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IoT Technology Applications-Based Smart Cities: Research Analysis

Mariana-Daniela González-Zamar
Emilio Abad-Segura
Esteban Vázquez-Cano
4 and
Eloy López-Meneses
Department of Education, University of Almeria, 04120 Almeria, Spain
Higher Technical School of Architecture of Seville, Avenida de la Reina Mercedes, 2, 41012 Seville, Spain
Department of Economics and Business, University of Almeria, 04120 Almeria, Spain
Department of Didactics and School Organization, National University of Distance Education, 28040 Madrid, Spain
Department of Education and Social Psychology, Pablo de Olavide University, 41013 Seville, Spain
Research Institute in Social Sciences and Education, Vice-Rectory for Research and Postgraduate, University of Atacama, Copiapó 1530000, Chile
Authors to whom correspondence should be addressed.
Electronics 2020, 9(8), 1246;
Submission received: 23 July 2020 / Revised: 30 July 2020 / Accepted: 1 August 2020 / Published: 2 August 2020
(This article belongs to the Special Issue Transforming Future Cities: Smart City)


The development of technologies enables the application of the Internet of Things (IoT) in urban environments, creating smart cities. Hence, the optimal management of data generated in the interconnection of electronic sensors in real time improves the quality of life. The objective of this study is to analyze global research on smart cities based on IoT technology applications. For this, bibliometric techniques were applied to 1232 documents on this topic, corresponding to the period 2011–2019, to obtain findings on scientific activity and the main thematic areas. Scientific production has increased annually, so that the last triennium has accumulated 83.23% of the publications. The most outstanding thematic areas were Computer Science and Engineering. Seven lines have been identified in the development of research on smart cities based on IoT applications. In addition, the study has detected seven new future research directions. The growing trend at the global level of scientific production shows the interest in developing aspects of smart cities based on IoT applications. This study contributes to the academic, scientific, and institutional discussion to improve decision making based on the available information.

1. Introduction

In recent years, digitization of society and transformation of business sector have allowed the development of cities based on Internet of Things (IoT) technology, the hyperconnection of electronic devices, and the interpretation of data that these generate [1,2]. Therefore, the purpose of smart cities is to achieve a more sustainable and livable environment that improves the quality of life of citizens based on IoT technological innovation [3].
In a broad sense, IoT consists of digital interconnection of everyday objects, through sensors that capture real-world data that are sent to platforms for processing, and these through service platforms become information and actions [4,5]. The analysis of data allows generating a better understanding of data and making optimal decisions. In this sense, the application of IoT in urban environments allows the development of smart cities, so that one concept is inextricably linked to the other. The application of this technology will drive production models such as Industry 4.0 [6], the development of new consumption and production models from the innovation of the Circular Economy [7], the intelligent management of public and private resources [8,9], the appearance of smart citizens by evaluating their opinions in real time [10], and intelligent mobility from public transport and traffic control. In this order, mobile connectivity is fundamental, and it is fundamentally based on 5G [11,12] technology.
Consequently, the smart city is defined as an urban space that performs more efficient management of its community services through digital technology. Such is the case of intelligent traffic management with the aim of reducing air, light, and noise pollution; improving the safety and health of citizens through Big Data; or developing a smart economy through sensors that help optimize available resources [13,14,15]. Hence, the development of smart and connected cities will depend on effective measures to guarantee the security of communications and data that will be transferred from IoT devices [16,17].
This approach justifies the research interest. Therefore, the motivation of this study is to document the evolution of the global knowledge base on smart cities based on IoT technology applications. Furthermore, the reviewed literature has allowed to focus on this topic, so that the research questions seek to find answers to the following. (1) What is the global structure of knowledge on smart cities based on IoT technology applications? (2) Who are the authors, research institutions, and the most productive countries? (3) What are the thematic axes that this research topic develops? (4) What are the future directions of research?
The main objective of this study is to analyze global research of the smart cities based on IoT technology applications, during the period 2011–2019. To obtain answers to the research questions, a sample of 1232 articles from scientific journals selected from Scopus database was analyzed. This work uses the bibliometric method to synthesize the global knowledge base on smart cities based on IoT technology applications. The results showed the contributions in this research field, so that it has allowed identifying the main driving agents (authors, research institutions, and countries), and current and future research directions.
This study presents a set of limitations, some defined by the research itself. For example, others unrelated to this refer to the difficulty of distinguishing whether the growth in the volume of articles published in the examination period can be related, among other causes, to the exponential development of digital technologies, with regulatory regulation, or with the demands of stakeholders.
Among the research lines developed at the international level, they mainly refer to the analysis of a smart city’ holistic vision; to studying IoT applications; to analyzing network security solutions; to identifying all macroscopic approaches to wireless telecommunication systems; to the implications of Internet in smart cities development; to cloud computing and the availability of data centers; and, finally, to the automation of processes.
Globally, research on smart cities based on IoT technology applications continues to progress, so this study has identified new directions in the research, which should contribute to the development of different aspects.
To achieve the stated objective, this study is organized as follows. Section 2 justifies the relevance of the research topic, delimiting the unit of analysis and conducting a review of the background, theoretical principles, and a conceptual framework of smart cities based on IoT technology applications. Section 3 details the methodology applied in this study. Section 4 shows and discusses the main results obtained. To close, the last section is dedicated to presenting the main conclusions of this research.

2. Scope of Research

Section 2 provides the theoretical principles and provides the definition of the key concepts that will be used in the analysis of the study on smart cities based on IoT technology applications, to avoid different interpretations of these. The purpose of this section is to serve as a guide for research and to offer theoretical and conceptual frameworks for interpreting the results and discussions of the next section.

2.1. Backgrounds

The evolution of smart cities based on IoT technology applications has been shaped by a series of chronological events. In this way, the Internet emerged connecting people through machines or devices. This was the origin of the IoT, with the purpose of moving the network to the objects, connecting them, and exchanging information [18].
In 1999, N. Gershenfeld, from the Massachusetts Institute of Technology (MIT), established the rights of things, in the sense of having identity, accessing other objects, and detecting their surroundings [19,20]. Thereby, in 2005, the International Telecommunication Union (ITU), from the United Nations, presented its report on the IoT, indicating that the next step would be to integrate things into a ubiquitous communication network—that is, anywhere, anytime, by anyone, and with anything. The technologies that made this network possible were radio frequency identification tags, wireless sensors, embedded intelligence, and nanotechnology. Likewise, the IoT user contemplates three levels of experiences: (1) tangible, which involves the introduction into the body of a device that allows data transmission; (2) the connection and sharing of information, and (3) visualization and reflection, which enables the acquisition of a broader knowledge of the environment and the objects we use [21,22,23].
With the development of this technology, IoT has been applied in cities. Thereby, Smart City is the most ambitious representation, whose purpose is to bring together intelligent systems that manage information around the factors that influence the positive evolution of the city, such as the economic, political, social, environmental, public and private mobility, and quality of life [12,15,24].

2.2. Framework

This research study is sustained by a set of theoretical principles, which together with the basic concepts define the framework for global research of the smart cities based on IoT technology applications. Moreover, a set of concepts related to the subject of study have been defined, which introduce part of the concepts that will stand out in the results and discussions due to their importance and connection.
In a first bibliographic search with the purpose of delimiting the research problem and avoiding approaches that are not connected with the study, a set of articles is identified that includes the essential concepts, provides evidence and an initial synthesis on the subject, and allows defining the objective of the research. Table 1 shows the main articles selected after reviewing the literature on the research topic, establishing a framework between the theoretical basis and the terminology of smart cities based on IoT technology applications at an international level. For this reason, the documents in Table 1 focus on the theoretical and conceptual structure of the research topic. This analysis has allowed determining the problem, the purpose, and the objective of the research, as well as obtaining the key terms to apply the methodology specified in Section 3.
The literature review has established the framework of this research field related to, among other aspects, Information and Communication Technologies (ICT), efficient urban planning, educational technologies, transparency between local governments and citizens, technologies applied to the health sector, open data, sustainable urban mobility, efficient waste management, automation and control in smart buildings, air pollution management, crime prevention and criminal activity through smart video surveillance, or sustainable public lighting.
The basis of research on smart cities based on IoT technology applications is confirmed by a set of theoretical principles. Hence, digital transformation consists of the reinvention or evolution of an entity using digital technology to improve performance [41,42]. The Internet and digital technologies, influenced by the speed of growth, transform business models and business sectors, establishing a new digital economy with a social, cultural, and economic impact. Thus, to conduct digital transformation optimally, whether in a company or in a city, the digitization of its strategic processes is key. This contributes to a better understanding of the business, optimizing decision-making, improving efficiency in each area, and increasing competitiveness [43,44].
Likewise, it is necessary to establish the key concepts or variables in the context of this research. First, the Internet refers to the decentralized set of interconnected communication networks, which ensures that the heterogeneous physical networks that comprise it constitute a single logical network with global reach. Its origin comes from ARPAnet (Advanced Research Projects Agency Network), in 1969, when the first connection between Stanford and UCLA computers arose [45,46,47].
Along these lines, the concept of Internet of Things (IoT), a term first coined in 1999 as a key element of digital transformation and the digital economy, refers to digital interconnection of everyday objects with the Internet, thus becoming smart objects. In other words, it consists of connecting the Internet with objects, mainly through sensors that send and receive data continuously, and then after their interpretation, proceed to carry out actions [48,49]. This term signals a radical change in the quality of life of people in society, since it allows new opportunities for access to data, educational services, security, healthcare, communications, and transportation. The unquestionable relevance of IoT is in the substantial changes that they suppose in society, notably in sectors such as Industry 4.0, smart cities, e-health, finance, tourism, education, business, entrepreneurship, or cybersecurity [50,51,52].
In relation to the subject of study, the concept of the city refers to the settlement of population with attributions and political, administrative, economic, and religious functions. This concept is reflected in the specific location of buildings and in its urban configuration. In other words, a city is an urban space with a high population density, with commerce, industry, and services predominating [53,54].
In this sense, a smart city is considered an evolution of city, since it uses technology, innovation, and other resources to promote sustainable development and improve the quality of life of its citizens [55,56]. It includes the concepts of energy efficiency and sustainability, contributing to the balance between the environment and the consumption of natural resources. Likewise, it refers to the city with investments in human and social capital, and in communication infrastructures, which promote sustainable development and quality of life [57,58]. It is also considered as a prototype for urban planning and development, an answer to environmental problems, as well as a solution to energy problems. Among its advantages, the following stand out: (1) a decrease in spending dedicated to the provision and management of public services; (2) offers a platform for innovation; (3) increases the efficiency and quality of services, appropriately managing resources; (4) facilitates the identification of the needs of city; (5) offers real-time information; (6) increases the transparency of the Local Administrations; and (7) promotes social development [59,60,61].

2.3. Related Concepts

To build an underlying conceptual structure on this theme, other concepts have been identified that form the knowledge base on smart cities based on IoT technology applications. Hence, terms such as Sensor, Application, Big Data, Blockchain, and Machine Learning are defined below in the context of this research.
A key concept to understand the implications of smart cities based on IoT technology applications is the sensor. This is a device to detect external actions or stimuli and respond accordingly [62,63]. In other words, they allow the information from the physical environment to be captured, and then the physical or chemical quantities to be measured and transformed into electrical signals so that they can be understood by a microcontroller [64].
In this order, an application consists of a computer program used as a tool that enables a user to perform tasks, and they belong to the application software. In general, a computer application is geared toward automating complicated tasks [65,66]. As a result of technological evolution, consolidated global start-ups and suppliers constantly innovate in new applications to respond to the needs that arise. Thus, the implementation of solutions for more devices with IoT will have an impact on development and security of smart cities [67,68].
Likewise, the concept of Big Data refers to the management and analysis of enormous volumes of data that cannot be processed in a conventional way, by overcoming the limits and capabilities of the software tools commonly used for data capture, management, and processing [69,70]. The purpose of Big Data, in the same way as conventional analytical systems, is to convert the data into suitable information for decision-making. This term encompasses technological infrastructures and services created to provide solutions to the processing of huge structured, unstructured, or semi-structured datasets [71,72].
In this technology, the term Blockchain refers to a shared database for the registration of transactions. Each block has a specific place in the chain, since each contains information from the hash of the previous block. The complete chain is saved in each node of the network that makes up the Blockchain [73,74]. As new records are created, they are first verified and validated by network nodes; then, they are added to a new block that binds to the chain. Blockchain technology allows storing information that can never be lost, modified, or deleted. Each node of the network uses certificates and digital signatures to verify the information and validate the transactions and data stored on Blockchain, which allows ensuring the authenticity of said information [75,76]. Therefore, any type of information that needs to be preserved intact and that must remain available can be stored on the Blockchain in a secure, decentralized, and cheaper way than through intermediaries. Moreover, if this information is kept encrypted, its confidentiality can be guaranteed. Therefore, its use can be applied, among others, to the economy, health, and IoT [77,78,79].
In this context, the Machine Learning concept refers to the scientific discipline in the field of Artificial Intelligence that creates systems that automatically learn [80,81]. The machine that really learns is an algorithm that reviews data and can predict future behavior. It automatically implies that these systems improve autonomously over time, without human intervention. In practice, it is used, for example, to predict urban traffic, make medical pre-diagnoses based on patient symptoms, or detect intrusions in a data communications network [82,83].

3. Materials and Methods

Section 3 shows the methodology applied in this study, and the data collection procedure, based on research questions, that will make up the article sample. Subsequently, they will be processed, analyzed, and interpreted.

3.1. Bibliometric Method

Bibliometrics applies mathematical and statistical methods to scientific literature, to analyze the activity of a certain scientific field. This methodology was started by E. Garfield in the middle of the 20th century, and since then, it has become generalized in the analysis of scientific research and has contributed to reviewing knowledge in multiple disciplines [84,85]. In this way, bibliometrics has evolved from reflection on scientific development and from the availability of numerous databases accessible to the researcher.
It has also become an indispensable tool for managers and specialists in management or in organizations that develop research or innovation programs. Quantitative studies, based on bibliometrics, enrich the understanding and description of the dynamics of activity and scientific production [86,87,88].
In recent years, bibliometric methodology has encouraged the revision of different schools of scientific knowledge. It has been used by numerous scientists, including management, finance, economics, and education [89,90,91]. Bibliometric indicators are the instruments used to measure the results of scientific activity in any of its manifestations [92].

3.2. Search Criteria and Data Collection

The aim of this study is to determinate the general dynamics of the smart cities based on IoT technology applications research at a global level. Hence, a quantitative analysis is performed using the bibliometric method. According to the main literature reviewed on this topic, which is presented in Table 1, the terms chosen in the search string have been “internet of things” and “smart city”.
Mainly, the preference of the Scopus database for the analysis of the document sample is due to the fact that when performing the initial search in the Web of Science (WoS) and Scopus databases, it showed a significant difference in the volume of articles during the period analyzed (2011–2019). That is, from WoS, 264 articles were extracted, while from Scopus, 1232 articles were extracted.
Scopus has a number of advantages over WoS, such as: (1) it is considered the largest deposit of peer-reviewed literature; (2) it minimizes the risk of losing documents during the search; (3) it is easily accessible; (4) it offers tools for data visualization and analysis; (5) it allows the sample to be downloaded in different formats; and (6) it presents a wide variety of data [93,94].
Hence, the procedure followed to select the sample on research in IoT and smart cities is adjusted to the flowchart of Figure 1, in relation to the Preferred Reporting Elements for Systematic Reviews and Meta-Analyses (PRISMA) [95].
In phase 1 (identification), 22,621 records were identified from the Scopus database, considering all the fields for each of the key search terms (internet of things, smart city), all types of documents, and all years in the data range (All years to Present: May 2020).
Next, in phase 2 (screening) the option of “article title, abstract and keywords” was chosen in the field of each term; consequently 17,513 were excluded, so that 5148 records remained.
In phase 3 (eligibility), only the “articles” were selected as the type of document, to guarantee the quality derived from the peer review process. Therefore, 3650 documents were excluded, and 1498 records were obtained.
The time horizon analysis was between 2011 and 2019, and both years were included—that is, from the publication of the first article on this topic (2011) to the last full year (2019). For these reasons, in the last phase (included), 266 documents were excluded, so the final sample included 1232 articles.
The search selected records from the subfields of title, abstract, and keywords, in the period that contains the last 9 years (2011–2019). This procedure has been successfully applied in numerous studies that have used the bibliometric method [96,97].

3.3. Data Processing

In this research study, the indicators of scientific production analyzed have been the distribution by years of the published articles and the productivity of the authors, research institutions, and countries. The quality indicators used and referred to the impact of the different agents of this research topic have been (1) the count of the number of citations; (2) the h-index, which allows to detect which are the most outstanding authors in the discipline, based on the number of citations that have received their scientific articles [98]; and (3) the 2018 CiteScore indicator, which is obtained from the calculation of the number of citations in a year received by academic articles published in a journal in the 3 immediately preceding years, divided by the total number of articles published during those same 3 years [99]; (4) the 2018 SCImago Journal Rank (SJR), which measures the quality of the scientific journals included in Scopus database [100]; and (5) the 2018 Source Normalized Impact per Paper (SNIP), which counts the number of citations received by a journal for three years divided by the potential citation from the journal’s scientific field [101].
Likewise, the indicators of the collaboration structure, which measure the links between the authors, research institutions, and countries, have been analyzed using the processing tools and network maps due to their reliability and suitability in bibliometric analysis, using the co-authorship method. Co-authorship of an article is an official declaration of the participation of two or more authors, organizations, or countries. Thereby, co-authorship analysis is widely used to understand and evaluate patterns of scientific collaboration. For this, in co-authoring networks, nodes represent authors, organizations, or countries that are connected when they share the authorship of an article [102,103].
The analysis of the keywords has allowed the detection of the main current or future research topics, based on the analysis of co-occurrences, since scientific texts can be reduced to the set of joint appearances between the words it comprises [104,105]. The co-occurrence of two concepts is very high if they frequently appear together in one set of documents and rarely do so separately in the rest. With the analysis of co-occurrences, the proximity relationship of two or more terms in a text unit is established. Furthermore, the graphic representation of the co-occurrence networks allows them to be viewed [106,107,108]. For the analysis of these relationship indicators, the software VOSviewer (version 1.6.10, Leiden University, Leiden, The Netherlands) has been applied, which provides data on collaborations and the evaluation of the contents, in order to measure the activities of the research networks [109,110].
The findings gained are valuable for a group of actors involved in scientific research on the evolution and innovation of smart cities based on IoT technology applications and who demand an examination of past and future information, such as engineers, analysts investment, academics, researchers, research institutes, universities, government agencies, materials, and services providers, among others.

4. Results and Discussion

Section 4, first, presents and discusses the main results of the evolution of scientific production in a global context on smart cities based on IoT technology applications. Then, the distribution of articles by subject area and journal is analyzed. Later, the results obtained from the analysis of the main keywords associated with this topic are discussed, which allowed identifying the main current lines. Next, the main keywords and subject areas associated with the most prolific authors, research institutions, and countries are presented. Lastly, future research directions are presented.

4.1. Scientific Production

Section 4.1 displays the evolution of scientific production on smart cities based on IoT technology applications. The interest of the scientific and academic community has increased significantly since 2011, when the first 2 articles on this topic were published, up to 95 in the last year analyzed (2019).
The repercussion of this theme is better understood when it is observed that 95.78% of the total has been published in the last five years (1180 articles), in the last triennium, 83.20% (1025), and in the last year, 40.02% (493).
Figure 2 shows the evolution of the total of the 1232 articles identified in the search carried out in the Scopus database. The polynomial trend line of order 2 indicates that the number of articles in this research topic increases more rapidly over time, in the last 9 years. This trend line, shaped like a parabola, displays a practically perfect goodness of fit to the data, with a coefficient of determination close to 1 (R2 = 0.983). The second-order polynomial model turned out to be the most appropriate for obtaining the growth curve.
The evolution of scientific production in this area of knowledge is part of the result of the fourth Industrial Revolution on a global scale, which is related to computing, transmission, and analysis of data, sensors and low-cost communication devices, and hyperconnectivity enabled by the digital ecosystem [111,112].
Furthermore, IoT transformation by connecting society and the business world has led to the dynamism of industries and their processes, as well as the appearance of new business models, effective health systems, new products and services, and, in particular, smarter cities that are also sustainable [113,114]. This transformation has also influenced research, where a growth in scientific activity is observed at the international level in recent years. In other words, scientific production reflects innovation and the changes that disruptive technologies and connectivity entail. Likewise, cooperation between the main actors that make up the core of scientific activity on smart cities based on IoT technology applications is a key factor in this growth [115,116].
In this research topic, 98.30% of the articles are written in English (1211). This circumstance is related to the fact that the publication in this language broadens its audience, as it happens widely in the searches made in the Scopus database [117]. In addition, the articles have been published in other languages with less representation: Chinese (12, 0.97%), Persian (3, 0.24%), German (2, 0.16%), Polish, Portuguese, and Russian (1, 0.08% each one of them).

4.2. Subject Areas and Journals

This section shows and discusses the main subject areas into which scientific production is classified and the analysis of the main journals on smart cities based on IoT technology applications, during the 2011–2019 period.
Hence, the 1232 articles are classified into 23 subject areas, according to the Scopus database. In this sense, an article could be classified in more than one subject area, or in a single area. There is a correlation between the subject areas and the journals, with the publisher being the journal who ends up cataloguing each article in a thematic area. Figure 3 presents the classification of these 23 main subject areas where articles are classified in worldwide research on smart cities based on IoT technology applications.
Computer Science is the category that collects the most articles (68.10%, 839 articles published), followed by Engineering (51.79%, 638). Next, they are followed by Physics and Astronomy (12.58%, 155), Materials Science (10.31%, 127), Social Sciences (10.31%, 127), Chemistry (9.58%, 118), Biochemistry, Genetics, and Molecular Biology (9.50%, 117), Mathematics (8.44%, 104), Business, Management, and Accounting (5.76%, 71), Energy (5.28%, 65), and Environmental Science (5.03%, 62). The rest of subject areas do not reach 2% each of the published documents.
The phenomenon of the transformation of urban environments into smart cities is the subject of multidisciplinary research. Its analysis is complex, since its evolution is the reflection of numerous disciplines [118]. Although in a subject related from its origin to computer science and engineering, it is also linked by its repercussions with the social sciences, the economy, health, or urban planning [119].
Table 2 displays the main characteristics of the 10 most productive scientific journals on the research topic in the 2011–2019 period: number of articles, number of citations for all articles, number of citations by article, country, subject area, h-index in this research topic, Scopus main quality metrics (CiteScore, SJR and SNIP), and year of the first and last published article.
According the number of articles published and the percentage they represent of the total sample, this ranking is led by Sensors (101, 8.18%), followed by IEEE Access (92, 7.46%). Both are followed by, in order, The IEEE Internet of Things Journal (6.48%) and Future Generation Computer Systems (5.35%). The rest of the journals in this ranking do not exceed 2% of the total. It highlights that 50% of these journals are of European origin (2 Swiss, 2 Dutch and 1 British), while 30% are North American and 20% are Indian.
The variety of the countries of the most outstanding journals is related to a set of socioeconomic factors existing in the context where the scientific activity is carried out, such as: investment for research and development (R&D), gross domestic product (GDP), economically active population (PEA), number of researchers, etc. Other factors such as cultural factors, the influence of educational systems, historical tradition, the scientific policies of governments, and the development of private companies also influence. All this allows certain regions and countries to excel in investments and R&D budgets with their consequent results in scientific advances. In this globalized and increasingly technological world, scientific production, publishers, journals, and readers are distributed heterogeneously throughout the world [10,31,38].
Moreover, The IEEE Internet of Things Journal (80 articles) is the journal with the most citations (4774), and the highest average number of citations per article (3.869), despite the fact that it has been publishing articles on this topic for only 6 years. It is followed by the Dutch Future Generation Computer Systems (2362, 1.914), which published its first article on IoT in smart city research in 2016. These two journals present the highest h-index in the ranking with 25.
The Computer Science and Engineering subject areas are the most outstanding, just as it happens in the total computation (see Figure 4), since 6 journals classify their articles in these. They are followed by Physics and Astronomy and Energy and Social Sciences with 2 journals each. This aspect reveals that the articles on smart cities based on IoT technology applications are classified in a wide range of subject areas, in addition to Computer Science and Engineering.
On the other hand, Table 2 includes for the top 10 journals the main impact metrics of 2018 suggested by Scopus database: CiteScore, SCImago Journal Rank (SJR), and Source Normalized Impact per Paper (SNIP).
Likewise, it is very remarkable, due to the interest generated by research on smart cities based on IoT technology applications in the international scientific community, which are the 10 most productive journals published in 2019.
The North American IEEE Internet of Things Journal (11.33) and IEEE Communications Magazine (11.27) were the journals with the highest CiteScore. The latter, IEEE Communications Magazine, was also the journal with the highest SJR (2.373) and SNIP (4.681).
It also highlights that 3 journals (International Journal of Innovative Technology and Exploring Engineering, International Journal of Recent Technology and Engineering, and International Journal of Advanced Computer Science and Applications) have not been able to calculate the metrics due to their recent incorporation into the study theme.
Besides, the first article was published in 2011, titled “Smart Cities at the Forefront of the Future Internet”, and written by Hernández-Muñoz, J. M., Vercher, J. B., Muñoz, L., Galache, J. A., Presser, M., Hernández Gómez, L. A. and Pettersson, J., in Lecture Notes in Computer Science. It currently has 207 citations [120]. Likewise, the most cited article (2387) was published in 2014, titled “Internet of Things for Smart Cities”, written by Zanella, A.; Bui, N., Castellani, A., Vangelista, L., and Zorzi, M., in IEEE Internet of Things Journal [121].

4.3. Keyword Analysis

Section 4.3 presents a keyword analysis on researching smart cities based on IoT technology applications from 2011 to 2019. From this analysis, the main lines of research carried out globally in this period have been detected.
Thereby, Table 3 lists, according to the Scopus database, the 20 most frequently used keywords in the 1232 articles of the analyzed sample. The most prominent terms are “Internet of Things” (in 901 articles, 73.01%) and “Smart City” (654, 53%). These two keywords were considered in the search query for the Scopus database. Similar terms to the main ones appear in the following positions: Smart Cities (280, 22.69%), Internet of Things (IoT) (269, 21.80%), and IoT (171, 13.86%).
The other keywords in these top 20 are grouped around thematic disciplines such as data intelligence—Big Data (147, 11.91%), Information Management (74, 6.00%) and Data Handling (57, 4.62%); Networks and sensors: Wireless Sensor Networks (111, 9%), Network Security (97, 7.86%), Network Architecture (78, 6.32%), Sensors (55, 4.46%) and Sensor Nodes (54, 4.38%); computing—Internet (126, 10.21%), Automation (105, 8.51%), Cloud Computing (88, 7.13%), Distributed Computer Systems (72, 5.83%); and architecture and urbanism—Intelligent Buildings (95, 7.70%), Energy Efficiency (80, 6.48%), and Energy Utilization (72, 5.83%).
The research theme of this study requires an interdisciplinary and transversal effort. The relatively recent emergence of this research field means that it is studied from different perspectives, both technical and social, that promote the emergence of new terms at an international level associated with this scientific approach [122,123].
The VOSviewer tool provides the data for the link and the total link strength attributes. The first denotes a co-occurrence connection between two keywords, while the second indicates the number of posts in which two keywords appear together. Thus, the “Internet of Things” is the one with more links (732) and more total link strength (6811), followed by “Smart City” (489, 5068). Among the similar terms, the criterion that follows has been to quantify only the one that is present in a greater number of articles, in order to avoid the software grouping them into different clusters.
Figure 4 represents the network map for the keywords of the articles on this research topic, which is based on the co-occurrence method. The color of the nodes is used to distinguish the different clusters based on the number of co-occurrences, while the size varies according to the number of repetitions.
VOSviewer software has identified in seven main lines of research from the different keyword communities on smart cities based on IoT technology applications. The keyword with the largest number of articles within each cluster has allowed us to name and define the research axis and on which the rest of the terms are associated. These are “Smart City”, “Internet of Things”, “Network Security”, “Wireless Telecommunication Systems”, “Internet”, “Cloud Computing”, and “Automation”. For each of the terms, the occurrences attribute is indicated, which denotes the number of documents in which a term appears, and the total strength of the link, which, as previously commented, refers to the number of publications in which two terms appear together.
Cluster 1 (pink color) is led by “Smart City” (occurrences: 655, links: 489, total link strength: 5068) and groups 21.86% of the keywords. Table 4 contains the 20 main keywords associated with this cluster. This first thematic axis studies the holistic vision of the city that applies new technologies to increase the quality of life and accessibility of its citizens, while considering sustainable development. This interconnected system manages transport systems, the efficient use of energy or water resources, socio-economic aspects, security in public spaces, and the commercial fabric, or effective communication [124,125].
Cluster 2 (green color) groups 21.26% of the main terms and is headed by “Internet of Things” (occurrences: 902, link: 493, total link strength: 6811). Table 5 contains the 20 main keywords associated with this cluster. This second thematic axis studies the network of physical objects that uses sensors and application programming interfaces to connect and exchange data over the Internet, together with Big Data management tools, predictive analytics, radio frequency identification, AI and machine learning, or the cloud [126,127].
Cluster 3 (red color) is led by “Network Security” (occurrences: 97, link: 287, total link strength: 962), and it groups 18.83% of the keywords. Table 6 contains the 20 main keywords associated with this cluster. This third research line looks at network security that ensures the integrity, availability, and performance of an organization through the protection of IT assets against cyber threats. Thereby, it is a key component of network optimization, to prevent attacks and increase the productivity of companies [128,129].
Cluster 4 (yellow color) associates 17% of the main keywords and is headed by “Wireless Telecommunication Systems” (occurrences: 40, link: 185, total link strength: 450). Table 7 contains the 20 main keywords associated with this cluster. The fourth thematic axis develops a macroscopic approach to wireless telecommunications systems through specific analyses related to power, data rates, multiple access, cellular engineering, and access network architectures [130,131].
Cluster 5 (purple color) is led by “Internet” (occurrences: 126, link: 273, total link strength: 989), and it groups 8.91% of the keywords. Table 8 contains the 20 main keywords associated with this cluster. The fifth research line has developed contributions on the concept of “Internet” in relation to smart cities based on IoT technology applications, as a decentralized set of interconnected communication networks that use the Transmission Control Protocol/Internet Protocol (TCP/IP), guaranteeing that the heterogeneous physical networks that comprise it constitute a unique logical global network [132,133].
Cluster 6 (cyan color) is led by “Cloud Computing” (occurrences: 88, link: 249, total link strength: 799,), and it groups 8.70% of the keywords. Table 9 contains the 20 main keywords associated with this cluster. The sixth thematic axis develops studies on cloud computing, in relation to the availability upon request of the resources of the computer system, such as data storage and computing capacity, without direct active management by the user. This keyword represents the data centers available from anywhere over the Internet from any mobile or fixed device [134,135].
Finally, cluster 7 (orange color) associates 3.44% of the main terms and is headed by “Automation” (link: 306, total link strength: 1071, occurrences: 105). Table 10 contains the 20 main keywords associated with this cluster. The seventh line of research contributes to developing automation, with reference to the system that allows a machine to carry out certain processes or perform tasks without human intervention, guaranteeing time and cost savings [136,137].
These research lines identified bring together all the concepts related to smart cities based on IoT technology applications global research, during the 2011–2019 period. In other words, these thematic axes include the different approaches analyzed by the different actors (authors, research institutions and countries) that make up this research field.

4.4. Analysis of Authors, Research Institutions, and Countries

Section 4.4 shows the thematic areas in which the articles and the main keywords of the authors, research institutions, and most productive countries are classified. Likewise, their collaboration networks are shown, based on co-authorship analysis.

4.4.1. Authors

Table 11 shows the main characteristics of the 10 most prolific authors in this research topic. The sample of articles has been written by 3744 authors.
Hence, the 10 most productive authors and the research institutions to which they are affiliated were Muñoz, L. (Network Planning and Mobile Communications Laboratory, Universidad de Cantabria, Santander, Spain); Choo, K.K.R. (Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX, USA); Kantarci, B. (School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada); Al-Turjman, F. (Antalya Bilim University, Antakya, Turkey); Park, J.H. (Seoul National University of Science and Technology—SNUST, Seoul, South Korea); Santana, J.R. (Network Planning and Mobile Communications Laboratory, Universidad de Cantabria, Santander, Spain); Barnaghi, P. (UK Dementia Research Institute, Care Research and Technology Centre, London, UK; Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK); Guizani, M. (Department of Computer Science and Engineering, Qatar University, Doha, Qatar); Sotres, P. (Network Planning and Mobile Communications Laboratory, Universidad de Cantabria, Santander, Spain); and Zaslavsky, A. (School of Information Technology, Deakin University, Geelong, Australia).
By territory, 4 authors are of European origin (3 Spanish: Muñoz, Santana, and Stores; and 1 British: Barnaghi); 3 are of Asian origin (Al-Turjman, Park, and Guizani), 2 are of American origin (Choo and Kantarci) and 1 is of Australian origin (Zaslavsky). In this line, by subject area, all the authors of this ranking classify their articles in Computer Science; followed by Engineering with 5 authors; and Mathematics and Energy with 1 author each.
In other words, the main thematic areas (Computer Science and Engineering) associated with the most prolific authors’ articles have been identified. These areas reflect the interests of this scientific field, which has implications both in technology and processes, as well as in innovation and ubiquity, all related to an infrastructure complex with the aim of improving the lives of city dwellers [138,139].
Moreover, among the 10 most productive authors on this topic in the 2011–2019 period, the keywords most used by them, not counting “Internet of Things” or “Smart City”, are mainly linked, in order, to cluster 6 (Blockchain, Network Architecture, Cloud Computing, and Digital Storage); cluster 5 (Internet, Electronic Commerce, and Experimentation); cluster 3 (Waste Management, Data Mining, Network Security, and Waste Disposal); cluster 2 (Energy Utilization, 5G Mobile Communication Systems, Extensive Simulations, and Power Management (telecommunication)); cluster 1 (Data Acquisition, Data Analytics, Semantics, Crowdsensing, and Information and Communication Technologies); and cluster 4 (Testbed).
On the other hand, the top 10 authors of this topic associate their articles, mainly, with research lines that analyze cloud computing, that is, the paradigm that offers computer services through the Internet [39]; and automation, which refers to the application of machines or automatic procedures in carrying out a process or in an industry [44,136].
Figure 5 displays the cooperation map between the authors, based on co-authorship analysis, who have published globally on smart cities based on IoT technology applications. The color of each cluster is related with the group of authors in the publication of articles, while the diameter of the circle indicates the number of articles by the author. The authors in this research topic are associated into 7 groups. In this sense, it is noteworthy that cluster 1, the most numerous, is mostly made up of authors of Chinese origin, and it is in a central position, confirming its potential for research and cooperation among its members. Likewise, component 2 describes the cooperation of the American authors who also confirm their potential researcher at a global level. This cluster is positioned laterally with a certain distance from component 1, which mainly includes authorship of Chinese origin. On the other hand, it is observed that cluster 5, predominantly of Spanish collaboration, is located laterally and is somewhat detached from the rest of the clusters.
Table 12 presents the leading authors by number of articles and the main collaborating authors of each of the 7 clusters formed.
The network of authors denotes the potential, fundamentally, of authors of Chinese, North American, and Spanish origin. This result is confirmed by the development of scientific activity in these countries. In this sense, the participation of public and private entities promote production for the purposes of these programs [140,141].

4.4.2. Research Institutions

The 1232 articles selected in smart cities based on IoT technology applications research have been written in 2680 international affiliations. Table 13 displays the 10 most prolific research institutions in this topic. This ranking highlights that 50% are of European origin (University of Surrey, Universidad de Cantabria, Universidad de Murcia, Alma Mater Studiorum Università di Bologna, and Universitat Politècnica de Catalunya) and 50% are of Asian origin (King Saud University, University of Electronic Science and Technology of China, COMSATS University Islamabad, K L Deemed to be University, and Kyungpook National University). Moreover, all these research institutions classify their published articles into the Computer Science and Engineering categories.
Regarding the subject areas, all the research institutions classify the articles produced in Computer Science and Engineering, just as it happens with all scientific production.
On the other hand, Table 13 also shows the main keywords associated with the articles published by the top 10 institutions in this research field. Among the most outstanding research institutions, the presence of the Vellore Institute of Technology (India) and the Chinese Academy of Sciences (China), which are made up of several organizations, are observed. Even though their contributions do not make a significant difference and occupy positions 7 and 8, respectively, the decision has been made not to include them in this ranking. In this ranking, the search keywords (Internet of Things, Smart City) have been omitted, since they occupied the first two positions in all research institutions. As for the main keywords linked to the top 10 research institutions and that define the thematic axes that they develop, they stand out: cluster 1 (Big Data, Distributed Computer System, Health Care, Information Management, Air Pollution); cluster 2 (5G Mobile Communication System, Data Communication Systems, Energy Efficiency, Simulation, Security, Wireless Sensor Network); cluster 3 (Data Mining, Deep Learning, Internet Protocol); cluster 5 (Electronic Commerce, Energy, Internet); and cluster 7 (Automation, Intelligent Building). In other words, it is observed from the research lines of these authors that the topics developed in their articles reach a wide range of aspects; although it also highlights that the thematic axis related to clusters 4 and 6 are not as well developed among these authors.
The process of digital transformation in the IoT in smart cities has a more collective than individual impact on research. Institutions play a key role in the implementation of projects that promote initiatives around different multidisciplinary objectives. This assumes that scientific activity is not concentrated in a few institutions, but rather that there is a wide variety that affects the research focus, as evidenced by the different key terms of the top 10 institutions [142].
Figure 6 shows the network of research institutions based on the co-authorship analysis. The VOSviewer software tool associates them into 5 groups. The co-authorship analysis of the research institutions infer that a greater number of actors involved in this topic will have an impact on accelerating the adoption of technology and generating a greater scientific impact. Thus, the multidisciplinary approach of this research field is linked to that of the variety of research institutions involved [143].
Table 14 presents the leading research institutions by number of articles and the main collaborating authors of each of the 5 clusters formed.

4.4.3. Countries

In this research topic, the 1232 articles were written in 93 different countries. Table 15 shows the top 10 countries in this research field. The country with the most articles is China (articles: 216, percentage of total: 17.53%), followed by the United States (201, 16.31%), India (195, 15.83%), Spain (137, 11.12%), Italy (108, 8.77%), the United Kingdom (104, 8.44%), South Korea (81, 6.57%), Australia (62, 5.03%), Canada (55, 4.46%), and Pakistan (53, 4.30%).
The articles published by the top 10 countries in the research on IoT technology applications-based smart cities are classified mainly in the same subject areas that make up the majority of the scientific production examined (see Figure 3), that is, Sciences of the Computing and Engineering.
Furthermore, Table 15 also presents the 3 main keywords for the most productive countries in this research topic. The main terms used by the top 10 countries in this thematic area in their articles are associated with six of the identified thematic axes, except for the one that develops the line on “wireless telecommunication systems”. Therefore, each cluster is represented by a set of terms that identify the topics mainly dealt with by these countries during the period 2011–2019. Hence, cluster 1 (Big Data, Information Management); cluster 2 (Energy Utilization, Wireless Sensor Network); cluster 3 (Energy Efficiency, Network Security); cluster 5 (Internet); cluster 6 (Cloud Computing); and cluster 7 (Automation, Intelligent Building).
The multidisciplinary approach of this research topic is related to the variety of countries and continents involved. Thereby, in the same way that it happens with the authors and research institutions, in the countries, as observed in the reviewed literature and in the keywords of the top 10 countries, there is also a multidisciplinary research [144,145].
Figure 7 shows the choropleth map of the countries that contribute to the development of smart cities based on IoT technology applications research. The color range of the blue color has been used to represent the number of articles published on this topic. This map allows visualizing the level of variability of the research at a global level.
Despite the fact that the United States, China, and India, as benchmarks for North America and Asia, bear the weight of research on smart cities based on IoT technology applications globally, the map also shows that Europe, with Spain, Italy, and the United Kingdom, also join this leadership. Australia, on the other hand, is also giving Oceania a voice in this research, and to a lesser extent, both Latin America and Africa are contributing to the more social approach to this topic [146].
Figure 8 shows a collaboration network between the main countries based on the co-authorship analysis. Different colors represent the different clusters formed by the groups of countries, while the diameter of the circle varies depending on the number of articles published by each country. The VOSviewer software has grouped them into 6 components.
Table 16 presents the leading countries by number of articles and the main collaborating countries of each of the 6 clusters formed.
Globally, the co-authorship analysis of the countries indicates that a greater number of participants will have an impact on accelerating research on the adoption of new technologies in smart cities. The centrality of the United States indicates the strength of its research activity and cooperation in its contributions at the international level. Likewise, China stands out in the development of this research field. The association in different clusters adds value to the international sound of this topic and promotes the participation and contributions of any country [147].

4.5. Future Research Directions

Section 4.5 presents the evolution that keywords have followed in the research in smart cities based on IoT technology applications during the period examined. Hence, the pioneering terms associated with this research are identified, which have been incorporated from the increase in published articles. For this reason, Figure 9 shows the evolution and maturity of each keyword community, since it differentiates the period in which they have been analyzed and associated with the articles examined. In this way, it is verified that there has been a progress in terminology in smart cities based on IoT technology applications research.
In this evolution of keywords associated with the research topic, Figure 9 shows that the group of pioneering keywords was incorporated and has allowed the study of smart cities based on IoT technology applications to be formed; this group includes smartphones, web services, augmented reality, network, and cloud computing technologies. In this first stage, the research has been devoted in a transversal way to the analysis and study of technologies that respond to the development and use of artificial intelligence and data analytics, connectivity, security, and well-being [148]. Next, the research focuses on studying the economic, environmental, and social challenges. The analysis of innovations worldwide allows collective participation and analyzes the key issues of Internet regulation and identifies solutions based on experiences in the previous stage [149]. Later, the research focuses on the analysis of smart cities as a process against climate change and the promotion of responsible environmental and health development policies [150].
In this sense, the different subperiods in which the scientific activity of the IoT is being developed in smart cities represent an abundant collection of keywords. This allows checking the variety of study axes in the research activity. Figure 9 visualizes the importance of key terms based on the moment in which they have been associated with this research. Therefore, the oldest have been a reference for the later ones [151,152].
Global research in smart cities based on IoT technology applications continues to advance and evolve. In this way, other concepts are being incorporated that make up new points of view and strategies, which propose new lines of research. The set of the last terms associated with this research has been identified, so that it has allowed them to be associated with new directions in the research. These are related to the development of topics covered and even to the emergence of new approaches. Hence, seven future research directions and various topics associated with these have been identified.
The grouping analysis carried out consisted of decomposing the units of analysis into groups of similar elements and determining the newest terms. The keywords obtained would be assimilable to future thematic lines in this field of research. This procedure constitutes an effective method to discover emerging trends and themes in a scientific discipline. Hence, Table 17 shows the new lines of research identified by the number of links and the total link strength. In addition, a description of each of the future research directions detected is added.
Although the research trends are global, the responses—that is, the materialization of these contributions—are local and varied. This is mainly due to differences in different factors when identifying applications in IoT, such as economic, social, or climatic factors. The progress of the research allows us to recognize various models of smart cities, which are mainly focused on technological aspects, the factor of sustainable development, or digital literacy for a better understanding of digital transformation.
Regarding the initiatives that arise around the development of smart cities based on IoT technology applications, the following stand out. The European Innovation Partnership on Smart Cities and Communities (EIP-SCC), within the European Commission, Regarding the initiatives that arise around the development of smart cities based on applications of IoT technology, the following stand out: The European Association of Innovation on Smart Cities and Communities (EIP-SCC), within the European Commission, was developed in the European Union’s Research and Innovation Funding Program, Horizon 2020 (H2020). This association combines ICT together with energy and transport management, with the aim of providing innovative responses to environmental challenges, Social and Health Sciences of European Cities [153,154]. Additionally, Alliance for Internet of Things Innovation (AIOTI) is another leading initiative of the European Commission, as a space for the interaction of different IoT actors in Europe, such as research centers, universities, and associations [155,156].
Likewise, there are other means that foster interest in these topics, such as: “Smart Cities World” [157], which provides updated information on the infrastructure necessary to create a smart city; “SmartCity.Press” [158], which transmits updated knowledge, progress, and transformation on smart cities; or “IoT World Today” [159], which provides news and case studies on technologies used in the IoT, in different industries, such as smart cities.

5. Conclusions

The aim of this study was to analyze the evolution of scientific production and research trends at a global level, over the last 9 years (2011–2019), on smart cities based on IoT technology applications. To this, a bibliometric analysis of a sample of 1232 articles obtained from the Scopus database has been developed. Fundamentally, the evolution of the number of articles, the subject areas where they are classified, the journals where they are published, the authors, the research institutions, and the most productive countries have been identified. Furthermore, current and future main research lines have been detected.
Scientific production has increased especially in the last triennium (2017–2019), where 1025 articles have been published. These represent 83.20% of the total on the subject in smart cities based on IoT technology applications, which confirms the relevance at the global level and the impact of this research topic. In the same way, the authors, the research institutions, and the most productive countries also link their articles to these two areas of knowledge. In addition, the most prolific countries in this research topic are China, the United States, India, and Spain.
On the other hand, this study has also identified the most influential areas of knowledge where the publications are classified: Computer Science and Engineering. Although it is a multidisciplinary research field, its link with technology and computing is observed.
In relation to the journals in which IoT articles are published in smart cities, Sensors stands out because since 2013, it has contributed to the field of research with the largest number of articles, and in addition, it classifies them in the thematic areas of Engineering, Physics and Astronomy, Biochemistry, Genetics and Molecular Biology, and Chemistry.
The research lines identified that develop the field of study in smart cities based on IoT technology applications generate contributions on the following: (1) the holistic vision of a smart city; (2) IoT applications; (3) network security solutions; (4) the macroscopic approach to wireless telecommunications systems; (5) the implications of Internet in the development of smart cities; (6) cloud computing and the availability of data centers; and (7) the automation of processes.
Globally, the research in smart cities based on IoT technology applications continues to evolve, so this study has identified new directions in research: (1) Energy Storage; (2) Environmental Temperature; (3) Geographic Distribution; (4) Intentional Contaminations; (5) Remote Health Monitoring; (6) End Users, (7) Electronic Crime Countermeasures; (8) Industrial Internet of Things (IIoT); (9) Flood Control; and (10) Social Internet of Things (SIoT).
This study supposes an analysis of the scientific production and the actors that stimulate the smart cities based on IoT technology applications research, in the period 2011–2019, as well as the identification of the research lines and future research directions. Innovation in this research field has been identified based on the groups of authors, research institutions, countries, and keywords, and also the intensity of the relationships that develop in them. The findings obtained are a complement to knowledge in smart cities based on IoT technology applications and allow establishing the relationship between science and technology and favoring the decision-making process. In this way, the individual quality of life of citizens would be benefited, in addition to a collective increase in productivity, since it would be easier for governments to have a better infrastructure at a lower cost to achieve an optimized management of resources.
However, the study has a set of limitations, which have conditioned the results obtained, and these could be considered as the basis for future research articles. Among these limitations, the Scopus database chosen to apply the methodology can be highlighted, as well as the keywords selected to extract the article sample, the study period, the bibliometric methodology used, and even the variables examined. It is also necessary to recognize that using data mining, one could explore large databases and find repetitive patterns that explain the behavior of this data.
Finally, it has been observed that global research in smart cities based on IoT technology applications shows an upward trend, which is derived both from the number of articles and from current and future lines of research. This indicates the interest increasingly accentuated by the academic and scientific community, which is mainly due to the multidisciplinary nature of the subject.

Author Contributions

Conceptualization, methodology, software, formal analysis, resources, data curation and writing—original draft preparation, E.A.-S. and M.-D.G.-Z.; investigation, validation, writing—review and editing, visualization, supervision, project administration, E.A.-S., M.-D.G.-Z., E.L.-M. and E.V.-C.; funding acquisition, E.L.-M. and E.V.-C. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. PRISMA flowchart (Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)).
Figure 1. PRISMA flowchart (Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)).
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Figure 2. Evolution of scientific production (2011–2019).
Figure 2. Evolution of scientific production (2011–2019).
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Figure 3. Top 15 subject areas (2011–2019).
Figure 3. Top 15 subject areas (2011–2019).
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Figure 4. Keywords network based on the co-occurrence method (2011–2019).
Figure 4. Keywords network based on the co-occurrence method (2011–2019).
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Figure 5. Authors network based on co-authorship method (2011–2019).
Figure 5. Authors network based on co-authorship method (2011–2019).
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Figure 6. Research Institutions network based on the co-authorship method (2011–2019).
Figure 6. Research Institutions network based on the co-authorship method (2011–2019).
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Figure 7. Scientific production by countries (2011–2019).
Figure 7. Scientific production by countries (2011–2019).
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Figure 8. Countries network based on the co-authorship method (2011–2019).
Figure 8. Countries network based on the co-authorship method (2011–2019).
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Figure 9. Evolution of keywords network based on co-occurrences (2011–2019).
Figure 9. Evolution of keywords network based on co-occurrences (2011–2019).
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Table 1. Key documents to define the research.
Table 1. Key documents to define the research.
YearArticle Title [Reference]Author(s)Journal
2019A Digital Model for Smart City using Internet of Things (IoT) [25]Alam, K.Global Sci-Tech
2019Structuring Reference Architectures for the Industrial Internet of Things [26]Bader, S.R.; Maleshkova, M.; Lohmann, S.Future Internet
2019Blockchain Powered Smart Cities [27]Dhondse, A.; Singh, S.Communications on Applied Electronics
2019Communication Protocols of an Industrial Internet of Things Environment: A Comparative Study [28]Jaloudi, S.Future Internet
2019IoT Based Smart City with Vehicular Safety Monitoring [29]Mohapatra, H.; Behura, A.Internet of Things and Cloud Computing
2019Blockchain-Supported Smart City Platform for Social Value Co-Creation and Exchange [30]Scekic, O.; Nastic, S.; Dustdar, S.IEEE Internet Computing
2018Redefining the Smart City: Culture, Metabolism, and Governance [31]Allam, Z.; Newman, P.Smart Cities
2018Smart cities and urban data platforms: Designing interfaces for smart governance [32]Barns, S.City, Culture and Society
2018IoT-based reconfigurable smart city architecture [33]Geetha Pratyusha, M.; Misra, Y.; Anil Kumar, M.International Journal of Engineering & Technology
2018Artificial Intelligence-Based Semantic Internet of Things in a User-Centric Smart City [34]Guo, K.; Lu, Y.; Gao, H.; Cao, R.Sensors
2018Development of Information Evaluation System for Smart City Planning Using Geoinformatics Techniques [35]Persai, P.; Katiyar, S.K.Journal of the Indian Society of Remote Sensing
2018A Comprehensive Study on Smart City using Blockchain Technology [36]Raja, A.International Journal of Computer Sciences and Engineering
2018Smart City Development with Urban Transfer Learning [37]Wang, L.; Guo, B.; Yang, Q.Computer
2016Cloud computing security: protecting cloud-based smart city applications [38]Giannakoulias, A.Journal of Smart Cities
2015An Information Framework for Creating a Smart City Through Internet of Things [39]Poslad, S.; Ma, A.; Wang, Z.; Mei, H.Sensors
2014Using a Smart City IoT to Incentivise and Target Shifts in Mobility Behaviour—Is It a Piece of Pie? [40]Jin, J.; Gubbi, J.; Marusic, S.; Palaniswami, M.IEEE Internet of Things Journal
Table 2. Top 10 journals (2011–2019).
Table 2. Top 10 journals (2011–2019).
JournalACC/ACountrySubject Areah *Citescore *SJR *SNIP *1A *LA *
IEEE Access9214081.141USACS-EN-PA204.960.6091.71820152019
IEEE Internet of Things Journal8047743.869USACS2511.331.3963.87420142019
Future Generation Computer Systems6623621.914NetherlandsCS256.300.8352.46420162019
IEEE Communications Magazine248430.683USACS-EN1511.272.3734.68120132019
International Journal of Innovative Technology and Exploring Engineering2300.000IndiaCS-EN1NANANA20192019
Sustainable Cities and Society172830.229NetherlandsEY-EN-SS95.221.1001.74520172019
International Journal of Recent Technology and Engineering1630.002IndiaBMA-EN1NANANA20182019
International Journal of Advanced Computer Science and Applications1390.007UKCS2NANANA20182019
A: number of articles; C: total citations; C/A: average number of citations per article; BGM: Biochemistry, Genetics, and Molecular Biology; CH: Chemistry; EN: Engineering; PA: Physics and Astronomy; CS: Computer Science; EY: Energy; SS: Social Sciences; BMA: Business, Management and Accounting; ES: Environmental Science; h: h-index or Hirsch index; CiteScore: CiteScore metric (2018); SJR: Scimago Journal Rank (2018); SNIP: Source Normalized Impact per Paper (2018); 1A: first article; LA: last article; NA: not available; (*) in this research topic.
Table 3. Top 20 keywords (2011–2019).
Table 3. Top 20 keywords (2011–2019).
RankKeywordArticles%LinksTotal Link StrengthCluster (See Figure 4)
1Internet of Things90173.13%49368112
2Smart City65453.08%48950681
3Big Data14711.93%33513551
5Wireless Sensor Networks1119.01%30410592
7Network Security977.87%2879623
8Intelligent Buildings957.71%2809797
9Cloud Computing887.14%2497996
10Energy Efficiency806.49%2628412
11Network Architecture786.33%2598316
12Information Management746.01%2667801
13Distributed Computer Systems725.84%2437291
14Energy Utilization725.84%2466962
15Data Handling574.63%2145901
17Fog Computing534.30%1765016
18Digital Storage514.14%1914856
19Decision Making504.06%1994761
%: percentage of total articles published.
Table 4. Cluster 1: Top 20 keywords (2011–2019).
Table 4. Cluster 1: Top 20 keywords (2011–2019).
1Big Data147335135511Semantics32122260
2Information Management7426678012Data Acquisition30150315
3Distributed Computer Systems7224372913Data Analytics28138292
4Data Handling5721459014Middleware28124227
5Sensors5519445615Environmental Monitoring27120221
6Decision Making5019947616Ubiquitous Computing26126235
8Data Privacy3717741818Proposed Architectures23134288
9Cryptography3514834519Application Programs21125204
10Information and Communication Technologies3217033520Smartphones20119192
R: Rank position; O: Occurrences; L: Links; TLS: Total Link Strength.
Table 5. Cluster 2: Top 20 keywords (2011–2019).
Table 5. Cluster 2: Top 20 keywords (2011–2019).
1Wireless Sensor Networks111304105911Intelligent Transportation Systems29136310
2Energy Efficiency8126284112Energy Conservation26124250
3Energy Utilization7224669613Complex Networks25141239
4Security4916239114Mobile Telecommunication Systems23121210
5Intelligent Systems4317843715Scheduling22118212
6Authentication3511331716Wireless Sensor Network21120205
7Optimization3314726817Gateways (Computer Networks)2099205
8Vehicles3215434318Wireless Sensor Network (WSNS)1875161
95G Mobile Communication Systems3014126519Energy Harvesting1779140
10Intelligent Transportation Systems2913631020Traffic Congestion17108178
R: Rank position; O: Occurrences; L: Links; TLS: Total Link Strength.
Table 6. Cluster 3: Top 20 keywords (2011–2019).
Table 6. Cluster 3: Top 20 keywords (2011–2019).
1Machine Learning4017638511Transportation20117216
2Data Mining3914333112Waste Management2084168
3Artificial Intelligence3316431713Air Quality1883157
4Learning Systems3317735614Architecture1784144
5Sensor Networks3013224115Neural Networks1682153
6Deep Learning2813126616Sensors and Actuators1689144
7Real Time Systems2615429317Security of Data1575128
8Forecasting219017918Urban Transportation1468123
9Learning Algorithms2011221319Economic and Social Effects1389122
10Security Systems2010018920Interactive Computer Systems1390151
R: Rank position; O: Occurrences; L: Links; TLS: Total Link Strength.
Table 7. Cluster 4. Top 20 keywords (2011–2019).
Table 7. Cluster 4. Top 20 keywords (2011–2019).
1Internet Protocols3416035712Long Range Technology (LORA)1762130
2Embedded Systems2915330613Network Layers17110185
3Radio Frequency Identification (RFID)2712522814Cost Effectiveness16100166
4Low Power Electronics2513227915IoT Architectures1581132
5Smart Power Grids2312926116Zigbee1578120
6Communication Technologies2212321117Wide Area Networks1490181
7Electric Power Transmission Networks2213826318LoRaWAN1365120
8Smart Grid2212623319Standards1377114
9Wireless Communications1912020920Wi-Fi1393146
10Radio Frequency Identification (RFID)1810320920Message Queue Telemetry Transport (MQTT)123149
R: Rank position; O: Occurrences; L: Links; TLS: Total Link Strength.
Table 8. Cluster 5: Top 20 keywords (2011–2019).
Table 8. Cluster 5: Top 20 keywords (2011–2019).
1Interoperability3814530111Smart Environment1368104
2IoT Applications3110721512Information and Communication Technology123570
3Sustainable Development2913126413Ecology1168113
4Urban Growth2713127214Mobility113863
5Semantic Web239919515Quality of Life115785
6Urban Planning229318416Energy1062101
7Electronic Commerce217615217Technological Development93354
9Population Statistics189917319Building Blocks83347
R: Rank position; O: Occurrences; L: Links; TLS: Total Link Strength.
Table 9. Cluster 6. Top 20 keywords (2011–2019).
Table 9. Cluster 6. Top 20 keywords (2011–2019).
1Network Architecture7825983111Resource Management1588159
2Fog Computing5317650112Telecommunication Services1490138
3Digital Storage5119148513Cloud135387
4Quality of Service4721846414Software Defined Networking13100162
5Edge Computing4417944515Web Services125695
6Fog4015341916Augmented Reality116390
7Computer Architecture3717744517Heterogeneous Devices1172107
8Blockchain2610119418Energy Consumption1073104
9Green Computing2515730619Industry 4.0103849
10Information Services2314026320Mobile Devices105983
R: Rank position; O: Occurrences; L: Links; TLS: Total Link Strength.
Table 10. Cluster 7: Top 20 keywords (2011–2019).
Table 10. Cluster 7: Top 20 keywords (2011–2019).
1Intelligent Buildings9528097911Lighting64355
2Smart Homes2610625012Wireless Sensor Networks (WSN)63044
3Energy Management179017813Building Management System53756
4Energy Management Systems11549614Energy Optimization54054
5Buildings106411915Light-Emitting Diodes53241
6Future Internet10487316Security Requirements62949
7Smart Building8478617Service-Oriented Architecture (SOA)64265
8Commerce7486318Control Systems52838
9Architectural Design6293619Realtime Processing55060
10Cameras6334320Electronic Data Interchange55059
R: Rank position; O: Occurrences; L: Links; TLS: Total Link Strength.
Table 11. Top 10 authors and main keywords (2011–2019).
Table 11. Top 10 authors and main keywords (2011–2019).
AuthorInstitutionACountrySACC/Ah *1A *LA *Keyword 1Keyword 2Keyword 3
Muñoz, L.Universidad de Cantabria10SpainCS-E61862820112019InternetElectronic CommerceExperimentation
Choo, K.K.R.University of Texas at San Antonio9USACS16919620162019Cloud computingDigital StorageBlockchain
Kantarci, B.University of Ottawa9CanadaCS-MA15517720162019Mobile CrowdsensingData AcquisitionCrowdsensing
Al-Turjman, F.Antalya Bilim University8TurkeyCS-E11114620182019Energy UtilizationExtensive SimulationsPower Management (telecommunication)
Park, J.H.Seoul National University of Science and Technology8South KoreaCS-EY16320520172019Network ArchitectureBlockchainNetwork Security
Santana, J.R.Universidad de Cantabria8SpainE-CS38348620122019InternetTestbedElectronic Commerce
Barnaghi, P.UK Dementia Research Institute7UKCS-E14821620152019Data MiningSemanticsData Analytics
Guizani, M.Qatar University7QatarCS-E99146201720195G Mobile Communication SystemsBlockchainDevice-to-Device Communications
Sotres, P.Universidad de Cantabria7SpainCS-E37854520122019Network ArchitectureElectronic CommerceInformation and Communication Technologies
Zaslavsky, A.Deakin University7AustraliaCS-E58383520142019Waste ManagementInternetWaste Disposal
A: number of articles; SA: subject areas; CS: Computer Science; EN: Engineering; MA: Mathematics; EY: Energy; C: total citations; C/A: average number of citations per article; h: h-index or Hirsch index; 1A: first article; LA: last article; (*) in this research topic.
Table 12. Clusters of authors (2011–2019).
Table 12. Clusters of authors (2011–2019).
ClusterCluster Color (See in Figure 5)%AuthorsArticlesLinksTLSCitations
1Pink54.92%Liu Y. (*)114953189
Zhang Y.114146139
Liu X.103640147
Wang X.8292988
Zhou Y.72730119
2Green15.89%Muñoz L. (*)103157681
Skarmeta A.F.92534235
Santana J.R.84268420
Sotres P.72549412
Barnaghi P.72336164
3Red15.66%Choo K.-K.R. (*)93336242
Kumar N.72129165
Guizani M.72526164
Paul A.61122485
Rathore M.M.61524485
4Yellow5.90%Zaslavsky A. (*)72531627
Perera C.41515861
Palaniswami M.41418636
Vasilakos A.V.41919296
Anagnostopoulos T.41621116
5Violet4.09%Alonso-Záate J. (*)32525100
Akkaya K.38823
Casellas R.2303276
Verikoukis C.2303276
Vilalta R.2303276
6Cyan1.97%Fortino G. (*)51623405
Russo W.3714100
Savaglio C.3714100
Pianini D.381478
Viroli M.381478
7Orange1.5%Dustdar S. (*)5131995
Vögler M.4111588
Inzinger C.34976
Schleicher J.M.34976
Truong H.-L.2131322
%: percentage of total authors; (*) leading author; TLS: Total Link Strength.
Table 13. Top 10 research institutions and main keywords (2011–2019).
Table 13. Top 10 research institutions and main keywords (2011–2019).
Research InstitutionACountrySubject Areah *1A *LA *Keyword 1Keyword 2Keyword 3
King Saud University20Saudi ArabiaCS-EN1120132019Big DataDeep LearningHealth Care
University of Surrey18UKCS-EN920122019Data MiningInformation ManagementInternet
University of Electronic Science and Technology of China17ChinaCS-EN8201620195G Mobile Communication SystemCryptographyData Communication System
Universidad de Cantabria17SpainCS-EN1020112019InternetElectronic CommerceCo-creation
Universidad de Murcia16SpainCS-EN1020132019InternetInternet ProtocolEnergy Efficiency
Alma Mater Studiorum Università di Bologna15ItalyCS-EN920132019Aggregate ComputingDistributed Computer SystemSimulation
COMSATS University Islamabad15PakistanCS-EN820172019AutomationIntelligent BuildingEnergy Efficiency
Universitat Politècnica de Catalunya13SpainEN-CS820152019AutomationDistributed Computer SystemEnergy
K L Deemed to be University13IndiaEN-CS220162019SecurityWireless Sensor NetworkAir Pollution
Kyungpook National University12South KoreaCS-EN820162019Big DataAutomationIntelligent Building
A: number of articles; EN: Engineering; CS: Computer Science; h: h-index or Hirsch index; 1A: first article; LA: last article; (*) in this research topic.
Table 14. Clusters of authors (2011–2019).
Table 14. Clusters of authors (2011–2019).
Cluster NumberCluster Color (See in Figure 6)%Research InstitutionRegion, CountryALC
1Pink25%Department of Embedded Systems Engineering (Incheon National University)Incheon, South Korea2541
Department of Computer Science (Bahria University)Islamabad, Pakistan142
Department of Computer Science (National Textile UniversityFaisalabad, Pakistan142
Department of Computer Science, University of Engineering and TechnologyTaxila, Pakistan142
School of Electronic Engineering (Xidian University)Shaanxi, China142
2Green25%School of Computer Science and Engineering, Kyungpook National UniversityDaegu, South Korea715120
Department of Computer Science and Information Technology, University of AjkCachemira Azad, Pakistan1238
Department of Computer Science, S.S.D. Women’s Institute of TechnologyBathinda (Punjab), India1114
School of Architecture, Civil, Environmental and Energy Engineering, Kyungpook National UniversityDaegu, South Korea1242
3Red20%Department of Computer Science, University of PeshawarPeshawar, Pakistan142
Department of Control and Computer Engineering (Dauin)Torino, Italy142
Faculty of Computing, University of TechnologySkudai, Malaysia142
School of Computing, Universiti Utara Malaysia (UUM)Kedah, Malaysia142
4Yellow15%Big Data Analytics Research Group, Comsats Institute SahiwalSahiwal, Pakistan138
Department of Computer Science, Comsats Institute of Information Technology (Ciit)Vehari, Pakistan138
Department of Computer Science, Comsats Institute of Information TechnologySahiwal, Pakistan138
5Violet15%Department of Computer Software Engineering, National University of Sciences and TechnologyIslamabad, Pakistan1313
Department of Information Communications Engineering, Hankuk University of Foreign StudiesSeoul, South Korea1313
Departmet of Computer Science, Sarhad University of Science and Information TechnologyPeshawar, Pakistan1313
%: percentage of total research institutions; (*) leading institution; A: articles; L: Links; C: citations.
Table 15. Top 10 Countries and main keywords (2011–2019).
Table 15. Top 10 Countries and main keywords (2011–2019).
CountryA%Subject Areah *1A *LA *Keyword 1Keyword 2Keyword 3
China21617.53CS-EN3520112019Big DataNetwork SecurityAutomation
USA20116.31CS-EN3720132019Big DataAutomationWireless Sensor Network
India19515.83EN-CS2020152019Wireless Sensor NetworkCloud ComputingBig Data
Spain13711.12CS-EN2620112019InternetEnergy UtilizationWireless Sensor Network
Italy1088.77CS-EN2720122019InternetBig DataWireless Sensor Network
UK1048.44CS-EN2920122019InternetBig DataInformation Management
South Korea816.57CS-EN1820132019Network SecurityAutomationBig Data
Australia625.03CS-EN1920142019InternetBig DataCloud Computing
Canada554.46CS-EN1620132019AutomationBig DataEnergy Efficiency
Pakistan534.30CS-EN1420162019Big DataAutomationIntelligent Building
A: number of articles; %: percentage of total articles published; CS: Computer Science; EN: Engineering; h: h-index or Hirsch index; 1A: first article; LA: last article; (*) in this research topic.
Table 16. Clusters of countries (2011–2019).
Table 16. Clusters of countries (2011–2019).
ClusterCluster Color (See in Figure 8)%CountryArticlesLinksTLSCitations
1Pink47.56%United States (∗)201402135441
United Kingdom104381653312
South Korea8126891671
2Green23.17%Spain (∗)137331275073
3Red8.54%Australia (∗)6229852287
4Yellow8.54%China (∗)218392025002
Hong Kong14517178
5Violet7.32%Italy (∗)10827875856
South Africa54473
6Cyan4.88%Germany (∗)4126551136
Bosnia and Herzegovina22238
%: percentage of total countries; (∗) leading country; TLS: Total Link Strength.
Table 17. Future research lines.
Table 17. Future research lines.
Research LineLinksTotal Link StrengthDescription
Energy Storage6771Efficient energy storage as an essential support for the energy transition and key to a decarbonized future. This allows flexibility in the production of renewable energy and guarantees its integration into the system.
Environmental Temperature4848Development of measurement, instruments, and applications of sensors for environmental and urban temperature. Establish an intelligence guide to ambient temperature in the IoT environment.
Geographic Distribution4848Use of remote sensors in the analysis of landscape fragmentation to monitor the patterns involved in fragmentation processes and thus avoid the loss of ecosystems and biodiversity.
Intentional Contaminations4040Increased research on pollution sensors that measure environmental variables, such as the concentration of CO2 and particles in suspension, in addition to generating urban pollution maps by region.
Remote Health Monitoring3232Remote patient monitoring technology that allows patient observation outside of conventional clinical settings. This will mean access to care and lower costs of medical care.
End Users2930Training tools for end users of information systems. Study of the user experience in the positive evolution of the smart city based on IoT, from the comfort, security, and control associated with connectivity. Analysis of the perception of the benefits of the IoT by the end user, for example, in energy savings in the home or car or in a more efficient use of daily activities.
Electronic Crime Countermeasures2828Protection against Computer Crime and Information Security, in addition to regulatory development.
Industrial Internet of Things (IIoT)2627Development and extension of the use of the Internet of Things (IoT) in industrial sectors and applications, such as robotics, medical devices, and software-defined production processes.
Flood Control2727Design and specifications of flood control systems with IoT sensors. Real-time control of flood control structures, using rainfall forecasts, sensor data, and water level and flow forecasts.
Social Internet of Things (SIoT)2424Study of how the integration of the principles of social networks in the IoT generates social and economic impact among the information consuming society.

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González-Zamar, M.-D.; Abad-Segura, E.; Vázquez-Cano, E.; López-Meneses, E. IoT Technology Applications-Based Smart Cities: Research Analysis. Electronics 2020, 9, 1246.

AMA Style

González-Zamar M-D, Abad-Segura E, Vázquez-Cano E, López-Meneses E. IoT Technology Applications-Based Smart Cities: Research Analysis. Electronics. 2020; 9(8):1246.

Chicago/Turabian Style

González-Zamar, Mariana-Daniela, Emilio Abad-Segura, Esteban Vázquez-Cano, and Eloy López-Meneses. 2020. "IoT Technology Applications-Based Smart Cities: Research Analysis" Electronics 9, no. 8: 1246.

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