Internet of Things: A Scientometric Review

Ruiz-Rosero, Juan; Ramírez-González, Gustavo; Williams, Jennifer M.; Liu, Huaping; Khanna, Rahul; Pisharody, Greeshma

doi:10.3390/sym9120301

Cited by 45 publications

(31 citation statements)

References 232 publications

(231 reference statements)

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“…The most relevant software tools for this analysis and mapping we found are Bibexcel by Persson [113], Citespace II by Chen [114], CitNetExplorer de Eck and Waltman [115], Leydesdorffs Software [116], Matheo Analyzer [117], Sci2 Tool [118], SciMat [119], ScientoPy [120] or VOSViewer [110]. Between all these tools, we found the most used is Citespace with some cases applied to tourism research as in References [121][122][123][124].…”

Section: Methodsmentioning

confidence: 99%

Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability

et al. 2019

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The tracking of tourist movements is an essential aspect in the management of sustainable tourist destinations. The current information and communication technologies provide innovative ways of collecting data on tourist movements, but it is still necessary to evaluate tools and methods of study for this challenge. At this point, mobile technologies are the best candidate for this task. Given the relevance of the topic, this paper proposes a mapping science analysis of publications on "movement of tourists" and "traceability." It has been carried out in the two main sources WOS and SCOPUS. The term "traceability" is brought from industry and technology areas to be applied to the tourist movement/mobility tracking and management. The methodological scheme is based on a selection of search criteria with combinations of terms. The sources of specialized information in applied social sciences and technology were then selected. From there, the searches have been executed for their subsequent analysis in three stages-(I) relevance analysis filtering the results to obtain the most pertinent; (II) analysis of articles with similarity thematic, authors, journals or citations; (III) analysis of selected papers as input for the mapping analysis using Citespace. The automatic naming of clusters under the selected processing confirms that the analysis of movements is a valid scientific trend but research-oriented from the perspective of traceability is non-existent, so this approach is novel and complementary to existing ones and a potential contribution to knowledge about tourist movements. Finally, a set of methodological considerations and a classification of information capture tools are proposed. In this classification, mobile technology is the best option to enable tourist movement analysis.

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Section: Methodsmentioning

confidence: 99%

Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability

et al. 2019

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“…One paper on data science techniques and applications is included as follows: "Internet of things: a scientometric review", by Ruiz-Rosero et al from Colombia and USA [45]. The study proposed a methodological approach to collect and discuss relevant literature from two scholarly bibliographic databases (i.e., Clarivate Analytics Web of Science and Scopus) for the analyses of country, author, and research topics (i.e., applications, communication protocols, software processing techniques, device operating systems (OS) and hardware, and top trending topics).…”

Section: Data Science Techniques and Applicationsmentioning

confidence: 99%

Introduction to the Special Issue: Applications of Internet of Things

et al. 2018

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This editorial introduces the special issue, entitled “Applications of Internet of Things”, of Symmetry. The topics covered in this issue fall under four main parts: (I) communication techniques and applications, (II) data science techniques and applications, (III) smart transportation, and (IV) smart homes. Four papers on sensing techniques and applications are included as follows: (1) “Reliability of improved cooperative communication over wireless sensor networks”, by Chen et al.; (2) “User classification in crowdsourcing-based cooperative spectrum sensing”, by Zhai and Wang; (3) “IoT’s tiny steps towards 5G: Telco’s perspective”, by Cero et al.; and (4) “An Internet of things area coverage analyzer (ITHACA) for complex topographical scenarios”, by Parada et al. One paper on data science techniques and applications is as follows: “Internet of things: a scientometric review”, by Ruiz-Rosero et al. Two papers on smart transportation are as follows: (1) “An Internet of things approach for extracting featured data using an AIS database: an application based on the viewpoint of connected ships”, by He et al.; and (2) “The development of key technologies in applications of vessels connected to the Internet”, by Tian et al. Two papers on smart home are as follows: (1) “A novel approach based on time cluster for activity recognition of daily living in smart homes”, by Liu et al.; and (2) “IoT-based image recognition system for smart home-delivered meal services”, by Tseng et al.

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“…Public Transport Systems (PTS) move a large number of people while consuming fewer resources (lower energy requirements, lower accident cost, and air quality improvements). Currently, one of the top applications of the Internet of Things (IoT) is the development of smart cities [2]. Smart cities target the increasing capacity to collect a large quantity of data from a PTS and process them in real-time (big data processing).…”

Section: Introductionmentioning

confidence: 99%

Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

2019

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There is a large number of tools for the simulation of traffic and routes in public transport systems. These use different simulation models (macroscopic, microscopic, and mesoscopic). Unfortunately, these simulation tools are limited when simulating a complete public transport system, which includes all its buses and routes (up to 270 for the London Underground). The processing times for these type of simulations increase in an unmanageable way since all the relevant variables that are required to simulate consistently and reliably the system behavior must be included. In this paper, we present a new simulation model for public transport routes’ simulation called Masivo. It runs the public transport stops’ operations in OpenCL work items concurrently, using a multi-core high performance platform. The performance results of Masivo show a speed-up factor of 10.2 compared with the simulator model running with one compute unit and a speed-up factor of 278 times faster than the validation simulator. The real-time factor achieved was 3050 times faster than the 10 h simulated duration, for a public transport system of 300 stops, 2400 buses, and 456,997 passengers.

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Internet of Things: A Scientometric Review

Cited by 45 publications

References 232 publications

Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability

Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability

Introduction to the Special Issue: Applications of Internet of Things

Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

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