing to developing AI-IoT

systems. Furthermore, this trend highlights the

importance of cross-disciplinary collaborations

for advancing this field.

7. Author acknowledgments

Manuel Baro Tijerina: Conceptualización,

metodología, escritura, investigación, edición.

Manuel R. Piña Monarrez: Conceptualización,

metodología, escritura, investigación. Cinthia

José Manuel Villegas Izaguirre: escritura,

edición, supervisión. Judith Valdiviezo

Castillo: Metodología, escritura, edición,

supervisión.

References

[1] P. R. Gunjal, S. R. Jondhale, J.

Lloret, and K. Agrawal, “Things in the

Internet of Things,” in Internet of Things,

Boca Raton: CRC Press, 2024, pp. 61–

115. doi: 10.1201/9781003282945-2.

[2] M. Shoikhedbrod, “Internet of

Things and JavaScript,” Journal of IoT

and Machine Learning, vol. 1, no. 1, pp.

1–8, Feb. 2024, doi:

10.48001/joitml.2024.111-8.

[3] C. Bell, “What Is the Internet of

Things?,” in MicroPython for the Internet

of Things, Berkeley, CA: Apress, 2024,

pp. 1–27. doi: 10.1007/978-1-4842-9861-

9_1.

[4] J. T. Ibibo, “IoT Attacks

Countermeasures: Systematic Review and

Future Research Direction,” 2024, pp. 95–

111. doi: 10.1007/978-3-031-52265-9_7.

[5] M. Ba, L. Dionlar, B. S. Haggar,

and I. DIOP, “A Survey on Security

in Data Transmission in IoT: Layered

Architecture,” 2024, pp. 1–17. doi:

10.1007/978-3-031-51734-1_1.

[6]P. Radanliev, “Artificial intelligence

and quantum cryptography,” J Anal Sci

Technol, vol. 15, no. 1, p. 4, Feb. 2024,

doi: 10.1186/s40543-024-00416-6.

[7]C. G. García, E. R. Núñez-Valdez, V.

García-Díaz, B. Cristina Pelayo G-

Bustelo, and J. M. C. Lovelle, “A Review

of Artificial Intelligence in the Internet of

Things,” International Journal of

Interactive Multimedia and Artificial

Intelligence, vol. 5, no. 4, pp. 9–20, 2019,

doi: 10.9781/ijimai.2018.03.004.

[8]A. Khang and V. H. Abdullayev,

“Computer Vision and AI-integrated IoT

Technologies in Medical Ecosystem”, doi:

10.4018/9781003429609.

[9]M. Eckardt and W. Kerber, “Property

rights theory, bundles of rights on IoT

data, and the EU Data Act,” European

Journal of Law and Economics, Jan. 2024,

doi: 10.1007/s10657-023-09791-8.

10 ISSN: 2594-1925

Revista de Ciencias Tecnológicas (RECIT). Volumen 7 (3): e350.

[10] D. Mohan et al., “Artificial

Intelligence and IoT in Elderly Fall

Prevention: A Review,” IEEE Sens J, vol.

24, no. 4, pp. 4181–4198, Feb. 2024, doi:

10.1109/JSEN.2023.3344605.

[11] B. Basumatary, M. Yuvaraj, and

M. K. Verma, “Scientific communication

of east Asian countries on internet of

things (IoT): A performance evaluation

based on scientometric tools,” Information

Development, p. 026666692211511, Jan.

2023, doi: 10.1177/02666669221151160.

[12] Y. Jia, M. R. Hosseini, B. Zhang,

I. Martek, B. Nikmehr, and J. Wang, “A

scientometric-content analysis of

integration of BIM and IoT,” IOP Conf

Ser Earth Environ Sci, vol. 1101, no. 7, p.

072002, Nov. 2022, doi: 10.1088/1755-

1315/1101/7/072002.

[13] H. Meziane and N. Ouerdi, “A

survey on performance evaluation of

artificial intelligence algorithms for

improving IoT security systems,” Sci Rep,

vol. 13, no. 1, p. 21255, Dec. 2023, doi:

10.1038/s41598-023-46640-9.

[14] Dr. A. P. Ms. Pooja B. Gandhi,

“The Power of Ai In Addressing The

Challenges Faced By Indian Farmers In

The Agriculture Sector: An Analysis,”

Tuijin Jishu/Journal of Propulsion

Technology, vol. 44, no. 4, pp. 4753–4777,

Nov. 2023, doi:

10.52783/tjjpt.v44.i4.1788.

[15] S. Wang et al., “Adaptive

Federated Learning in Resource

Constrained Edge Computing Systems.”

vol. 37, no. 6, pp. 1205-1221, June 2019,

doi: 10.1109/JSAC.2019.2904348.

[16] Z. Zhou, X. Chen, E. Li, L. Zeng,

K. Luo, and J. Zhang, “Edge Intelligence:

Paving the Last Mile of Artificial

Intelligence With Edge Computing,”

Proceedings of the IEEE, vol. 107, no. 8,

pp. 1738–1762, Aug. 2019, doi:

10.1109/JPROC.2019.2918951.

[17] Z. Zhou, D. Niyato, Z. Xiong, X.

Gong, W. Saad, and X. Fu, “Guest

Editorial the Nexus Between Edge

Computing and AI for 6G Networks,”

IEEE Trans Netw Sci Eng, vol. 10, no. 3,

pp. 1186–1189, May 2023, doi:

10.1109/TNSE.2023.3249040.

[18] H. Zhao, Y. Zhu, K. Lu, Q. Li, Z.

Li, and S. Dong, “Edge computing and

hybrid control technology for microgrids

based on activity on edge networks,”

Energy Conversion and Economics, vol.

4, no. 6, pp. 387–400, Dec. 2023, doi:

10.1049/enc2.12103.

[19] Md. M. H. Shuvo, S. K. Islam, J.

Cheng, and B. I. Morshed, “Efficient

Acceleration of Deep Learning Inference

on Resource-Constrained Edge Devices:

A Review,” Proceedings of the IEEE, vol.

111, no. 1, pp. 42–91, Jan. 2023, doi:

10.1109/JPROC.2022.3226481.

[20] F. Tao, Q. Qi, A. Liu, and A.

Kusiak, “Data-driven smart

manufacturing,” J Manuf Syst, vol. 48, pp.

157–169, Jul. 2018, doi:

10.1016/j.jmsy.2018.01.006.

[21] S. Vengusamy and H. A. L.

Rajendran, “Artificial Intelligence (AI) in

Battle Against COVID-19,” in The Role of

AI, IoT and Blockchain in Mitigating the

Impact of COVID-19, BENTHAM

SCIENCE PUBLISHERS, 2023, pp. 1–

25. doi:

10.2174/9789815080650123010003.

[22]A. Iglesias, A. Gálvez, and P. Suárez,

“The Role of Artificial Intelligence and

Machine Learning for the Fight Against

COVID-19,” 2023, pp. 111–128. doi:

10.1007/978-3-031-33183-1_7.

[23]M. A. Muhammad and F. Al-Turjman,

“Application of IoT, AI, and 5G in the

Fight Against the COVID-19 Pandemic,”

2021, pp. 213–234. doi: 10.1007/978-3-

030-60188-1_10.

11 ISSN: 2594-1925

Revista de Ciencias Tecnológicas (RECIT). Volumen 7 (3): e350.

[24]S. Gnanamurthy, S. Raguvaran, B.

Suresh Kumar, C. Santhosh Kumar, “AI-

Powered Healthcare System to Fight the

COVID-19 Pandemic on Federated

Learning,” 2023, pp. 178–202. doi:

10.4018/979-8-3693-1082-3.ch010.

[25]P. R. Gunjal, S. R. Jondhale, J. Lloret,

and K. Agrawal, “Things in the Internet of

Things,” in Internet of Things, Boca

Raton: CRC Press, 2024, pp. 61–115. doi:

10.1201/9781003282945-2.

[26]Z. Li, “Application Scenarios of Edge

Computing in Conjunction with Cloud

Computing and Artificial Intelligence,”

Highlights in Science, Engineering and

Technology, vol. 81, pp. 527–533, Jan.

2024, doi: 10.54097/bky92374.

Cinthia Judith Valdiviezo Castillo

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