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Article

Internet of Things: A Scientometric Review

by
Juan Ruiz-Rosero
1,*,
Gustavo Ramirez-Gonzalez
1,
Jennifer M. Williams
2,
Huaping Liu
2,
Rahul Khanna
3 and
Greeshma Pisharody
3
1
Departamento de Telemática, Universidad del Cauca, Popayán, Cauca 190002, Colombia
2
School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USA
3
Intel Corporation, 2111 NE 25th Ave., Hillsboro, OR 97124, USA
*
Author to whom correspondence should be addressed.
Symmetry 2017, 9(12), 301; https://doi.org/10.3390/sym9120301
Submission received: 31 August 2017 / Revised: 9 November 2017 / Accepted: 23 November 2017 / Published: 6 December 2017
(This article belongs to the Special Issue Applications of Internet of Things)
Browse Figures
Graphical abstract
"> Figure 1
<p>Documents per year published (WoS and Scopus) with the search string “Internet of Things” (IoT) for the period 2000 to 2016. (<b>a</b>) before the duplicates-removal filter; (<b>b</b>) after duplicates-removal filter.</p> "> Figure 2
<p>Internet of Things percentage of documents published per year by the top 7 first author’s corresponding address country for the period 2002 to 2016.</p> "> Figure 3
<p>Internet of Things top 5 authors with most documents published per year, for the period 2006 to 2016.</p> "> Figure 4
<p>Internet of Things top authors’ keywords documents published per year, excluding the keywords: Internet of Things, IoT, Internet of Things (IoT), and The Internet of Things, for the period 2006 to 2016.</p> "> Figure 5
<p>Internet of Things top applications based authors’ keywords in documents per year, for the period 2006 to 2016. (<b>a</b>) applications that start with “smart” (<b>b</b>) applications that do not start with “smart”.</p> "> Figure 6
<p>Internet of Things media and host layers communication protocols based on authors’ keywords in documents per year, for the 2006 to 2016 period. (<b>a</b>) media layers’ communications protocols; (<b>b</b>) host layers’ communication protocols.</p> "> Figure 7
<p>Internet of Things software processing techniques based on authors’ keywords in documents per year, for the period 2006 to 2016.</p> "> Figure 8
<p>Internet of Things devices operating systems (OS) and hardware based on authors’ keywords, for the period 2006 to 2016. (<b>a</b>) most used operating systems in authors’ keywords per year; (<b>b</b>) most used hardware in authors’ keywords per year.</p> "> Figure 9
<p>Internet of Things top trending topics based on authors’ keywords, with average growth rate (AGR) for different times periods (2011–2012, 2013–2014, and 2015–2016).</p> ">
Versions Notes

Abstract

:
Internet of Things (IoT) is connecting billions of devices to the Internet. These IoT devices chain sensing, computation, and communication techniques, which facilitates remote data collection and analysis. wireless sensor networks (WSN) connect sensing devices together on a local network, thereby eliminating wires, which generate a large number of samples, creating a big data challenge. This IoT paradigm has gained traction in recent years, yielding extensive research from an increasing variety of perspectives, including scientific reviews. These reviews cover surveys related to IoT vision, enabling technologies, applications, key features, co-word and cluster analysis, and future directions. Nevertheless, we lack an IoT scientometrics review that uses scientific databases to perform a quantitative analysis. This paper develops a scientometric review about IoT over a data set of 19,035 documents published over a period of 15 years (2002–2016) in two main scientific databases (Clarivate Web of Science and Scopus). A Python script called ScientoPy was developed to perform quantitative analysis of this data set. This provides insight into research trends by investigating a lead author’s country affiliation, most published authors, top research applications, communication protocols, software processing, hardware, operating systems, and trending topics. Furthermore, we evaluate the top trending IoT topics and the popular hardware and software platforms that are used to research these trends.

Graphical Abstract">

Graphical Abstract

1. Introduction

Internet of Things (IoT) connects billions of devices to the Internet and has gained tremendous popularity in the past decade as a diverse and pioneering technology. In general, IoT devices combine sensing, computation, and communication techniques to deliver remote data collection and system control. Today, these “things” range from everyday consumer electronics to specialized industrial systems [1], such as fitness-tracking wristwatches [2], transport logistics [3], and smart cars [4] to manufacturing [5] and smart grids [6]. Contingent on implementation, an IoT device may be used for real-time alerts, data archiving, trend analysis, and forecasting by leveraging related technologies such as cloud services [7]. Furthermore, the technology has proven useful for small- and large-scale networks, generating a vast portfolio of enabling hardware and software at various complexities [8,9]. IoT technology has led to solutions in use-cases ranging from smart appliances, utilities, biomedical, industrial, data center management, agriculture, body area networks (BANs), surveillance, and more.
Proliferation of IoT research has contributed to increased availability, affordability, responsiveness, diversity, miniaturization, mobility, and more. Recent studies have demonstrated that IoT, cloud computing, and mobile solutions are among the top technologies that will shape our future in the next 3–5 years [7]. Not surprisingly, connectivity and intelligence are becoming a contributing factor to many designs fueling advanced development. Therefore, the number of new designs and publications categorized under IoT continues to grow exponentially.
Evolution of IoT has spearheaded many research fields such as wireless sensor networks (WSNs), Big Data, and cloud computing. Wireless Sensor Networks (WSN) comprise: sensor nodes, specialized firmware [10], relay devices, and data sinks called a gateway. In addition to facilitating data archiving and local processing, the gateway also acts as a hub that connects to the worldwide web for cloud storage and services using a WiFi or cellular network.The computational complexity of analysis and functional use of the data towards trend and forecasting has grown rapidly, such as in the data center management use-cases [11]. The radio frequency (RF) communication protocols and the interaction between these sensor entities continue to place stringent hardware requirements. Implementations using one software stack over another could achieve better range, quality-of-service (QoS), and spectral efficiency, at the expense, however, of additional processing, storage, power, and form-factor [9]. Additionally, the connectivity and archiving with WSN results in a large volume of samples that create a “big data” challenge.
While IoT is not a new paradigm, it is gaining traction in recent years around the world and yielding extensive research from diverse perspectives. As a result, IoT and similar technologies are progressively challenging topics to review. Starting in 2010, Atzori et al. made a survey about IoT enabling technologies and applications [12]. Then, in 2013, Gubbi et al. defined a cloud center vision for worldwide implementation of IoT, describing the key enabling technologies, applications domains and future directions [13]. In 2014, Borgia presented an extended review about IoT key features, driving technologies and protocols, applications, challenges, IoT initiatives, and research directions [14]. Next, in 2015, Yan et al. developed a co-word analysis, generating seven clusters that represented the intellectual structure of IoT, which were analyzed by a co-occurrence matrix [15]. The following year, in 2016, Mishra et al. composed a bibliometric study about the future vision, applications, and challenges of Internet of Things [16]. In that review, Mishra et al. identified the top contributing authors, key research topics, most influential works, and emerging research clusters, limited only to future vision and applications of IoT, from a sample of 1556 papers from the Scopus database.
As noted above, when conducting a review of IoT publications in recent years, the outcome may vary depending on methodology and time spent browsing through search results. At a minimum, only publications of reputable categories from credible databases should be considered for the review process. For example, conference papers, journal articles, proceedings papers, and reviews are widely accepted as reliable information sources in the industry and academia. Additionally, the manual labor of searching thousands of bibliographic data can be reduced by scripting to facilitate the filtering and comparison activities. This allows the reviewer additional time to investigate supplementary metrics in order to render stronger and methodological conclusions.
Therefore, this paper presents a methodology for citation analysis using search results produced by two scholarly bibliographic databases: Clarivate Web of Science (WoS) and Scopus. To facilitate a thorough review of several thousand publications related to IoT, the study presented herein utilized a novel literature review script called ScientoPy to analyze document bibliographies according to predefined metrics. This scientometrics analysis provides insight into research trends in IoT over recent years by investigating a lead author’s country affiliation, most published authors, and prevalence of various research topics. Using the authors’ keywords, the research topics inspected in this review include applications, communication protocols, software processing, hardware, operating systems, and trending topics.

2. Materials and Methods

Scientometrics is the study of measuring and analyzing scientific literature by measuring the impact of the innovation and understanding the relevance of these scientific citations to this innovation [17]. Thus, a Python script for scientometrics literature review (ScientoPy) was developed by the authors to analyze content of publications related to the Internet of Things. This ScientoPy script has the capability to:
  • Read Clarivate Web of Science and Scopus databases (.CSV files).
  • Filter publications by document type.
  • Find and remove duplicated documents.
  • Graph the history of the top topics (keywords, authors, countries).
  • Graph the history of selected items inside a topic.
  • Find trending topics using the top average growth rate (AGR).
  • Calculate the h-index for authors and countries.
ScientoPy is a Python script that automatically generates and reports the top topics (based on authors’ keywords), authors, and countries, along with related documents. This automatic data synthesis avoids potential bias as in individual studies. Nevertheless, author name analysis (such as author top list) has a risk of bias across the studies due to possible similarities in names. The writers of this review know and warn about this possibility of documents’ author names similarity, which is part of the limitation of any scientometrics study; thus, in this moment not all the authors and data bases have a unique author identifier, like the ORCID, associated with all entries.

2.1. Data Set

This scientometrics analysis used two bibliographic databases: Clarivate Web of Science (WoS), and Scopus. For the span of 1 January 2002 to 31 December 2016, the following document types were studied:
  • Conference Paper;
  • Article;
  • Review;
  • Proceedings Paper.
The search string for this analysis was “Internet of Things”. This string was applied to the topic search in WoS and Scopus, which includes title, abstract, authors’ keywords, and KeyWords Plus® (for WoS). With this search criteria, the data set was extracted within a day on 6 July 2017. Table 1 describes the number and type of documents found in the two databases totaling 27,120 documents.

2.2. Pre-Processing

A pre-processing technique was applied to improve reliability and precision, as detailed in the following sub sections.

2.2.1. Simplify Author’s name

In general, scientific and bibliographic databases have the following inconsistencies in authors names:
  • Most journals abbreviate the author’s first name to an initial and a dot.
  • Most journals use the author name’s special accents.
  • WoS uses a comma between the author’s last name and first name initial, but Scopus does not.
These name-related inconsistencies mean that scientometrics scripts cannot find all of the similar author’s names. For that reason, ScientoPy script applies the following steps to simplify author’s name fields:
  • Remove dots and coma from author’s name.
  • Remove special accents from author’s name.

2.2.2. Remove Duplicate Samples

Of the 27,120 original samples, only 72% have an associated DOI for uniqueness. Therefore, duplicated samples were identified by identical title and authors. For duplicated samples in different databases, the WoS publication was kept and the Scopus sample was removed from the set, resulting in remaining 19,035 documents. Table 1 shows the number of documents by type and duplicates removed for each database.

2.3. Times Cited and h-Index

Scopus and WoS databases report the Times Cited Count for each document; however, 47% of the Counts for the same (duplicate) document differ between sources. In such instances, the ScientoPy script selects the highest Times Cited Count, be it from Scopus or WoS, to assign the most favorable value to each document for this metric. Therefore, the h-index for authors and countries is calculated based on these Times Cited Count for the period 2006 to 2016.

2.4. Document’s Country

In this study, the document’s country was extracted from the primary author’s corresponding address. Thus, only one country was associated to each document. Furthermore, some authors use different naming to refer to the same country (such as USA and United States). For that reason, some country names were replaced based on Table 2.
In this data set, 95 documents were missing the author’s corresponding address to extract the document’s country. These samples were discarded for analyses related to country.

2.5. General IoT Publications Growth

The yearly growth of IoT related documents were observed as in Figure 1a, revealing an exponential growth in both databases (WoS and Scopus), without removing the duplicated documents. Figure 1b shows the similar growth after removing the duplicated documents.
The first mention of the “Internet of Things” was an article published in March 2002 reported by WoS. Published by Forbes, Schoenberger, and Upbin, this article described how the IoT could be a standardized way to help the computers understand the world [18]. In 2003, Scopus reported a paper from the Institute of Electrical and Electronics Engineers (IEEE) International Conference on Systems, Man and Cybernetic, in which Qui and Zhang showed the design of enterprise web servers supporting instant data retrieval for a product labeled with an Radio-frequency identification (RFID) based smart tag [19]. Scopus reported a second conference paper in 2003 for the 36th Annual Hawaii International Conference on System Sciences, Traversat et al. on the stated the JXTA (abbreviation of Juxtapose) protocols as a foundation of the upcoming Web of Things [20].
In 2004, WoS and Scopus reported the same two articles: “The Internet of Things” by Gershenfeld et al. [21], and “The Supply Chain” by Luckett [22]. From 2005 to 2016, Scopus reported about 30% more publications than WoS. Nevertheless, for this research, WoS documents were given more priority over Scopus documents during the duplicates-removal process because WoS fields were more complete than Scopus, such as cited references with Digital Object Identifier (DOI) number and subject category. For this reason, Figure 1b shows more documents from WoS than Scopus from 2013 onwards.

3. Country and Author Research Analysis

In this section, analysis was focused on authors and their corresponding country. Below is a graph of the percentage of publications related to IoT each year for the seven countries with the highest occurrence in the data set. A table of the most occurring 50 countries is also provided. Another graph presents the top five authors per year, alongside tables detailing the top 20 authors and 10 most cited author documents for articles, conferences and reviews.

3.1. Country Analysis

A list of the countries with the most associated publications was generated. Figure 2 shows the top seven countries, along with the percentage of documents published per year. In 2002, one article was published on Forbes by Schoenberger; unfortunately, the database does not associate any author address for this document and the sample had to be removed from this data set according to methodology. In 2003, two conference papers were published by United States authors [19,20]. In 2004, there was one review publication in the United States [21] and one article in the United Kingdom [22].
Germany [23] and Malaysia [24] first appear in 2005, joined by the United States [25]. These three conference papers demonstrated how the RFID can boost Internet of Things for manufacturing, packing, tracking, and automobile logistics. In 2006, the total publications grew from 3 to 12, with more countries participating, such as France [26,27], Switzerland [28,29], and Japan [30]. From 2006 to 2009, Germany led the number of publications with 2, 3, 9, and 11, respectively. During that period, the German author Broll led the citations count with a proposed framework for integrating web services and mobile interaction with physical objects [31].
China drastically increased from 11 to 239 publications from 2009 to 2010, continuing to contribute more than half of the globally published documents between 2010 to 2013. Most of that growth resulted from China’s Twelfth 5 Year Plan (2011–2015), which included the development of the Internet of Things [32]. The conference paper “IOT Gateway: Bridging Wireless Sensor Networks into Internet of Things” by Zhu was the most cited IoT paper during this period for China, as it explained how an IoT Gateway could make a bridge between wireless sensors networks and traditional communications networks to the Internet [33].
From 2014 to 2016, China maintained the highest rank, contributing 20% of the globally published documents, as well as a peak in 2016 and h-index of 47. In 2014, the United States was second to China with 187 publications and h-index of 42. India’s contributions grew at a rate of 153%, 103%, 286%, and 120%, in 2013 to 2016, respectively, moving from the 8th to 3rd position in 2013 and 2016, respectively. The Indian daily “The Economic Times” forecasted the country is expected to see a rapid 31-fold growth of IoT devices to reach 1.9 billion by 2020 [34].
Expanding the results from Figure 2, Table 3 shows the top 50 countries of the primary author with the average percentage growth and the h-index of each country from the last three years (2014 to 2016). Of the top 10 countries, South Korea represents the maximum average growth with 206%, where the mobile carrier SK Telecom (Seoul, South Korea) launched the first commercial low-cost Internet of Things (IoT) network in 2016 [35]. However, this growth is not reflected yet (next year) in the available literature and thus the data set has an h-index of 16, only half of its successor in this list, Italy. In the same way, this list includes the top growing countries with low h-index but anticipated to be higher next year: Indonesia, Turkey, Russian Federation, and Pakistan.
The International Data Corporation (IDC) predicts that, by 2019, 20% of local and regional governments in Indonesia will use the Internet of Things to turn infrastructure such as roads, street lights, and traffic signals into assets instead of liabilities [36]. In addition, the Dutch IoT start-up, Xeelas (Arnhem, Netherlands), and Turkish group, Sade (Ankara, Turkey), partnered to build Turkey’s largest LoRaWAN (LoRa, Long Range Wide-area network network) in Istanbul to enable business, local governments, and conservation groups to collect and analyze from connected devices [37]. In Russia, the Internet of Things market is expected to reach USD 74 Billion by 2023, where the Russian government’s Internet start-up fund (FRII) has joined forces with tech giants GS Group (Saint-Petersburg, Russia) and mobile operators to launch a national Internet of Things (IoT) consortium [38]. In Pakistan, by January 2017, 17 Internet of Things start-ups were launched, on their own or incubated, at Plan9 (Lahore, Pakistan), NEST i/o (Karachi, Pakistan), and i2i (Islamabad, Pakistan) [39].

3.2. Author Analysis

The data set analyzed here includes 31,422 authors of the 19,035 documents related to Internet of Things. In addition, 592 of these authors have 10 or more publications in WoS or Scopus. Figure 3 shows the top five authors with the most published documents per year. Y. Zhang was positioned first with 130 published documents related to IoT and RFID, security, electric vehicle, artificial immune system, smart grid, and cloud computing. In 31 documents, Y. Zhang appeared as a first author. His most cited document is the article titled “Toward Cloud-Based Vehicular Networks with Efficient Resource Management” [40] with 96 citations.
Y. Liu is positioned second with 115 documents, of which 36 list him as the primary author. His publications are more focused on hardware such as Raspberry Pi, test bed, optical communications, ZigBee, and RFID. The article titled “IOT gateway: Bridging wireless sensor networks into Internet of Things” is his most cited document with 134 citations [33]. Y. Liu shares the authorship in four publications with the first author in this list, Y. Zhang.
Y. Li is the third in this list with 97 publications, with 37 as the primary author. His focus was on RFID, big data, and databases. “Towards a theoretical framework of strategic decision, supporting capability and information sharing under the context of Internet of Things” [41] is his most cited publication with 39 citations. With the same number of publications, 97 and 39 as the primary author, Y. Wang is next in this list. His research is related to RFID, smart gird, security, logistics, and big data. His most cited document is [42] with 16 citations. Lastly, fifth on this list is J. Li with 96 publications with 33 as the primary author. His papers related to RFID, ZigBee, and standardized breeding, with his proceedings paper in [43] being his most cited paper with 97 citations in this set.
Table 4 shows the top 20 authors with the most published number of documents, along with the author’s h-index in IoT, most cited document, and top research topics. Nevertheless, the two-top h-index authors in this case are not in the top 20 number of documents. L.D. Xu is the author with the highest h-index of 21 and 33 publications. Similarly, L. Atzori has second place in h-index of 14 and 41 documents.
Table 5 shows the most cited papers for three document types (articles, conference/proceedings and reviews). Atzori et al. surveys IoT vision and enabling technologies [12]. Second in articles, Gubbi et al. describe a cloud-centered vision for the worldwide implementation of IoT [13]. Miorandi et al.’s article surveys on technologies, applications and research challenges for IoT [59]. For conferences and proceedings, Bonomi et al. describe the Fog computing characteristics and its role in IoT [60]. Tao et al. propose cloud computing, Internet of Things, virtualization, and service-oriented combination technologies with advanced manufacturing models and enterprise information technologies to generate a new manufacturing model, called cloud manufacturing (CMfg) [61]. Tan and Wang show a skeleton of the Internet of Things with an application model that can apply to automatic facilities management in the smart campus [62].
Finally, on the reviews side, Gershenfeld et al. present a review about the Internet-0 (Internet-Zero) protocol, whose approach for the reduced complexity of the IP stack extends the notion of internetworking to interdevice [21]. Meng and Ci mention that the data type and amount is growing at a high speed due to emerging services such as cloud computing, IoT, and social media. Thus, they review the concept of big data and describe a new era for data handling [63]. Lastly, Aziz et al. surveys the topology control techniques for extending the lifetime of battery to power WSNs for the Internet of Things battery-powered devices [64].

4. Research Topics

IoT has a broad spectrum of research fields such as applications, smart objects, communications protocols, software processing, devices hardware, and operating systems. On the data set analyzed here, most of the authors include their research topic in the document keywords. In this section, author keywords were analyzed to find the trends in the different research topics. For Scopus, the regular authors’ keywords were used, and similarly for WoS. KeyWords Plus from WoS were discharged because they are index terms created automatically from significant, frequently occurring words in the titles of an article’s cited references, and they are less comprehensive in representing an article’s content [82]. The top 1000 keywords were extracted, manually classified in the different research field, and grouped by plural-singular similarity and/or abbreviations. For instance, the keywords WSN, wireless sensor network, and wireless sensor networks were grouped into the WSN keyword. The following subsections describe the trend of the research topics in different fields, based on the top authors’ keywords publications per year. Figure 4 shows the general top 10 authors’ keywords.

4.1. Applications

There are several application fields related to IoT research and development. In this section, the authors’ keywords were analyzed to find the top specified applications. Figure 5 shows the trend of these applications in documents per year. Furthermore, Figure 5a presents the applications that start with the word “smart”, and Figure 5b those that do not.
In the data set, 1052 documents were found for applications that start with “smart”. Smart city is the top one in this list, with 413 documents, and a sigmoid growth in exponential phase, 95% more publications in 2016 vs. 2015. A.J. Jara has the most number of documents in this field with 12 publications. His most cited document [83] refers to Smart and Connected Communities as a concept that is evolving from Smart cities. The leading country in this application is Italy with 50 publications, and the most important correlated topics are Big data [84,85,86], Cloud computing [87,88], and Smart grid [89,90]. Similarly, Smart home has a linear growth, with 230 documents, and 111 in the last year, with China as the leading country. The most important related topics for Smart home are security [91,92,93], ZigBee [94,95,96], and activity recognition [97,98,99].
In contrast, smart grid is a topic that has not demonstrated continuous growth. In 2012–2013, the documents published per year decreased from 21 to 13, and in 2015–2016 from 56 to 55. Nevertheless, an overall 194 documents were registered on this topic, with China as the leading country. The term smart grid refers to “a next generation power grid that uses two-way flows of electricity and information to create a widely distributed automated energy delivery network” [100]. Within IoT, the research on smart grids are related to security [101,102,103], cloud computing [104,105], and privacy [103,106,107].
According to Dumitrache, “Cyber-Physical Systems (CPS)s are physical, biological and engineered systems whose operations are monitored, coordinated, controlled and integrated by a computing and communication core” [108]. Publications of this topic related to IoT have a sigmoid growth in exponential phase, with 182 publications in 2016, noting United States as the leading country with 34 documents. The most important related topics are: Industry 4.0 [109,110,111,112], big data [113,114,115], and security [116,117,118]. Next, Healthcare and E-Health related to IoT exhibited a sigmoid growth in a transitional phase, with a total of 180 documents, and India as the leading country with 20 publications. Different IoT technologies are applied in this area such as sensors [119], RFID [120,121,122,123], 6LoWPAN [124,125], and wearables [126,127]. The third most growth topic was energy efficiency with 154 documents, and China and Italy as leading countries with 18 and 17 publications, respectively. Energy efficiency as an application for IoT is related in this data set with the following topics: smart buildings [128,129,130], energy harvesting [131,132,133], and RFID [134,135]. Social networks (or Social media) is another application for IoT, with 117 documents, and Italy as the leading country with 23 publications. Among the top related topics in this field are trust management [136,137,138] and recommendation systems [139,140]. Education, Learning, E-Learning, and mobile learning is the fifth top application in this list, with 93 publications and the United Kingdom as the leading country with 12 documents. The most popular related topics with education are: augmented reality [141,142,143], context aware [144,145,146], and near field radio technologies such as RFID [147,148,149] and Near Field Communication (NFC) [150,151].

4.2. Communication Protocols According to Open Systems Interconnection Model (OSI Model)

Regarding telecommunications systems, the Open Systems Interconnection model (OSI model) describes the communications process in seven layers which are divided into media layers (Physical, Data, and Network) and host layers (Transport, Session, Presentation, and Application) [152]. In this review on IoT, the most used communication protocols are divided into these two layers (see Table 6). Figure 6 shows the yearly trend of the different communications protocols for media layers in Figure 6a and host layers in Figure 6a.
RFID is the top used author’s keyword in the analyzed data set and the most used media layer communication protocol in the authors’ research. On IoT 923 publications are related to RFID, where security authentication [153,154,155], wireless sensors networks [156,157], privacy [158,159], and electronic product code (ECP) [160,161] were major applications. Next, 6LoWPAN appears in 230 publications, with documents related to upper layer protocols such as Constrained Application Protocol (CoAP) [162,163], Routing Protocol for Low power and Lossy Networks (RPL) [164,165], and operating systems like Contiki [166,167], and Android [168,169]. With similar growth, ZigBee follows with 222 documents, and research integrates this protocol with solutions such as RFID [170,171,172], or applications like smart home [95,173,174], and health care [175,176,177].
Bluetooth Low Energy (BLE) has experienced a rapid growth in the last three years, near 100% from 2015 to 2016. A total of 98 documents on IoT are related to BLE, with one of the most cited articles by Gomez et al. at 176 citations. In this article, the authors describe the main features and potential applications for BLE technology [178]. This data set shows BLE related applications such as home automation [179,180,181] and indoor location [182,183,184] and health care [185,186,187]. WiFi is the other network protocol used for IoT research, with a total of 85 publications, and applications related to: home automation [188,189], indoor localization [190]. Nevertheless, with this wireless technology, some authors have focused on how the 2.4 GHz spectrum could be efficiently used with other IoT network protocols [191,192,193,194,195,196,197]. Similarly, 5th generation mobile networks (5G) appear in IoT with 82 publications, much more than 4G and LTE, with 54 documents both combined. The most cited paper is a suvery on 5G architecture and emerging technologies written by Gupta et al. [198] with 109 citations. Network Function Visualization (NFV) and Software Defined Networking (SDN) [199,200,201] were the top related technologies, which offer different architectural options to address IoT needs for 5G. Finally, RPL is discussed as an IPv6 Routing Protocol for Low-Power and Lossy Networks as a mechanism for multipoint-to-point and point-to-multipoint traffic for these kinds of networks [202]. This protocol has 78 documents with publications related to the Contiki OS and its simulator tool Cooja for WSN [203,204], and mesh networks [205,206,207].
At the host layer, communication protocols publications are led by the Constrained Application Protocol (CoAP), which is a specialized web transfer protocol for use with constrained nodes and constrained networks [208]. A total of 201 publications were found in this area, with some of these publications related to: 6LoWPAN [163,165,209], and Datagram Transport Layer Security (DTLS) [210,211]. Second, the Message Queue Telemetry Transport (MQTT) shows up with 46 documents. This is a lightweight, and open client-server publish/subscribe messaging transport protocol [212]. Next, Datagram Transport Layer Security (DTLS) protocol follows this list with 35 publications. This DTLS provides communications privacy for datagram protocols based on the stream-oriented Transport Layer Security (TLS) [213]. This protocol helps to enhance the security of others’ higher layers protocols like CoAP [214,215]. Finally, iBeacon is the fifth on this list with nine documents. This is a protocol designed by Apple (Cupertino, CA, United States) to describe its own implementation of BLE Beacon, which emits a signal that can be detected by any BLE enabled device within a close range [216]. Most of the applications for this protocol include indoor localization [50,217].

4.3. Software Processing Techniques

The proliferation of IoT has significantly increased the data collection and the strain it places on faster data analytics. Several software processing techniques have been researched, developed, and published. In these published documents, the various software processing techniques used were specified in the authors’ keywords. Figure 7 shows the top authors’ keywords for these processing techniques. Machine learning is the most popular research technique for data processing with 100 publications. This technique is used for data prediction [218,219], activity recognition [220,221], and data classification [222,223]. Next, data mining appears with 89 documents, with distributed data mining [224], and applications such as event detection [225,226] as sub-techniques.
Complex event processing is a method of tracking and analyzing streams of data surrounding events or anomalies and basing a conclusion from them [227]. It was found that 63 documents are related to this processing technique with applications such as supply chain [228,229,230] and health care [231,232]. Apache Hadoop (or Hadoop) is a software framework used for distributed storage and big data processing using the MapReduce programming model [233], appearing with 42 documents and applications including smart cities [234,235,236], and Social Internet of Things [237].

4.4. Device Operating Systems (OS) and Hardware

The data set analyzed here shows that the investigations used different IoT devices (end devices and gateway devices), operating systems (OS), and hardware. Figure 8 shows the top authors’ keywords per year for the most employed OS and hardware. Android is the most used OS for researchers, with 87 documents. This OS is used for IoT gateways [238,239,240,241] or end sensing devices [242,243,244]. Contiki is a lightweight OS for memory constrained systems (like microcontroller-based systems) designed for low-power wireless devices [245]. A total of 56 publications were found related to this OS, where the author uses capabilities like embedded protocols: 6LoWPAN [166,246], CoAP [247], and RPL [248]. In addition, some publications use the Contiki network simulator Cooja to simulate routing protocols [249] and performance evaluation [250].
Other operating systems, such as Linux, are used in IoT for image processing [251] and gateway services [252]. The iPhone Operating System (iOS) is used as user interface for presentation, configuration, and remote controlling for IoT environments [253]. Finally, last year, Culic et al. demonstrated the potential of Windows 10 IoT Core (Redmond, WA, United States), a light-weight version of Windows 10, as an IoT operating system optimized to run on small devices that have no display [254].
On the hardware side, some authors’ keywords detail the hardware devices employed (see Figure 8b). Raspberry Pi is a small single board computer (SBC) capable of supporting operating systems like Linux Ubuntu, Windows, or Android. The Raspberry Pi is the most popular platform employed for IoT, with 88 publications, as a versatile platform for a gateway [255,256,257] or monitoring system [258,259]. Arduino boards are single-board microcontroller kits, in which the developer connects sensors, actuators, or RF communication interface easily using shield boards. For this specific IoT publications data set, 83 documents refer to Arduino hardware. These boards are widely used for IoT learning [253,260,261] and monitoring devices [262,263,264].
Presently, smartphones are highly capable embedded systems that run full OS, with integrated sensors. Sixty-six documents were found related to smartphones in IoT, be they used as sensors [265,266], gateway [267,268,269], or user interface [270,271]. The Field-Programmable Gate Array (FPGA) is a hardware reconfigurable component that contains an array of computational (logic) elements, with a functionality specified by a hardware description language [272]. These FPGAs are used in IoT investigations for data encryption [273,274,275], routing algorithms [276], and parallel simulation [277]. A microcontroller (MCU) is a small computer on a single integrated circuit, which includes a processor core, RAM/ROM memory, peripherals, and, in some cases, RF transceivers. For IoT, the MCU plays a fundamental role in sensing end devices [255,278,279] and actuators [280].

4.5. Top Trending Topics

For this analysis, the top trending topics are the authors’ keywords, which have higher average growth rate (AGR) over the others. These topics represent concepts that have a large impact on IoT research. To find these trending topics, two-year AGR time periods (2011–2012, 2013–2014, and 2015–2016) were found using the following Equation (1):
A G R = ∑ i = Y s Y e P i − P i − 1 ( Y e − Y s ) + 1 ,
where:
  • A G R = Average growth rate;
  • Y s = Start year;
  • Y e = End year;
  • P i = Number of publications on year i .
Figure 9 shows the top eight trending topics with the AGR time periods. Cloud computing leads, with an AGR of 90 publications/year for 2015–2016, 284 documents on 2016, and a constant growth in all time periods. In 2013, Gubbi et al. mentioned that the integration of IoT with Cloud computing applications can enable the creation of smart environments such as Smart Cities and others [13]. The growth of publications about IoT related to Cloud computing shows that the mentioned integration is currently happening. The second trending topic on this list is security. This topic has a moderate growth in 2011–2012 and 2013–2014 periods (about 18 publications/years), but, in 2015–2016, its growth soared to 83 publications/year. Security backs the industry’s concerns about the user privacy and confidentiality [281,282]. The same way that the communications protocols were analyzed in this paper by layers, Jing et al. divided the IoT into three layers (perception, transportation, and application layers) to analyze features and security issues of each [283].
IoT is one of many applications of Big Data because the rapid growth of IoT devices further propels the sharp growth of data to be processed and analyzed [70]. Wireless sensors networks (WSN), such as RFID, is one of the most important technologies enabling the IoT [284]. Likewise, security/privacy is a trending topic and also a concern in IoT, which was well summarized in [285].
Industry 4.0 is a new trending topic, without any growth in the 2011–2012 period, but with a sharp rise in publications from 3 to 66 in 2014 to 2016. Industry 4.0 includes the use of intelligent manufacturing processes, Cyber-Physical Systems (CPSs), and implementation and operation of smart factories [286]. This fourth industrial revolution aims to integrate IoT technologies such as remote control, manufacturing analytic tools and services, supply chains integration, and tracking and tracing inter- and intra-plant logistics [287]. Fog computing is also a new trending topic, with a small growth in 2011–2014 periods, but a large increase in the 2015–2016 period. Fog is a platform that provides compute, storage and networking services between end devices and cloud computing servers, most commonly, but not exclusively, located at the edge of network [60]. For IoT, this new platform is aimed to decentralize the data processing [288], decrease the latency [289], and bring more reliability for WSN [290]. Finally, Software-Defined Networking (SDN) is a novel concept for IoT, without any growth in the 2011–2012 period, but which was gaining popularity from one publication in 2013, and 2014, to 33 in 2016. SDN brings network routing intelligence via a centralized controller that connects to the network switch through the OpenFlow protocol [291], for example. This allows efficient node mobility [292], resource management [293], and improves the security of IoT networks [291,294,295].

5. Conclusions

A scientometrics review about Internet of Things was performed over a data set of 19,035 documents published during a period of 15 years (2002–2016) from two databases (Clarivate Web of Science and Scopus). A Python script called ScientoPy was developed to make a quantitative analysis of this data set, providing insight into research trends by investigating primary author’s country affiliation, most published authors, and prevalence of various research topics. Using authors’ keywords, the top research topics for IoT were found, including applications, communication protocols, software processing, hardware, and operating systems.
Analysis by country affiliation of the primary author shows a major increase in the number of publications for IoT in countries where the government has possibly implemented polices that improve the development of IoT. Similarly, this increase has shown in countries where private initiatives could have launched commercial low cost IoT networks (such as LoRa, Sigfox, etc.). In addition, prototype IoT infrastructure on small test environments, such as universities, creates microcosms that foster investigations for different IoT applications.
From 2014 to 2016, there was a sharp growth of smart environments including smart city, home, grids, and other surfaces with technology incorporating Big Data and cloud computing into IoT devices. Nevertheless, security [91,92,93] and privacy [103,106,107] are major concerns for many applications such as smart home and grid. Cyber-Physical Systems is an application that, when powered by the IoT, enables the targets fixed by Industry 4.0 [111]. Trends in communications protocols have changed in the last few years. The RFID publications sigmoid growth is on a stationary phase, while other media layer protocols such as BLE, WiFi, and 5G are on sigmoid growth exponential phase. Host layer protocols show a high growth rate for CoAP and MQTT. Software data processing demonstrates that the techniques designed to work with Big Data are growing on the sigmoid exponential phase for IoT data processing environments.
For operating systems, Android has become the most used OS for scientific researchers on IoT. This OS has been used for IoT gateways [238,239,240,241] and user interface in IoT devices [270,271]. Contiki is growing in a sigmoid exponential phase with its integrated protocol stack and WSN simulator, Cooja. Similarly, in IoT research, Raspberry Pi and Arduino are the most popular platforms for learning and development. In addition, the combination is widely used wherein Raspberry Pi is an IoT gateway [255,256,257] and Arduino boards serve as edge monitoring devices [262,263,264]. Similarly, smartphones exhibit their versatility being used as gateway [267,268,269], user interface [270,271], and sensing [265,266] IoT devices. Meanwhile, FPGAs have exhibited a sigmoid exponential phase growth in the last two years, with applications such as data encryption [273,274,275], routing algorithms optimization [276], and parallel networks simulation [277].
Top trending topics demonstrate that cloud integration with IoT devices is enabling the implementation of smart environments. Nevertheless, security and privacy in these environments are important growing concerns for IoT researchers and industry. WSNs are one of the most utilized technologies enabling the IoT. Furthermore, Fog computing has emerged as a promising edge device to decentralize data processing, decrease latency, and bring more reliability for WSN in IoT. Likewise, research on Software Defined Networks (SDN) grew rapidly during the last year, offering more efficient nodes, mobility, resources management, and improved security of IoT networks. The related trending topics offer unique opportunities for IoT innovations and start-ups in pursuit of an efficient, secure, and reliable IoT environment.

Acknowledgments

This research is funded by Colciencias Doctoral scholarship 647-2014 for the Ph.D. in Telematic Engineering at the Universidad del Cauca, Popayán, Colombia.

Author Contributions

Juan Ruiz-Rosero, Gustavo Ramirez-Gonzalez, and Rahul Khanna proposed the concept of this research. Jennifer M. Williams and Huaping Liu contributed for the state of art and final paper draft revisions. Greeshma Pisharody and Rahul Kahanna contributed to analize the data and to revise the drafts of the paper. Juan Ruiz-Rosero and Gustavo Ramirez-Gonzalez desiged, build, and validate the ScientoPy script. Juan Ruiz-Rosero, Gustavo Ramirez-Gonzalez, Rahul Khanna and Jennifer M. Williams wrote the paper.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Williams, J.M.; Khanna, R.; Ruiz-Rosero, J.P.; Pisharody, G.; Qian, Y.; Carlson, C.R.; Liu, H.; Ramirez-Gonzalez, G. Weaving the Wireless Web: Toward a Low-Power, Dense Wireless Sensor Network for the Industrial IoT. IEEE Microw. Mag. 2017, 18, 40–63. [Google Scholar] [CrossRef]
  2. Hiremath, S.; Yang, G.; Mankodiya, K. Wearable Internet of Things: Concept, Architectural Components and Promises for Person-Centered Healthcare; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2015; pp. 304–307. [Google Scholar]
  3. Williams, J.M.; Ruiz-Rosero, J.P.; Ramirez-Gonzalez, G.; Khanna, R.; Pisharody, G.; Qian, Y.; Wang, J.; Carlson, C.R.; Liu, H. Enabling Densely-Scalable Low-Power WSNs for Shipping and Industrial IoT; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2017. [Google Scholar]
  4. Gerla, M.; Lee, E.K.; Pau, G.; Lee, U. Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds; IEEE Computer Society: Washington, DC, USA, 2014; pp. 241–246. [Google Scholar]
  5. Bi, Z.; Xu, L.; Wang, C. Internet of Things for enterprise systems of modern manufacturing. IEEE Trans. Ind. Inf. 2014, 10, 1537–1546. [Google Scholar]
  6. Spanò, E.; Niccolini, L.; Pascoli, S.; Iannaccone, G. Last-meter smart grid embedded in an internet-of-things platform. IEEE Trans. Smart Grid 2015, 6, 468–476. [Google Scholar] [CrossRef]
  7. IBM. Redefining Boundaries: Insights from the Global C-Suite Study. IBM Institute for Business Value, IBM Corporation. 2015. Available online: https://public.dhe.ibm.com/common/ssi/ecm/gb/en/gbe03695usen/GBE03695USEN.PDF (accessed on 28 August 2017).
  8. Garcia-Sanchez, A.J.; Garcia-Sanchez, F.; Garcia-Haro, J. wireless sensor network deployment for integrating video-surveillance and data-monitoring in precision agriculture over distributed crops. Comput. Electron. Agric. 2011, 75, 288–303. [Google Scholar] [CrossRef]
  9. Buratti, C.; Stajkic, A.; Gardasevic, G.; Milardo, S.; Abrignani, M.D.; Mijovic, S.; Morabito, G.; Verdone, R. Testing Protocols for the Internet of Things on the EuWIn Platform. IEEE Int. Things J. 2016, 3, 124–133. [Google Scholar] [CrossRef]
  10. Ruiz-Rosero, J.; Ramirez-Gonzalez, G. Firmware architecture to support Plug and Play sensors for IoT environment. In Proceedings of the VII Congreso Iberoamericano de Telemática CITA 2015, Popayán, Colombia, 10–12 June 2015. [Google Scholar]
  11. Khanna, R.; Liu, H.; Rangarajan, T. Wireless Data Center Management: Sensor Network Applications and Challenges. IEEE Microw. Mag. 2014, 15, S45–S60. [Google Scholar] [CrossRef]
  12. Atzori, L.; Iera, A.; Morabito, G. The Internet of Things: A survey. Comput. Netw. 2010, 54, 2787–2805. [Google Scholar] [CrossRef]
  13. Gubbi, J.; Buyya, R.; Marusic, S.; Palaniswami, M. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Gener. Comput. Syst. 2013, 29, 1645–1660. [Google Scholar] [CrossRef]
  14. Borgia, E. The internet of things vision: Key features, applications and open issues. Comput. Commun. 2014, 54, 1–31. [Google Scholar] [CrossRef]
  15. Yan, B.N.; Lee, T.S.; Lee, T.P. Mapping the intellectual structure of the Internet of Things (IoT) field (2000–2014): A co-word analysis. Scientometrics 2015, 105, 1285–1300. [Google Scholar] [CrossRef]
  16. Mishra, D.; Gunasekaran, A.; Childe, S.; Papadopoulos, T.; Dubey, R.; Wamba, S. Vision, applications and future challenges of Internet of Things: A bibliometric study of the recent literature. Ind. Manag. Data Syst. 2016, 116, 1331–1355. [Google Scholar] [CrossRef]
  17. Leydesdorff, L.; Milojevic, S. Scientometrics. arXiv, 2012; arXiv:1208.4566v2. [Google Scholar]
  18. Schoenberger, C.R.; Upbin, B. The Internet of Things. Forbes Mag. 2002, 169, 155–160. [Google Scholar]
  19. Qiu, R.; Zhang, Z. Design of enterprise web servers in support of instant information retrievals. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Washington, DC, USA, 8 October 2003; Volume 3, pp. 2661–2666. [Google Scholar]
  20. Traversat, B.; Abdelaziz, M.; Doolin, D.; Duigou, M.; Hugly, J.C.; Pouyoul, E. Project JXTA-C: Enabling a Web of Things. 2003. Available online: http://ieeexplore.ieee.org/document/1174816/ (accessed on 10 August 2017).
  21. Gershenfeld, N.; Krikorian, R.; Cohen, D. The internet of things. Sci. Am. 2004, 291, 76–81. [Google Scholar] [CrossRef] [PubMed]
  22. Luckett, D. The supply chain. BT Technol. J. 2004, 22, 50–55. [Google Scholar] [CrossRef]
  23. Böse, F.; Lampe, W. Adoption of RFID in logistics. IBIMA 2005, 1, 62–65. [Google Scholar]
  24. Norman, A. Leveraging Radio Frequency Identification (RFID) Technology for Halal Tracking Tag; Universiti Malaya: Kuala Lumpur, Malaysia, 2005; Volume 1. [Google Scholar]
  25. Djassemi, M.; Singh, J. The use of RFID in manufacturing and packaging technology laboratories. In Proceedings of the Thirty-Third North American Manufacturing Research Conference, New York, NY, USA, 23–27 May 2005; Volume TP05PUB223. [Google Scholar]
  26. Pujolle, G. An Autonomic-oriented Architecture for the Internet of Things. In Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA’06), Sofia, Bulgaria, 3–6 October 2006; pp. 163–168. [Google Scholar]
  27. Bernard, G. Invited Paper: Middleware for Next Generation Distributed Systems: Main Challenges and Perspectives. In Proceedings of the 17th International Workshop on Database and Expert Systems Applications (DEXA’06), Krakow, Poland, 4–8 September 2006; pp. 237–240. [Google Scholar]
  28. Adelmann, R.; Langheinrich, M.; Flörkemeier, C. Toolkit for Bar Code Recognition and Resolving on Camera Phones—Jump Starting the Internet of Things. In Proceedings of the Workshop Mobile and Embedded Interactive Systems (MEIS 2006) at Informatik, Dresden, Germany, October 2006. [Google Scholar]
  29. Lehtonen, M.; Michahelles, F.; Staake, T.; Fleisch, E. Strengthening the Security of Machine Readable Documents by Combining RFID and Optical Memory Devices; Springer: Paris, France, 2006; pp. 77–92. [Google Scholar]
  30. Futatsumori, S.; Kono, T.; Hikage, T.; Nojima, T.; Koike, B. Experimental Test System to Assess the EMI From RFID Reader/Writer on Implantable Cardiac Pacemaker. 2006, pp. 258–261. Available online: https://www.scopus.com/record/display.uri?eid=2-s2.0-84901711947&origin=inward (accessed on 10 August 2017).
  31. Broll, G.; Rukzio, E.; Paolucci, M.; Wagner, M.; Schmidt, A.; Hussmann, H. Perci: Pervasive Service Interaction with the Internet of Things. IEEE Int. Comput. 2009, 13, 74–81. [Google Scholar] [CrossRef]
  32. Hvistendahl, M. Information Technology China Pushes the ‘Internet of Things’. Science 2012, 336, 1223. [Google Scholar] [CrossRef] [PubMed]
  33. Zhu, Q.; Wang, R.; Chen, Q.; Liu, Y.; Qin, W. IOT gateway: Bridging wireless sensor networks into Internet of Things. In Proceedings of the 2010 IEEE/IFIP 8th International Conference on Embedded and Ubiquitous Computing (EUC), Hong Kong, China, 11–13 December 2010; pp. 347–352. [Google Scholar]
  34. The Economic Times. Internet of Things to Grow Rapidly in India by 2020: Report. 2017. Available online: http://economictimes.indiatimes.com/articleshow/57129635.cms (accessed on 28 July 2017).
  35. BBC. South Korea Launches First Internet of Things Network. 2016. Available online: http://www.bbc.com/news/technology-36710667 (accessed on 25 July 2017).
  36. Estopace, E. IDC Sees Gov’t Use of IoT in Indonesia by 2019. 2017. Available online: https://www.enterpriseinnovation.net/article/idc-sees-govt-use-iot-indonesia-2019-1967761722 (accessed on 25 July 2017).
  37. Telecompaper. Xeelas, Sade to Build IoT Network in Turkey. 2017. Available online: https://www.telecompaper.com/news/xeelas-sade-to-build-iot-network-in-turkey--1189559 (accessed on 25 July 2017).
  38. Research an Markets. Russia Internet of Things (IoT) Market Size, Demand, Opportunity & Growth Outlook 2023. 2017. Available online: https://www.researchandmarkets.com/research/xmn4pm/russia_internet (accessed on 25 July 2017).
  39. Ignite National Technology Fund. Internet of Things: It’s Happening! 2017. Available online: https://www.ictrdf.org.pk/blog/2017/01/23/internet-of-things-its-happening-2/ (accessed on 25 July 2017).
  40. Yu, R.; Zhang, Y.; Gjessing, S.; Xia, W.; Yang, K. Toward Cloud-Based Vehicular Networks with Efficient Resource Management. IEEE Netw. 2013, 27, 48–55. [Google Scholar] [CrossRef]
  41. Li, Y.; Hou, M.; Liu, H.; Liu, Y. Towards a theoretical framework of strategic decision, supporting capability and information sharing under the context of Internet of Things. Inf. Technol. Manag. 2012, 13, 205–216. [Google Scholar] [CrossRef]
  42. Wang, Y.; Mao, S.; Nelms, R.M. Distributed Online Algorithm for Optimal Real-Time Energy Distribution in the Smart Grid. IEEE Int. Things J. 2014, 1, 70–80. [Google Scholar] [CrossRef]
  43. Su, K.; Li, J.; Fu, H. Smart city and the applications. In Proceedings of the 2011 International Conference on Electronics, Communications and Control (ICECC), Ningbo, China, 9–11 September 2011; pp. 1028–1031. [Google Scholar]
  44. Zhang, J.; Iannucci, B.; Hennessy, M.; Gopal, K.; Xiao, S.; Kumar, S.; Pfeffer, D.; Aljedia, B.; Ren, Y.; Griss, M.; et al. Sensor Data as a Service—A Federated Platform for Mobile Data-centric Service Development and Sharing. In Proceedings of the 2013 IEEE International Conference on Services Computing, Santa Clara, CA, USA, 28 June–3 July 2013; pp. 446–453. [Google Scholar]
  45. Wang, J.; Katabi, D. Dude, where’s my card? RFID positioning that works with multipath and non-line of sight. In Proceedings of the ACM SIGCOMM 2013 Conference, Hong Kong, China, 12–16 August 2013; pp. 51–62. [Google Scholar]
  46. Li, B.H.; Zhang, L.; Wang, S.L.; Tao, F.; Cao, J.W.; Jiang, X.D.; Song, X.; Chai, X.D. Cloud manufacturing: A new service-oriented networked manufacturing model. Comput. Integr. Manuf. Syst. 2010, 16, 1–7. [Google Scholar]
  47. Li, W.; Zhong, Y.; Wang, X.; Cao, Y. Resource virtualization and service selection in cloud logistics. J. Netw. Comput. Appl. 2013, 36, 1696–1704. [Google Scholar] [CrossRef]
  48. Chen, Y.; Han, F.; Yang, Y.H.; Ma, H.; Han, Y.; Jiang, C.; Lai, H.Q.; Claffey, D.; Safar, Z.; Liu, K. Time-reversal wireless paradigm for green internet of things: An overview. IEEE Int. Things J. 2014, 1, 81–98. [Google Scholar] [CrossRef]
  49. Jara, A.J.; Zamora, M.A.; Skarmeta, A.F.G. An internet of things-based personal device for diabetes therapy management in ambient assisted living (AAL). Pers. Ubiquitous Comput. 2011, 15, 431–440. [Google Scholar] [CrossRef]
  50. Yang, J.; Wang, Z.; Zhang, X. An iBeacon-based indoor positioning systems for hospitals. Int. J. Smart Home 2015, 9, 161–168. [Google Scholar] [CrossRef]
  51. Li, H.; Gao, B.; Chen, Z.; Zhao, Y.; Huang, P.; Ye, H.; Liu, L.; Liu, X.; Kang, J. A learnable parallel processing architecture towards unity of memory and computing. Sci. Rep. 2015, 5. [Google Scholar] [CrossRef] [PubMed]
  52. Qin, Y.; Sheng, Q.; Falkner, N.; Dustdar, S.; Wang, H.; Vasilakos, A. When things matter: A survey on data-centric internet of things. J. Netw. Comput. Appl. 2016, 64, 137–153. [Google Scholar] [CrossRef]
  53. Li, X.; Lu, R.; Liang, X.; Shen, X.; Chen, J.; Lin, X. Smart community: An internet of things application. IEEE Commun. Mag. 2011, 49, 68–75. [Google Scholar] [CrossRef]
  54. Sun, Q.B.; Liu, J.; Li, S.; Fan, C.X.; Sun, J.J. Internet of Things: Summarize on concepts, architecture and key technology problem. J. Beijing Univ. Posts Telecommun. 2010, 33, 1–9. [Google Scholar]
  55. Lim, S.; Son, D.; Kim, J.; Lee, Y.; Song, J.K.; Choi, S.; Lee, D.; Kim, J.; Lee, M.; Hyeon, T.; Kim, D.H. Transparent and stretchable interactive human machine interface based on patterned graphene heterostructures. Adv. Funct. Mater. 2015, 25, 375–383. [Google Scholar] [CrossRef]
  56. Guo, B.; Zhang, D.; Wang, Z.; Yu, Z.; Zhou, X. Opportunistic IoT: Exploring the harmonious interaction between human and the internet of things. J. Netw. Comput. Appl. 2013, 36, 1531–1539. [Google Scholar] [CrossRef]
  57. Li, J.; Liu, X. An important aspect of big data: Data usability. Comput. Res. Dev. 2013, 50, 1147–1162. [Google Scholar]
  58. Hong, S.; Kim, D.; Ha, M.; Bae, S.; Park, S.; Jung, W.; Kim, J.E. SNAIL: An IP-based wireless sensor network approach to the Internet of things. IEEE Wirel. Commun. 2010, 17, 34–42. [Google Scholar] [CrossRef]
  59. Miorandi, D.; Sicari, S.; De Pellegrini, F.; Chlamtac, I. Internet of Things: Vision, applications and research challenges. Ad Hoc Netw. 2012, 10, 1497–1516. [Google Scholar] [CrossRef]
  60. Bonomi, F.; Milito, R.; Zhu, J.; Addepalli, S. Fog computing and its role in the internet of things. In Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, Helsinki, Finland, 17 August 2012; pp. 13–15. [Google Scholar]
  61. Tao, F.; Zhang, L.; Venkatesh, V.; Luo, Y.; Cheng, Y. Cloud manufacturing: A computing and service-oriented manufacturing model. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2011, 225, 1969–1976. [Google Scholar] [CrossRef]
  62. Tan, L.; Wang, N. Future Internet: The Internet of Things. In Proceedings of the 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), Chengdu, China, 20–22 August 2010; Volume 5, pp. V5376–V5380. [Google Scholar]
  63. Meng, X.; Ci, X. Big data management: Concepts, techniques and challenges. Comput. Res. Dev. 2013, 50, 146–169. [Google Scholar]
  64. Aziz, A.; Şekercioǧlu, Y.; Fitzpatrick, P.; Ivanovich, M. A survey on distributed topology control techniques for extending the lifetime of battery powered wireless sensor networks. IEEE Commun. Surv. Tutor. 2013, 15, 121–144. [Google Scholar] [CrossRef]
  65. Kortuem, G.; Kawsar, F.; Sundramoorthy, V.; Fitton, D. Smart objects as building blocks for the internet of things. IEEE Int. Comput. 2010, 14, 44–51. [Google Scholar] [CrossRef]
  66. Ganti, R.; Ye, F.; Lei, H. Mobile crowdsensing: Current state and future challenges. IEEE Commun. Mag. 2011, 49, 32–39. [Google Scholar] [CrossRef]
  67. Bobadilla, J.; Ortega, F.; Hernando, A.; Gutiérrez, A. Recommender systems survey. Knowl. Based Syst. 2013, 46, 109–132. [Google Scholar] [CrossRef]
  68. Perera, C.; Zaslavsky, A.; Christen, P.; Georgakopoulos, D. Context aware computing for the internet of things: A survey. IEEE Commun. Surv. Tutor. 2014, 16, 414–454. [Google Scholar] [CrossRef]
  69. Zanella, A.; Bui, N.; Castellani, A.; Vangelista, L.; Zorzi, M. Internet of Things for smart cities. IEEE Int. Things J. 2014, 1, 22–32. [Google Scholar] [CrossRef]
  70. Chen, M.; Mao, S.; Liu, Y. Big data: A survey. Mob. Netw. Appl. 2014, 19, 171–209. [Google Scholar] [CrossRef]
  71. Spiess, P.; Karnouskos, S.; Guinard, D.; Savio, D.; Baecker, O.; Souza, L.; Trifa, V. Soa-based integration of the internet of things in enterprise services. In Proceedings of the IEEE International Conference on Web Services, Los Angeles, CA, USA, 6–10 July 2009; pp. 968–975. [Google Scholar]
  72. Mainetti, L.; Patrono, L.; Vilei, A. Evolution of wireless sensor networks towards the Internet of Things: A survey. In Proceedings of the 2011 19th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia, 15–17 September 2011; pp. 16–21. [Google Scholar]
  73. Dohr, A.; Modre-Osprian, R.; Drobics, M.; Hayn, D.; Schreier, G. The internet of things for ambient assisted living. In Proceedings of the 2010 Seventh International Conference on Information Technology: New Generations (ITNG), Las Vegas, NV, USA, 12–14 April 2010; pp. 804–809. [Google Scholar]
  74. Kovatsch, M.; Duquennoy, S.; Dunkels, A. A low-power CoAP for Contiki. In Proceedings of the 2011 IEEE 8th International Conference on Mobile Adhoc and Sensor Systems (MASS), Valencia, Spain, 17–22 October 2011; pp. 855–860. [Google Scholar]
  75. Khan, R.; Khan, S.; Zaheer, R.; Khan, S. Future internet: The internet of things architecture, possible applications and key challenges. In Proceedings of the 2012 10th International Conference on Frontiers of Information Technology (FIT), Islamabad, India, 17–19 December 2012; pp. 257–260. [Google Scholar]
  76. Domingo, M. An overview of the Internet of Things for people with disabilities. J. Netw. Comput. Appl. 2012, 35, 584–596. [Google Scholar] [CrossRef]
  77. Hancke, G.; de Silva, B.; Hancke, G., Jr. The role of advanced sensing in smart cities. Sensors 2013, 13, 393–425. [Google Scholar] [CrossRef] [PubMed]
  78. Wang, Z.; Yang, R.; Zhou, J.; Qin, Y.; Xu, C.; Hu, Y.; Xu, S. Lateral nanowire/nanobelt based nanogenerators, piezotronics and piezo-phototronics. Mater. Sci. Eng. R Rep. 2010, 70, 320–329. [Google Scholar] [CrossRef]
  79. Wang, S.; Lin, L.; Wang, Z. Triboelectric nanogenerators as self-powered active sensors. Nano Energy 2015, 11, 436–462. [Google Scholar] [CrossRef]
  80. Keoh, S.; Kumar, S.; Tschofenig, H. Securing the internet of things: A standardization perspective. IEEE Int. Things J. 2014, 1, 265–275. [Google Scholar] [CrossRef]
  81. Malhotra, A.; Melville, N.; Watson, R. Spurring impactful research on information systems for environmental sustainability. MIS Q. Manag. Inf. Syst. 2013, 37, 1265–1274. [Google Scholar] [CrossRef]
  82. Zhang, J.; Yu, Q.; Zheng, F.; Long, C.; Lu, Z.; Duan, Z. Comparing keywords plus of WOS and author keywords: A case study of patient adherence research. J. Assoc. Inf. Sci. Technol. 2016, 67, 967–972. [Google Scholar] [CrossRef]
  83. Sun, Y.; Song, H.; Jara, A.J.; Bie, R. Internet of Things and Big Data Analytics for Smart and Connected Communities. IEEE Access 2016, 4, 766–773. [Google Scholar] [CrossRef]
  84. Li, D.; Yao, Y.; Shao, Z. Big data in smart city. Geomat. Inf. Sci. Wuhan Univ. 2014, 39, 631–640. [Google Scholar]
  85. Moreno-Cano, V.; Terroso-Saenz, F.; Skarmeta-Gomez, A. Big Data for IoT Services in Smart Cities; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2015; pp. 418–423. [Google Scholar]
  86. Gómez Romero, C.D.; Díaz Barriga, J.K.; Rodríguez Molano, J.I. Big Data Meaning in the Architecture of IoT for Smart Cities. In Proceedings of the First International Conference on Data Mining and Big Data (DMBD 2016), Bali, Indonesia, 25–30 June 2016; Tan, Y., Shi, Y., Eds.; Springer: Cham, Switzerland, 2016; pp. 457–465. [Google Scholar]
  87. Kaur, M.; Maheshwari, P. Building Smart Cities Applications Using IoT and Cloud-Based Architectures; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016. [Google Scholar]
  88. Chang, C.I.; Lo, C.C. Planning and Implementing a Smart City in Taiwan. IT Prof. 2016, 18, 42–49. [Google Scholar] [CrossRef]
  89. Longo, M.; Zaninelli, D.; Roscia, M.; Costoiu, M. Smart City to Improve Power Quality; IEEE Computer Society: Washington, DC, USA, 2014; pp. 458–462. [Google Scholar]
  90. Longo, M.; Roscia, M.; Lazaroiu, G.; Zaninelli, D. The spread of smart city and power quality requirements. UPB Sci. Bull. Ser. C Electr. Eng. Comput. Sci. 2015, 77, 281–292. [Google Scholar]
  91. Rehman, S.U.; Manickam, S. A Study of Smart Home Environment and its Security Threats. Int. J. Reliab. Qual. Saf. Eng. 2016, 23. [Google Scholar] [CrossRef]
  92. Alohali, B.; Merabti, M.; Kifayat, K. A Secure Scheme for a Smart House Based on Cloud of Things (CoT); Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2014; pp. 115–120. [Google Scholar]
  93. Peter, S.; Gopal, R. Multi-Level Authentication System for Smart Home-Security Analysis and Implementation; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2017; Volume 2. [Google Scholar]
  94. Peng, Y.; Jiang, L.; Gang, W.; Wang, X. Smart home system based on zigbee wireless sensor network. Rev. Tec. Fac. Ing. Univ. Zulia 2016, 39, 335–341. [Google Scholar]
  95. Yiqi, W.; Lili, H.; Chengquan, H.; Yan, G.; Zhangwei, Z. A Zigbee-Based Smart Home Monitoring System; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2014; pp. 114–117. [Google Scholar]
  96. Gong, S.F.; Yin, X.Q. Solution of Home Security Based on ARM and ZigBee; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 89–91. [Google Scholar]
  97. Cicirelli, F.; Fortino, G.; Giordano, A.; Guerrieri, A.; Spezzano, G.; Vinci, A. On the Design of Smart Homes: A Framework for Activity Recognition in Home Environment. J. Med. Syst. 2016, 40. [Google Scholar] [CrossRef] [PubMed]
  98. Bourobou, S.; Yoo, Y. User activity recognition in smart homes using pattern clustering applied to temporal ANN algorithm. Sensors 2015, 15, 11953–11971. [Google Scholar] [CrossRef] [PubMed]
  99. Fortino, G.; Giordano, A.; Guerrieri, A.; Spezzano, G.; Vinci, A. A Data Analytics Schema for Activity Recognition in Smart Home Environments. In Ubiquitous Computing and Ambient Intelligence, Proceedings of the 9th International Conference on Sensing, Processing, and Using Environmental Information (UCAmI 2015), Puerto Varas, Chile, 1–4 December 2015; García-Chamizo, J.M., Fortino, G., Ochoa, S.F., Eds.; Springer: Cham, Switzerland, 2015; pp. 91–102. [Google Scholar]
  100. Fang, X.; Misra, S.; Xue, G.; Yang, D. Smart grid—The new and improved power grid: A survey. IEEE Commun. Surv. Tutor. 2012, 14, 944–980. [Google Scholar] [CrossRef]
  101. Noll, J.; Garitano, I.; Fayyad, S.; Åsberg, E.; Abie, H. Measurable security, privacy and dependability in smart grids. J. Cyber Secur. Mob. 2014, 3, 371–398. [Google Scholar] [CrossRef]
  102. Pitas, C.; Tsirakis, C.; Zotou, E.; Panagopoulos, A. Emerging communication technologies and security challenges in a smart grid wireless ecosystem. Int. J. Wirel. Mob. Comput. 2014, 7, 231–245. [Google Scholar] [CrossRef]
  103. Dalipi, F.; Yayilgan, S. Security and Privacy Considerations for Iot Application on Smart Grids: Survey and Research Challenges; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 63–68. [Google Scholar]
  104. Meloni, A.; Atzori, L. A Cloud-Based and Restful Internet of Things Platform to Foster Smart Grid Technologies Integration and Re-Usability; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 387–392. [Google Scholar]
  105. Wang, G.; Li, B.; Hu, Z.; Song, Y. Challenges and future evolution of control center under smart grid environment. Power Syst. Technol. 2011, 35, 1–5. [Google Scholar]
  106. Beligianni, F.; Alamaniotis, M.; Fevgas, A.; Tsompanopoulou, P.; Bozanis, P.; Tsoukalas, L. An Internet of Things Architecture for Preserving Privacy of Energy Consumption; Institution of Engineering and Technology: Stevenage, UK, 2016; Volume 2016. [Google Scholar]
  107. Winter, J. Citizen perspectives on the customization/privacy paradox related to smart meter implementation. Int. J. Technoethics 2015, 6, 45–59. [Google Scholar] [CrossRef]
  108. Dumitrache, L. The next generation of cyber-physical systems. Control Eng. Appl. Inf. 2010, 12, 3–4. [Google Scholar]
  109. Jazdi, N. Cyber Physical Systems in the Context of Industry 4.0; IEEE Computer Society: Washington, DC, USA, 2014. [Google Scholar]
  110. Pisching, M.A.; Junqueira, F.; Filho, D.J.D.S.; Miyagi, P.E. An architecture based on IoT and CPS to organize and locate services. In Proceedings of the 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), Berlin, Germany, 6–9 September 2016; pp. 1–4. [Google Scholar]
  111. Zhou, K.; Liu, T.; Liang, L. From cyber-physical systems to Industry 4.0: Make future manufacturing become possible. Int. J. Manuf. Res. 2016, 11, 167–188. [Google Scholar] [CrossRef]
  112. Mosterman, P.; Zander, J. Industry 4.0 as a Cyber-Physical System study. Softw. Syst. Model. 2016, 15, 17–29. [Google Scholar] [CrossRef]
  113. Jara, A.; Genoud, D.; Bocchi, Y. Big Data for Cyber Physical Systems an Analysis of Challenges, Solutions and Opportunities; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2014; pp. 376–380. [Google Scholar]
  114. Lee, J.; Bagheri, B.; Jin, C. Introduction to cyber manufacturing. Manuf. Lett. 2016, 8, 11–15. [Google Scholar] [CrossRef]
  115. O’Donovan, P.; Leahy, K.; Bruton, K.; O’Sullivan, D. Big data in manufacturing: A systematic mapping study. J. Big Data 2015, 2. [Google Scholar] [CrossRef]
  116. Vegh, L.; Miclea, L. Secure and Efficient Communication in Cyber-Physical Systems through Cryptography and Complex Event Processing; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 273–276. [Google Scholar]
  117. Mourtzis, D.; Vlachou, E. Cloud-based cyber-physical systems and quality of services. TQM J. 2016, 28, 704–733. [Google Scholar] [CrossRef]
  118. Watt, S.; Milne, C.; Bradley, D.; Russell, D.; Hehenberger, P.; Azorin-Lopez, J. Privacy Matters—Issues within Mechatronics. IFAC-PapersOnLine 2016, 49, 423–430. [Google Scholar] [CrossRef]
  119. Miranda, J.; Cabral, J.; Wagner, S.; Pedersen, C.; Ravelo, B.; Memon, M.; Mathiesen, M. An open platform for seamless sensor support in healthcare for the internet of things. Sensors 2016, 16. [Google Scholar] [CrossRef] [PubMed]
  120. Tsirbas, H.; Giokas, K.; Koutsouris, D. “Internet of Things“, an RFID—IPv6 scenario in a healthcare environment. In XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010; Springer: Berlin/Heidelberg, Germany, 2010; Volume 29, pp. 808–811. [Google Scholar]
  121. Sowmiya, E.; Malathi, L.; Selvi, A.T. A study on security issues in healthcare applications using medical wireless sensor network and IoT. IIOAB J. 2016, 7, 575–583. [Google Scholar]
  122. Ullah, K.; Shah, M.; Zhang, S. Effective Ways to Use Internet of Things in the Field of Medical And Smart Health Care; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 372–379. [Google Scholar]
  123. Chong, A.Y.L.; Liu, M.; Luo, J.; Keng-Boon, O. Predicting RFID adoption in healthcare supply chain from the perspectives of users. Int. J. Prod. Econ. 2015, 159, 66–75. [Google Scholar] [CrossRef]
  124. Shahamabadi, M.; Ali, B.; Noordin, N.; Rasid, M.; Varahram, P.; Jara, A. A NEMO-HWSN solution to support 6LoWPAN network mobility in hospital wireless sensor network. Comput. Sci. Inf. Syst. 2014, 11, 943–960. [Google Scholar] [CrossRef]
  125. Gia, T.; Thanigaivelan, N.; Rahmani, A.M.; Westerlund, T.; Liljeberg, P.; Tenhunen, H. Customizing 6lowpan Networks towards Internet-of-Things Based Ubiquitous Healthcare Systems; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2015. [Google Scholar]
  126. Romero, L.; Chatterjee, P.; Armentano, R. An IoT approach for integration of computational intelligence and wearable sensors for Parkinson’s disease diagnosis and monitoring. Health Technol. 2016, 6, 167–172. [Google Scholar] [CrossRef]
  127. Perez, M.; Mata, F.; Rodriguez, V.; Zhang, S. Pervasive Healthcare Monitoring System; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 1712–1716. [Google Scholar]
  128. Abdennadher, I.; Khabou, N.; Rodriguez, I.; Jmaiel, M. Designing Energy Efficient Smart Buildings in Ubiquitous Environments; IEEE Computer Society: Washington, DC, USA, 2016; pp. 122–127. [Google Scholar]
  129. Patti, E.; Acquaviva, A.; Jahn, M.; Pramudianto, F.; Tomasi, R.; Rabourdin, D.; Virgone, J.; MacIi, E. Event-Driven User-Centric Middleware for Energy-Efficient Buildings and Public Spaces. IEEE Syst. J. 2016, 10, 1137–1146. [Google Scholar] [CrossRef]
  130. Ferrández-Pastor, F.J.; Gómez-Trillo, S.; García-Chamizo, J.M.; Valdivieso-Sarabia, R. Developing a Context Aware System for Energy Management in Urban Areas. In Proceedings of the 10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2016), San Bartolomé de Tirajana, Gran Canaria, Spain, 29 November–2 December 2016; García, C.R., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A., Eds.; Springer: Cham, Switzerland, 2016. Part II. pp. 326–331. [Google Scholar]
  131. Magno, M.; Spadaro, L.; Singh, J.; Benini, L. Kinetic Energy Harvesting: Toward Autonomous Wearable Sensing for Internet of Things; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 248–254. [Google Scholar]
  132. Wang, Y.; Liu, Y.; Wang, C.; Li, Z.; Sheng, X.; Lee, H.; Chang, N.; Yang, H. Storage-Less and Converter-Less Photovoltaic Energy Harvesting with Maximum Power Point Tracking for Internet of Things. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2016, 35, 173–186. [Google Scholar] [CrossRef]
  133. Dong, Y.; Wang, J.; Shim, B.; Kim, D. DEARER: A Distance-And-Energy-Aware Routing with Energy Reservation for Energy Harvesting Wireless Sensor Networks. IEEE J. Sel. Areas Commun. 2016, 34, 3798–3813. [Google Scholar] [CrossRef]
  134. Zhang, S.G.; Liu, G.L.; Liu, X.; Wang, J.X. An energy-efficient and fast missing tag detection algorithm in large scale RFID systems. Chin. J. Comput. 2014, 37, 434–444. [Google Scholar]
  135. Colella, R.; Catarinucci, L.; Tarricone, L. EM Design of a Passive RFID-Based Device with Sensing and Reasoning Capabilities; IEEE Computer Society: Washington, DC, USA, 2015. [Google Scholar]
  136. Rafey, S.; Abdel-Hamid, A.; El-Nasr, M. CBSTM-IoT: Context-Based Social Trust Model for the Internet of Things; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016. [Google Scholar]
  137. Abdelghani, W.; Zayani, C.A.; Amous, I.; Sèdes, F. Trust Management in Social Internet of Things: A Survey. In Social Media: The Good, the Bad, and the Ugly, Proceedings of the 15th IFIP WG 6.11 Conference on E-Business, E-Services, and E-Society, Swansea, UK, 13–15 September 2016; Dwivedi, Y.K., Mäntymäki, M., Ravishankar, M., Janssen, M., Clement, M., Slade, E.L., Rana, N.P., Al-Sharhan, S., Simintiras, A.C., Eds.; Springer: Cham, Switzerland, 2016; pp. 430–441. [Google Scholar]
  138. Chen, I.R.; Guo, J.; Bao, F. Trust Management for SOA-Based IoT and Its Application to Service Composition. IEEE Trans. Serv. Comput. 2016, 9, 482–495. [Google Scholar] [CrossRef]
  139. Yao, L.; Sheng, Q.; Ngu, A.; Ashman, H.; Li, X. Exploring Recommendations in Internet of Things; Association for Computing Machinery: New York, NY, USA, 2014; pp. 855–858. [Google Scholar]
  140. Muñoz-Organero, M.; Ramíez-González, G.; Muñoz-Merino, P.; Delgado Kloos, C. A collaborative recommender system based on space-time similarities. IEEE Pervasive Comput. 2010, 9, 81–87. [Google Scholar] [CrossRef]
  141. Jurkovičová, L.; Červenka, P.; Hrivíková, T.; Hlavatý, I. E-Learning in Augmented Reality Utilizing iBeacon Technology; Academic Conferences Limited: Sonning Common, UK, 2015; pp. 170–178. [Google Scholar]
  142. Meda, P.; Kumar, M.; Parupalli, R. Mobile Augmented Reality Application for Telugu Language Learning; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2015; pp. 183–186. [Google Scholar]
  143. Rose, R.; Bhuvaneswari, G. Word Recognition Incorporating Augmented Reality for Linguistic E-Conversion; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 2106–2109. [Google Scholar]
  144. Pozza, R.; Nati, M.; Georgoulas, S.; Gluhak, A.; Moessner, K.; Krco, S. CARD: Context-Aware Resource Discovery for Mobile Internet of Things Scenarios; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2014. [Google Scholar]
  145. Thomas, A.; Shah, H.; Moore, P.; Rayson, P.; Wilcox, A.; Osman, K.; Evans, C.; Chapman, C.; Athwal, C.; While, D.; et al. E-Education 3.0: Challenges and Opportunities for the Future Of iCampuses. In Proceedings of the 2012 Sixth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), Palermo, Italy, 4–6 July 2012; pp. 953–958. [Google Scholar]
  146. Bhatti, Z.; Naqvi, N.; Ramakrishnan, A.; Preuveneers, D.; Berbers, Y. Learning distributed deployment and configuration trade-offs for context-aware applications in Intelligent Environments. J. Ambient Intell. Smart Environ. 2014, 6, 541–559. [Google Scholar]
  147. Yin, C.; David, B.; Chalon, R. A Contextual Mobile Learning System in Our Daily Lives And Professional Situations; Academic Conferences Limited: Sonning Common, UK, 2009; pp. 703–711. [Google Scholar]
  148. Muñoz-Organero, M.; Ramírez, G.A.; Muñoz-Merino, P.J.; Kloos, C.D. Framework for Contextualized Learning Ecosystems. In Towards Ubiquitous Learning, Proceedings of the 6th European Conference of Technology Enhanced Learning, Palermo, Italy, 20–23 September 2011; Kloos, C.D., Gillet, D., Crespo García, R.M., Wild, F., Wolpers, M., Eds.; Springer: Berlin/Heidelberg, Germany, 2011; pp. 260–270. [Google Scholar]
  149. Kavka, L.; Kodym, O.; Strakos, V. Logistics laboratory in education. In Proceedings of the 15th International Multidisciplinary Scientific GeoConference 2015 (SGEM 2015), Albena, Bulgaria, 18–24 June 2015; Volume 3, pp. 965–972. [Google Scholar]
  150. Ramírez-González, G.; Córdoba-Paladinez, C.; Sotelo-Torres, O.; Palacios, C.; Muñoz-Organero, M.; Delgado-Kloos, C. Pervasive learning activities for the LMS.LRN through Android mobile devices with NFC support. In Proceedings of the 2012 IEEE 12th International Conference on Advanced Learning Technologies (ICALT), Rome, Italy, 4–6 July 2012; pp. 672–673. [Google Scholar]
  151. González, G.; Organero, M.; Kloos, C. Early infrastructure of an Internet of Things in spaces for learning. In Proceedings of the Eighth IEEE International Conference on Advanced Learning Technologies, Santander, Spain, 1–5 July 2008; pp. 381–383. [Google Scholar]
  152. Stallings, W. Handbook of Computer-Communications Standards; Volume 1: The Open Systems Interconnection (OSI) Model and OSI-Related Standards; Macmillan Publishing Co., Inc.: Indianapolis, IN, USA, 1987. [Google Scholar]
  153. Yuan, J.; Xu, Y.; Gao, H. A new security authentication method in the internet of things based on PID. Int. J. Simul. Syst. Sci. Technol. 2016, 17, 9.1–9.6. [Google Scholar]
  154. Li, X.; Liu, C. A novel RFID authentication protocol support detecting cloned tags. Information 2012, 15, 4971–4976. [Google Scholar]
  155. Li, H.; Tang, S. Enhanced bidirectional authentication scheme for RFID communications in internet of things environment. Int. J. Simul. Syst. Sci. Technol. 2016, 17. [Google Scholar] [CrossRef]
  156. Hamza, K.; Amir, F. Evolutionary Clustering for Integrated WSN-RFID Networks; Association for Computing Machinery, Inc.: New York, NY, USA, 2016; pp. 267–272. [Google Scholar]
  157. Zhu, J.; Sun, N. Research on Integration of WSN and RFID Technology for Agricultural Product Inspection. In Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, Xi’an, China, 23–25 August 2012; pp. 908–911. [Google Scholar]
  158. Wu, D.; Du, J.; Zhu, D.; Wang, S. A Simple RFID-Based Architecture for Privacy Preservation; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2015; Volume 1, pp. 1224–1229. [Google Scholar]
  159. Burmester, M.; Munilla, J. Pre vs post state update: Trading privacy for availability in RFID. IEEE Wirel. Commun. Lett. 2014, 3, 317–320. [Google Scholar] [CrossRef]
  160. Yan, B.; Hu, D.; Shi, P. A traceable platform of aquatic foods supply chain based on RFID and EPC Internet of Things. Int. J. RF Technol. Res. Appl. 2012, 4, 55–70. [Google Scholar]
  161. Xu, H.; Wang, S.P.; Wang, R.C.; Wang, Z.Q. Efficient P2P-based mutual authentication protocol for RFID system security of EPC network using asymmetric encryption algorithm. J. China Univ. Posts Telecommun. 2011, 18, 40–47. [Google Scholar] [CrossRef]
  162. Castellani, A.; Rossi, M.; Zorzi, M. Back pressure congestion control for CoAP/6LoWPAN networks. Ad Hoc Netw. 2014, 18, 71–84. [Google Scholar] [CrossRef]
  163. Bimschas, D.; Kleine, O.; Pfisterer, D. Debugging the Internet of Things: A 6LoWPAN/CoAP Testbed Infrastructure. In Ad-hoc, Mobile, and Wireless Networks, Proceedings of the 11th International Conference, ADHOC-NOW 2012, Belgrade, Serbia, 9–11 July 2012; Li, X.Y., Papavassiliou, S., Ruehrup, S., Eds.; Springer: Berlin/Heidelberg, Germany, 2012; pp. 207–220. [Google Scholar]
  164. Pongle, P.; Chavan, G. A survey: Attacks on RPL and 6LoWPAN in IoT; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2015. [Google Scholar]
  165. Hellaoui, H.; Koudil, M. Bird Flocking Congestion Control for CoAP/RPL/6LoWPAN Networks; Association for Computing Machinery, Inc.: New York, NY, USA, 2015; pp. 25–30. [Google Scholar]
  166. Bragg, G.; Martinez, K.; Basford, P.; Hart, J. 868MHz 6LoWPAN with ContikiMAC for an Internet of Things Environmental Sensor Network; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 1273–1277. [Google Scholar]
  167. Caputo, D.; Mainetti, L.; Patrono, L.; Vilei, A. Implementation of the EXI schema on wireless sensor nodes using Contiki. In Proceedings of the 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), Palermo, Italy, 4–6 July 2012; pp. 770–774. [Google Scholar]
  168. Wang, K.K.; Dubey, S.; Rajamohan, A.; Salcic, Z. An Android-Based Mobile 6LoWPAN Network Architecture for Pervasive Healthcare; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2015; pp. 49–56. [Google Scholar]
  169. Schleiss, P.; Tørring, N.; Mikkelsen, S.; Jacobsen, R. Interconnecting IPv6 wireless sensors with an Android smartphone in the Future Internet. In Proceedings of the 2012 2nd Baltic Congress on Future Internet Communications (BCFIC), Vilnius, Lithuania, 25–27 April 2012; pp. 14–18. [Google Scholar]
  170. Fan, C.; Wen, Z.; Wang, F.; Wu, Y. A middleware of internet of things(iot) based on Zigbee and RFID. In Proceedings of the IET International Conference on Communication Technology and Application (ICCTA 2011), Beijing, China, 14–16 October 2012; Volume 2011, pp. 732–736. [Google Scholar]
  171. Alharbe, N.; Atkins, A.; Akbari, A. Application of ZigBee and RFID technologies in healthcare in conjunction with the internet of things. In Proceedings of the International Conference on Advances in Mobile Computing & Multimedia, Vienna, Austria, 2–4 December 2013; pp. 191–195. [Google Scholar]
  172. Zhang, Q.H.; Cheng, G.Q.; Wang, Z.; Cheng, J.; Zhang, D. The Implementation of Workshop Production Information Acquisition System Based on RFID and ZigBee. Appl. Mech. Mater. 2014, 556, 6324–6327. [Google Scholar] [CrossRef]
  173. Yi, X.J.; Zhou, M.; Liu, J. Design of Smart Home Control System by Internet of Things Based On ZigBee; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 128–133. [Google Scholar]
  174. Wang, Y.M. The Internet of Things Smart Home System Design Based on ZigBee/GPRS Technology. Appl. Mech. Mater. 2013, 263, 2849–2852. [Google Scholar] [CrossRef]
  175. Kodali, R.; Swamy, G.; Lakshmi, B. An Implementation of IoT for Healthcare; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 411–416. [Google Scholar]
  176. Spanò, E.; Di Pascoli, S.; Iannaccone, G. Low-Power Wearable ECG Monitoring System for Multiple-Patient Remote Monitoring. IEEE Sens. J. 2016, 16, 5452–5462. [Google Scholar] [CrossRef]
  177. Rosner, D.; Tataroiu, R.; Gheorghe, L.; Tilimpea, R. UNCHAIN—Ubiquitous Wireless Network Communication Architecture For Ambient Intelligence and Health Scenarios; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2014; pp. 44–51. [Google Scholar]
  178. Gomez, C.; Oller, J.; Paradells, J. Overview and evaluation of bluetooth low energy: An emerging low-power wireless technology. Sensors 2012, 12, 11734–11753. [Google Scholar] [CrossRef]
  179. Horvat, I.; Lukac, N.; Pavlovic, R.; Starcevic, D. Smart Plug Solution Based on Bluetooth Low Energy; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 435–437. [Google Scholar]
  180. Pham-Huu, D.N.; Nguyen, V.H.; Trinh, V.A.; Bui, V.H.; Pham, H.A. Towards an Open Framework for Home Automation Development; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 75–81. [Google Scholar]
  181. Papp, I.; Velikic, G.; Lukac, N.; Horvat, I. Uniform Representation and Control of Bluetooth Low Energy Devices in Home Automation Software; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 366–368. [Google Scholar]
  182. Kudeshia, P.; Shah, S.; Bhattacharjee, A. A Cost-Effective Solution for Pedestrian Localization in Complex Indoor Environment; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 1–7. [Google Scholar]
  183. Peng, Y.; Fan, W.; Dong, X.; Zhang, X. An Iterative Weighted KNN (IW-KNN) Based Indoor Localization Method in Bluetooth Low Energy (BLE) Environment; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2017; pp. 794–800. [Google Scholar]
  184. Vasilateanu, A.; Goga, N.; Guta, L.; Mihailescu, M.; Pavaloiu, B. Testing Wi-Fi and Bluetooth Low Energy Technologies for a Hybrid Indoor Positioning System; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016. [Google Scholar]
  185. Kang, H.W.; Kim, C.M.; Koh, S.J. ISO/IEEE 11073-Based Healthcare Services Over Iot Platform Using 6LoWPAN and BLE: Architecture and Experimentation; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 313–318. [Google Scholar]
  186. Touati, F.; Mnaouer, A.; Erdene-Ochir, O.; Mehmood, W.; Hassan, A.; Gaabab, B. Feasibility and performance evaluation of a 6LoWPAN-enabled platform for ubiquitous healthcare monitoring. Wirel. Commun. Mob. Comput. 2016, 16, 1271–1281. [Google Scholar] [CrossRef]
  187. Fafoutis, X.; Tsimbalo, E.; Mellios, E.; Hilton, G.; Piechocki, R.; Craddock, I. A residential maintenance-free long-term activity monitoring system for healthcare applications. Eurasip J. Wirel. Commun. Netw. 2016, 2016, 1–20. [Google Scholar] [CrossRef]
  188. Gao, X.; Zhang, B.; Li, S. A 220-Volts Power Switch Controlled Through WiFi; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 526–529. [Google Scholar]
  189. Airola, A.; Jousimaa, O.; Niemi, K.; Vuokko, S.; Kiviluoma, P.; Kuosmanen, P. Integration of household appliances to the existing internet infrastructure. In Proceedings of the 10th International DAAAM Baltic Conference “Industrial Engineering”, Tallinn, Estonia, 12–13 May 2015; pp. 111–116. [Google Scholar]
  190. Nambiar, V.; Vattapparamban, E.; Yurekli, A.; Guvenc, I.; Mozaffari, M.; Saad, W. SDR Based Indoor Localization Using Ambient WiFi and GSM Signals; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2017; pp. 952–957. [Google Scholar]
  191. Zhang, Z.L.; Chen, H.M.; Huang, T.P.; Cui, L.; Zhao, Z. A channel allocation scheme to mitigate Wifi interference for wireless sensor networks. Chin. J. Comput. 2012, 35, 504–517. [Google Scholar] [CrossRef]
  192. Samuel, S. A Review of Connectivity Challenges in IoT-Smart Home; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 364–367. [Google Scholar]
  193. Rajandekar, A.; Sikdar, B. The feasibility of using WiFi White spaces for opportunistic m2m communications. IEEE Wirel. Commun. Lett. 2015, 4, 681–684. [Google Scholar] [CrossRef]
  194. King, A.; Brown, J.; Roedig, U. DCCA: Differentiating Clear Channel Assessment for Improved 802.11/802.15.4 Coexistence; IEEE Computer Society: Washington, DC, USA, 2014; pp. 45–50. [Google Scholar]
  195. Lim, C.; Bolt, M.; Syed, A.; Ng, P.; Goh, C.; Li, Y. Dynamic Performance of IEEE 802.15.4 Devices under Persistent WiFi Traffic; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2015. [Google Scholar]
  196. Shi, G.; Xu, R.; Shu, Y.; Luo, J. Exploiting temporal and spatial variation for WiFi interference avoidance in ZigBee networks. Int. J. Sens. Netw. 2015, 18, 204–216. [Google Scholar] [CrossRef]
  197. Ndih, E.; Cherkaoui, S. On Enhancing Technology Coexistence in the IoT Era: ZigBee and 802.11 Case. IEEE Access 2016, 4, 1835–1844. [Google Scholar] [CrossRef]
  198. Gupta, A.; Jha, R. A Survey of 5G Network: Architecture and Emerging Technologies. IEEE Access 2015, 3, 1206–1232. [Google Scholar] [CrossRef]
  199. Costa-Requena, J.; Santos, J.; Guasch, V.; Ahokas, K.; Premsankar, G.; Luukkainen, S.; Pérez, O.; Itzazelaia, M.; Ahmad, I.; Liyanage, M.; et al. SDN and NFV Integration in Generalized Mobile Network Architecture; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2015; pp. 154–158. [Google Scholar]
  200. Uher, J.; Harper, J.; Mennecke, R.G.I.; Patton, P.; Farroha, B. Investigating end-to-end security in the fifth generation wireless capabilities and IoT extensions. SPIE 2016, 9826. [Google Scholar] [CrossRef]
  201. Kljaic, Z.; Skorput, P.; Amin, N. The Challenge of Cellular Cooperative ITS Services Based on 5G Communications Technology; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 587–594. [Google Scholar]
  202. Alexander, R.; Brandt, A.; Vasseur, J.; Hui, J.; Pister, K.; Thubert, P.; Levis, P.; Struik, R.; Kelsey, R.; Winter, T. RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks; RFC 6550; 2012; Available online: https://rfc-editor.org/rfc/rfc6550.txt (accessed on 12 August 2017).
  203. Gautham Krishna, G.; Krishna, G.; Bhalaji, N. Analysis of Routing Protocol for Low-power and Lossy Networks in IoT Real Time Applications. Elsevier 2016, 87, 270–274. [Google Scholar]
  204. Ancillotti, E.; Bruno, R.; Conti, M. On the interplay between RPL and address autoconfiguration protocols in LLNs. In Proceedings of the 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC), Sardinia, Italy, 1–5 July 2013; pp. 1275–1282. [Google Scholar]
  205. Vittecoq, S. A radio mesh platform for the IOT. In Proceedings of the 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), Taichung, Taiwan, 3–5 July 2013; pp. 530–534. [Google Scholar]
  206. Duquennoy, S.; Al Nahas, B.; Landsiedel, O.; Watteyne, T. Orchestra: Robust Mesh Networks through Autonomously Scheduled TSCH; Association for Computing Machinery, Inc.: New York, NY, USA, 2015; pp. 337–350. [Google Scholar]
  207. Sebastian, E.; Yushev, A.; Sikora, A.; Schappacher, M.; Prasetyo, J. Performance Investigation of 6Lo with RPL Mesh Networking for Home and Building Automation; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2017; pp. 127–133. [Google Scholar]
  208. Shelby, Z.; Hartke, K.; Bormann, C. The Constrained Application Protocol (CoAP); RFC 7252; 2014; Available online: https://rfc-editor.org/rfc/rfc7252.txt (accessed on 12 August 2017).
  209. Mohiuddin, J.; Bhadram, V.; Palli, S.; Koshy, S. 6LoWPAN Based Service Discovery and RESTful Web Accessibility for Internet of Things; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2014; pp. 24–30. [Google Scholar]
  210. Bhattacharyya, A.; Bose, T.; Bandyopadhyay, S.; Ukil, A.; Pal, A. LESS: Lightweight Establishment of Secure Session: A Cross-Layer Approach Using CoAP and DTLS-PSK Channel Encryption; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2015; pp. 682–687. [Google Scholar]
  211. Lakkundi, V.; Singh, K. Lightweight DTLS Implementation in CoAP-Based Internet of Things; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2014; pp. 7–11. [Google Scholar]
  212. Hillar, G. Mqtt Essentials—A Lightweight Iot Protocol; Packt Publishing: Birmingham, UK, 2017. [Google Scholar]
  213. Rescorla, E.; Modadugu, N. Datagram Transport Layer Security Version 1.2. RFC 6347. 2012. Available online: https://rfc-editor.org/rfc/rfc6347.txt (accessed on 12 August 2017).
  214. Lessa Dos Santos, G.; Guimaraes, V.; Da Cunha Rodrigues, G.; Granville, L.; Tarouco, L. A DTLS-Based Security Architecture for the Internet of Things; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 809–815. [Google Scholar]
  215. Raza, S.; Shafagh, H.; Hewage, K.; Hummen, R.; Voigt, T. Lithe: Lightweight secure CoAP for the internet of things. IEEE Sens. J. 2013, 13, 3711–3720. [Google Scholar] [CrossRef]
  216. Newman, N. Apple iBeacon technology briefing. J. Direct Data Digit. Mark. Pract. 2014, 15, 222–225. [Google Scholar] [CrossRef]
  217. Xiong, M.; Wu, Y.; Ding, Y.; Mao, X.; Fang, Z.; Huang, H. A Smart Home Control System Based on Indoor Location and Attitude Estimation; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016. [Google Scholar]
  218. Wu, D.; Jennings, C.; Terpenny, J.; Kumara, S. Cloud-Based Machine Learning for Predictive Analytics: Tool Wear Prediction in Milling; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 2062–2069. [Google Scholar]
  219. Han, J.H.; Chi, S.Y. Consideration of Manufacturing Data to Apply Machine Learning Methods for Predictive Manufacturing; IEEE Computer Society: Washington, DC, USA, 2016; pp. 109–113. [Google Scholar]
  220. Al Safadi, E.; Mohammad, F.; Iyer, D.; Smiley, B.; Jain, N. Generalized Activity Recognition Using Accelerometer in Wearable Devices for IoT Applications; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 73–79. [Google Scholar]
  221. Jatti, A.; Kannan, M.; Alisha, R.M.; Vijayalakshmi, P.; Sinha, S. Design and development of an IOT based wearable device for the safety and security of women and girl children. In Proceedings of the 2016 IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT), Bangalore, India, 20–21 May 2016; pp. 1108–1112. [Google Scholar]
  222. Khan, M.; Khan, A.; Khan, M.; Anwar, S. A Novel Learning Method to Classify Data Streams in the Internet of Things; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2014; pp. 61–66. [Google Scholar]
  223. Keshan, N.; Parimi, P.; Bichindaritz, I. Machine Learning for Stress Detection From ECG Signals in Automobile Drivers; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2015; pp. 2661–2669. [Google Scholar]
  224. Kholod, I.; Kuprianov, M.; Petukhov, I. Distributed Data Mining Based on Actors for Internet of Things; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 480–484. [Google Scholar]
  225. Bhuiyan, M.; Wu, J. Event Detection through Differential Pattern Mining in Internet of Things; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2017; pp. 109–117. [Google Scholar]
  226. Tseng, J.C.C.; Gu, J.Y.; Wang, P.F.; Chen, C.Y.; Tseng, V.S. A Novel Complex-Events Analytical System Using Episode Pattern Mining Techniques. In Proceedings of the International Conference on Intelligent Science and Big Data Engineering (IScIDE 2015), Suzhou, China, 14–16 June 2015; He, X., Gao, X., Zhang, Y., Zhou, Z.H., Liu, Z.Y., Fu, B., Hu, F., Zhang, Z., Eds.; Springer: Cham, Switzerland, 2015; pp. 487–498. [Google Scholar]
  227. Luckham, D.C. The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems; Addison-Wesley Longman Publishing Co., Inc.: Boston, MA, USA, 2001. [Google Scholar]
  228. Li, J.; Cheng, X.; Liu, B. Research on complex event of Internet of Things for supply chain decision support. ICIC Express Lett. Part B Appl. 2013, 4, 1481–1487. [Google Scholar]
  229. Li, J.T.; Lin, G.; Cheng, X.L. Research of IOT Context-Aware Processing Framework Based on Complex Event for Supply Chain Application. Appl. Mech. Mater. 2012, 263–266, 1677–1687. [Google Scholar] [CrossRef]
  230. Liu, B.; Zhao, G.; Li, J. Research on Information Services Architecture of IOT Oriented Supply Chain Application. In LISS 2013: Proceedings of 3rd International Conference on Logistics, Informatics and Service Science; Zhang, R., Zhang, Z., Liu, K., Zhang, J., Eds.; Springer: Berlin/Heidelberg, Germany, 2015; pp. 477–482. [Google Scholar]
  231. Mohamedali, F.; Matoorian, N. Support Dementia: Using Wearable Assistive Technology and Analysing Real-Time Data; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 50–54. [Google Scholar]
  232. Sheriff, C.; Naqishbandi, T.; Geetha, A. Healthcare Informatics and Analytics Framework; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2015. [Google Scholar]
  233. White, T. Hadoop: The Definitive Guide, 1st ed.; O’Reilly Media, Inc.: Newton, MA, USA, 2009. [Google Scholar]
  234. Ji, Z.; Ganchev, I.; O’Droma, M.; Zhao, L.; Zhang, X. A cloud-based car parking middleware for IoT-based smart cities: Design and implementation. Sensors 2014, 14, 22372–22393. [Google Scholar] [CrossRef] [PubMed]
  235. Hans, V.; Sethi, P.; Kinra, J. An Approach to IoT Based Car Parking and Reservation System on Cloud; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 352–354. [Google Scholar]
  236. Tahmassebpour, M.; Otaghvari, A. Increase Efficiency Big Data in Intelligent Transportation System with Using Iot Integration Cloud. J. Fundam. Appl. Sci. 2016, 8, 2443–2461. [Google Scholar]
  237. Ahmad, A.; Rathore, M.; Paul, A.; Rho, S. Defining Human Behaviors Using Big Data Analytics in Social Internet of Things; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 1101–1107. [Google Scholar]
  238. Chen, M.; Wang, J.; Li, P.; Zhou, J.; Xia, D. Development of intelligent gateway for heterogeneous networks environment monitoring in greenhouse based on Android system. Trans. Chin. Soc. Agric. Eng. 2015, 31, 218–225. [Google Scholar]
  239. Bian, J.; Fan, D.; Zhang, J. The new intelligent home control system based on the dynamic and intelligent gateway. In Proceedings of the 2011 4th IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT), Shenzhen, China, 28–30 October 2011; pp. 526–530. [Google Scholar]
  240. Carlson, D.; Altakrouri, B.; Schrader, A. In Proceedings of the An ad-hoc smart gateway platform for the web of things. In Proceedings of the IEEE International Conference on and IEEE Cyber, Physical and Social Computing Green Computing and Communications (GreenCom), Beijing, China, 20–23 August 2013; pp. 619–625. [Google Scholar]
  241. Garcia, C.; Eckard, D.; Netto, J.; Pereira, C.; Muller, I. Bluetooth Enabled Data Collector for Wireless Sensor Networks; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 54–57. [Google Scholar]
  242. Prakash, M.; Gowshika, U.; Ravichandran, T. A smart device integrated with an android for alerting a person’s health condition: Internet of Things. Indian J. Sci. Technol. 2016, 9, 1–2. [Google Scholar] [CrossRef]
  243. Sutar, S.; Koul, R.; Suryavanshi, R. Integration of Smart Phone and IOT for Development of Smart Public Transportation System; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 73–78. [Google Scholar]
  244. Hossain, A.; Canning, J.; Ast, S.; Rutledge, P.J.; Li, Y.T.; Jamalipour, A. Lab-in-a-Phone: Smartphone-Based Portable Fluorometer for pH Measurements of Environmental Water. IEEE Sens. J. 2015, 15, 5095–5102. [Google Scholar] [CrossRef]
  245. Dunkels, A.; Gronvall, B.; Voigt, T. Contiki—A lightweight and flexible operating system for tiny networked sensors. In Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks, Tampa, FL, USA, 16–18 November 2004; pp. 455–462. [Google Scholar]
  246. Anjana, S.; Sahana, M.; Ankith, S.; Natarajan, K.; Shobha, K.; Paventhan, A. An IoT Based 6LoWPAN Enabled Experiment for Water Management; IEEE Computer Society: Washington, DC, USA, 2016. [Google Scholar]
  247. Yassein, M.; Abuein, Q.; Amer, A. Energy saving in constrained application protocol of internet of things. Int. J. Commun. Antenna Propag. 2016, 6, 160–168. [Google Scholar]
  248. Gonizzi, P.; Ferrari, G.; Medagliani, P.; Leguay, J. Data storage and retrieval with RPL routing. In Proceedings of the 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC), Sardinia, Italy, 1–5 July 2013; pp. 1400–1404. [Google Scholar]
  249. Banh, M.; Mac, H.; Nguyen, N.; Phung, K.H.; Thanh, N.; Steenhaut, K. Performance Evaluation of Multiple RPL Routing Tree Instances For Internet of Things Applications; IEEE Computer Society: Washington, DC, USA, 2016; pp. 206–211. [Google Scholar]
  250. Sitanayah, L.; Sreenan, C.; Fedor, S. Demo Abstract: A Cooja-Based Tool for Maintaining Sensor Network Coverage Requirements in a Building; Association for Computing Machinery, Inc.: New York, NY, USA, 2013. [Google Scholar]
  251. Dinesh, M.; Sudhaman, K. Real Time Intelligent Image Processing System With High Speed Secured Internet of Things: Image Processor with IOT; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016. [Google Scholar]
  252. Xu, Z. Design and Implementation of Intelligent Gateway for Smart Home; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 4713–4718. [Google Scholar]
  253. Kovalcik, M.; Vapenik, R.; Fecil’Ak, P.; Jakab, F. Smart Home Control Module as IOT/IOE Learning; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016. [Google Scholar]
  254. Culic, I.M.; Radovici, A. Extending the wyliodrin platform for windows 10 IoT core. In Proceedings of the 2016 15th RoEduNet Conference: Networking in Education and Research, Bucharest, Romania, 7–9 September 2016; pp. 1–5. [Google Scholar]
  255. Suresh, S.; Vadivukkarasi, K. Multifunction sensor node for home intelligent system and Raspberry Pi as gateway. Int. J. Appl. Eng. Res. 2015, 10, 17163–17170. [Google Scholar]
  256. Kim, B.; Oh, S. Sensors control of smart farm system using gateway based on raspberry Pi. Adv. Sci. Lett. 2017, 23, 1533–1537. [Google Scholar] [CrossRef]
  257. Glória, A.; Cercas, F.; Souto, N. Design and implementation of an IoT gateway to create smart environments. Elsevier 2017, 109, 568–575. [Google Scholar] [CrossRef]
  258. Gupta, M.S.D.; Patchava, V.; Menezes, V. Healthcare based on IoT using Raspberry Pi. In Proceedings of the 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), Noida, India, 8–10 October 2015; pp. 796–799. [Google Scholar]
  259. Balasubramaniyan, C.; Manivannan, D. IoT enabled Air Quality Monitoring System (AQMS) using Raspberry Pi. Indian J. Sci. Technol. 2016, 9. [Google Scholar] [CrossRef]
  260. Bogdanovic, Z.; Simic, K.; Milutinovic, M.; Radenkovic, B.; Despotović-Zrakic, M. A platform for learning internet of things. In Proceedings of the Multi Conference on Computer Science and Information Systems, Lisbon, Portugal, 15–19 July 2014; pp. 259–266. [Google Scholar]
  261. Raikar, M.; Desai, P.; Naragund, J. Active Learning Explored in Open Elective Course: Internet of Things (IoT); Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2017; pp. 15–18. [Google Scholar]
  262. Amin, A.; Rahman, A.; Salim, S.; Aris, I.; Suliaman, M.; Mohamad, R.; Zulkefle, A. Cost effective remote monitoring for mini hybrid automation system. Int. J. Appl. Eng. Res. 2015, 10, 43663–43668. [Google Scholar]
  263. Fuertes, W.; Carrera, D.; Villacis, C.; Toulkeridis, T.; Galarraga, F.; Torres, E.; Aules, H. Distributed System as Internet of Things for a New Low-Cost, Air Pollution Wireless Monitoring on Real Time; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 58–67. [Google Scholar]
  264. Ashwini, M.; Gowrishankar, S.; Siddaraju. Internet of Things based intelligent monitoring and reporting from agricultural fields. Int. J. Control Theory Appl. 2016, 9, 4311–4320. [Google Scholar]
  265. Behringer, R.; Ramachandran, M.; Chang, V. A Low-Cost Intelligent Car Break-in Alert System: Using Smartphone Accelerometers for Detecting Vehicle Break-Ins; SciTePress: Setúbal, Portugal, 2016; pp. 179–184. [Google Scholar]
  266. Kothandaraman, D.; Chellappan, C. Human movement tracking system with smartphone sensing and Bluetooth Low Energy in Internet of Things. Asian J. Inf. Technol. 2016, 15, 661–669. [Google Scholar]
  267. Seol, S.; Shin, Y.; Kim, W. Design and realization of personal IoT architecture based on mobile gateway. Int. J. Smart Home 2015, 9, 133–144. [Google Scholar] [CrossRef]
  268. Gupta, B.; Jyoti, K. Theoretical Framework for Physiological Profiling Using Sensors and Big Data Analytics; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 416–420. [Google Scholar]
  269. Pereira, C.; Pinto, A.; Aguiar, A.; Rocha, P.; Santiago, F.; Sousa, J. IoT Interoperability for Actuating Applications Through Standardised M2M Communications; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016. [Google Scholar]
  270. Mayer, S.; Soros, G. User Interface Beaming—Seamless Interaction with Smart Things Using Personal Wearable Computers; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2014; pp. 46–49. [Google Scholar]
  271. Lee, K.Y.; Kang, M.S.; Kang, J.J.; Choi, S.J.; Im, Y.S.; Kang, E.Y.; Jeong, Y.M. Home automation system using wireless communication. Information 2016, 19, 3777–3782. [Google Scholar]
  272. Compton, K.; Hauck, S. Reconfigurable Computing: A Survey of Systems and Software. ACM Comput. Surv. 2002, 34, 171–210. [Google Scholar] [CrossRef]
  273. Prasetyo, K.; Purwanto, Y.; Darlis, D. An Implementation of Data Encryption for Internet of Things Using Blowfish Algorithm on FPGA; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2014; pp. 75–79. [Google Scholar]
  274. Rao, M.; Newe, T.; Grout, I.; Lewis, E.; Mathur, A. FPGA Based Reconfigurable IPSec AH Core Suitable for IoT Applications; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2015; pp. 2212–2216. [Google Scholar]
  275. Prathiba, A.; Kanchana Bhaaskaran, V. FPGA implementation and analysis of the block cipher mode architectures for the PRESENT light weight encryption algorithm. Indian J. Sci. Technol. 2016, 9. [Google Scholar] [CrossRef]
  276. Qu, L.; Sun, X.; Huang, Y.; Tang, C.; Ling, L. FPGA implementation of QoS multicast routing algorithm of mine internet of things perception layer based on ant colony algorithm. Adv. Inf. Sci. Serv. Sci. 2012, 4, 124–131. [Google Scholar]
  277. Wehner, P.; Gohringer, D. Parallel and Distributed Simulation of Networked Multi-Core Systems; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2014. [Google Scholar]
  278. Lee, S.S.; Jung, W.C.; Park, J.H. Design of IoT based fire-watching and atmospheric environment monitoring systems applied with compound sensors and image processing. Int. J. Smart Home 2016, 10, 155–168. [Google Scholar] [CrossRef]
  279. Lim, Y.; Daas, S.; Hashim, S.; Sidek, R.; Kamsani, N.; Rokhani, F. Reduced Hardware Architecture for Energy-Efficient IoT Healthcare Sensor Nodes; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016; pp. 90–95. [Google Scholar]
  280. Sushmitha, S.; Malathi, K. Home automation bymeansof Internet of Things. Int. J. Pharm. Technol. 2016, 8, 19874–19883. [Google Scholar]
  281. Zhang, K.; Li, H.; Ma, J.; Liu, X. Efficient large-universe multi-authority ciphertext-policy attribute-based encryption with white-box traceability. Sci. China Inf. Sci. 2018, 61. [Google Scholar] [CrossRef]
  282. Suo, H.; Wan, J.; Zou, C.; Liu, J. Security in the internet of things: A review. In Proceedings - 2012 International Conference on Computer Science and Electronics Engineering. ICCSEE 2012, 3, 648–651. [Google Scholar] [CrossRef]
  283. Jing, Q.; Vasilakos, A.V.; Wan, J.; Lu, J.; Qiu, D. Security of the Internet of Things: perspectives and challenges. Wirel. Netw. 2014, 20, 2481–2501. [Google Scholar] [CrossRef]
  284. Roman, R.; Alcaraz, C.; Lopez, J.; Sklavos, N. Key management systems for sensor networks in the context of the Internet of Things. Comput. Electr. Eng. 2011, 37, 147–159. [Google Scholar] [CrossRef]
  285. Ziegeldorf, J.; Morchon, O.; Wehrle, K. Privacy in the internet of things: Threats and challenges. Secur. Commun. Netw. 2014, 7, 2728–2742. [Google Scholar] [CrossRef]
  286. Zhou, K.; Liu, T.; Zhou, L. Industry 4.0: Towards future industrial opportunities and challenges. In Proceedings of the 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Zhangjiajie, China, 15–17 August 2015; pp. 2147–2152. [Google Scholar]
  287. Weinberger, M.; Bilgeri, D.; Fleisch, E. IoT business models in an industrial context. At-Automatisierungstechnik 2016, 64, 699–706. [Google Scholar] [CrossRef]
  288. Gonzalez, N.; Goya, W.; De Fatima Pereira, R.; Langona, K.; Silva, E.; De Brito Carvalho, T.; Miers, C.; Mangs, J.E.; Sefidcon, A. Fog Computing: Data Analytics and Cloud Distributed Processing on The Network Edges; IEEE Computer Society: Washington, DC, USA, 2017. [Google Scholar]
  289. Yangui, S.; Ravindran, P.; Bibani, O.; Glitho, R.H.; Hadj-Alouane, N.B.; Morrow, M.J.; Polakos, P.A. A platform as-a-service for hybrid cloud/fog environments. In Proceedings of the 2016 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN), Rome, Italy, 13–15 June 2016; pp. 1–7. [Google Scholar]
  290. Madsen, H.; Burtschy, B.; Albeanu, G.; Popentiu-Vladicescu, F. Reliability in the utility computing era: Towards reliable Fog computing. In Proceedings of the 2013 20th International Conference on Systems, Signals and Image Processing (IWSSIP), Bucharest, Romania, 7–9 July 2013; pp. 43–46. [Google Scholar]
  291. Flauzac, O.; Gonzalez, C.; Hachani, A.; Nolot, F. SDN Based Architecture for IoT and Improvement of the Security; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2015; pp. 688–693. [Google Scholar]
  292. Oh, Y.; Kim, G.; Lee, S. Multi-RAT Mobile Node Architecture for Efficient Handover Using Software Defined Network; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016. [Google Scholar]
  293. El-Mougy, A.; Ibnkahla, M.; Hegazy, L. Software-defined wireless network architectures for the Internet-of-Things. In Proceedings of the 2015 IEEE 40th Local Computer Networks Conference Workshops (LCN Workshops), Clearwater Beach, FL, USA, 26–29 October 2015; pp. 804–811. [Google Scholar]
  294. Vilalta, R.; Ciungu, R.; Mayoral, A.; Casellas, R.; Martinez, R.; Pubill, D.; Serra, J.; Munoz, R.; Verikoukis, C. Improving Security in Internet of Things With Software Defined Networking; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2016. [Google Scholar]
  295. Hafeez, I.; Ding, A.; Suomalainen, L.; Kirichenko, A.; Tarkoma, S. Securebox: Toward Safer and Smarter IoT Networks; Association for Computing Machinery, Inc.: New York, NY, USA, 2016; pp. 55–60. [Google Scholar]
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Figure 1. Documents per year published (WoS and Scopus) with the search string “Internet of Things” (IoT) for the period 2000 to 2016. (a) before the duplicates-removal filter; (b) after duplicates-removal filter.
Figure 1. Documents per year published (WoS and Scopus) with the search string “Internet of Things” (IoT) for the period 2000 to 2016. (a) before the duplicates-removal filter; (b) after duplicates-removal filter.
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Figure 2. Internet of Things percentage of documents published per year by the top 7 first author’s corresponding address country for the period 2002 to 2016.
Figure 2. Internet of Things percentage of documents published per year by the top 7 first author’s corresponding address country for the period 2002 to 2016.
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Figure 3. Internet of Things top 5 authors with most documents published per year, for the period 2006 to 2016.
Figure 3. Internet of Things top 5 authors with most documents published per year, for the period 2006 to 2016.
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Figure 4. Internet of Things top authors’ keywords documents published per year, excluding the keywords: Internet of Things, IoT, Internet of Things (IoT), and The Internet of Things, for the period 2006 to 2016.
Figure 4. Internet of Things top authors’ keywords documents published per year, excluding the keywords: Internet of Things, IoT, Internet of Things (IoT), and The Internet of Things, for the period 2006 to 2016.
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Figure 5. Internet of Things top applications based authors’ keywords in documents per year, for the period 2006 to 2016. (a) applications that start with “smart” (b) applications that do not start with “smart”.
Figure 5. Internet of Things top applications based authors’ keywords in documents per year, for the period 2006 to 2016. (a) applications that start with “smart” (b) applications that do not start with “smart”.
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Figure 6. Internet of Things media and host layers communication protocols based on authors’ keywords in documents per year, for the 2006 to 2016 period. (a) media layers’ communications protocols; (b) host layers’ communication protocols.
Figure 6. Internet of Things media and host layers communication protocols based on authors’ keywords in documents per year, for the 2006 to 2016 period. (a) media layers’ communications protocols; (b) host layers’ communication protocols.
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Figure 7. Internet of Things software processing techniques based on authors’ keywords in documents per year, for the period 2006 to 2016.
Figure 7. Internet of Things software processing techniques based on authors’ keywords in documents per year, for the period 2006 to 2016.
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Figure 8. Internet of Things devices operating systems (OS) and hardware based on authors’ keywords, for the period 2006 to 2016. (a) most used operating systems in authors’ keywords per year; (b) most used hardware in authors’ keywords per year.
Figure 8. Internet of Things devices operating systems (OS) and hardware based on authors’ keywords, for the period 2006 to 2016. (a) most used operating systems in authors’ keywords per year; (b) most used hardware in authors’ keywords per year.
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Figure 9. Internet of Things top trending topics based on authors’ keywords, with average growth rate (AGR) for different times periods (2011–2012, 2013–2014, and 2015–2016).
Figure 9. Internet of Things top trending topics based on authors’ keywords, with average growth rate (AGR) for different times periods (2011–2012, 2013–2014, and 2015–2016).
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Table
Table 1. Type of documents found with the search string “Internet of Things” found in Clarivate Web of Science (WoS) and Scopus within one day on 6 July 2017.
Table 1. Type of documents found with the search string “Internet of Things” found in Clarivate Web of Science (WoS) and Scopus within one day on 6 July 2017.
SourceArticleConference PaperProceedings PaperReviewDuplicated Removed
WoS31120821513055
Scopus528310,06803128030
Table
Table 2. Documents’ countries names replacing table.
Table 2. Documents’ countries names replacing table.
OriginalReplacement
Republic of ChinaChina
USAUnited States
England, Scotland, and WalesUnited Kingdom
U Arab EmiratesUnited Arab Emirates
RussiaRussian Federation
Viet NamVietnam
Trinid & TobagoTrinidad and Tobago
Table
Table 3. Internet of Things top 50 countries of first author’s corresponding address. Country number position (N.), total number of publications (Total), average percentage growth from the last 3 years (2014 to 2016), and h-index (h-ind.) from 2006 to 2016.
Table 3. Internet of Things top 50 countries of first author’s corresponding address. Country number position (N.), total number of publications (Total), average percentage growth from the last 3 years (2014 to 2016), and h-index (h-ind.) from 2006 to 2016.
N.CountryTotalAverage Growthh-Ind.
1China482216%47
2United States1561116%42
3India1089169%15
4South Korea894206%16
5Italy87461%32
6Germany81164%24
7United King.71171%25
8France543126%21
9Spain46342%23
10Japan449166%11
11Taiwan43868%16
12Brazil27290%9
13Finland26650%20
14Canada259104%15
15Australia24959%22
16Sweden21668%17
17Switzerland19331%19
18Portugal19145%13
19Greece18046%14
20Romania16972%9
21Belgium16487%11
22Austria146113%12
23Malaysia13771%9
24Russian Fed.134271%8
25Ireland126116%9
26Netherlands109122%12
27Singapore109112%8
28Poland10477%6
29Czech Rep.101153%5
30Turkey92319%5
31Pakistan82210%7
32Saudi Arabia80122%7
33Norway72119%11
34UAE71180%6
35South Africa60162%9
36Denmark5939%11
37Tunisia55163%6
38Serbia53−1%6
39Croatia51159%6
40Hungary5124%6
41Indonesia51410%3
42Egypt49159%4
43Morocco47163%4
44Iran4294%5
45Colombia39146%3
46Algeria38113%5
47Jordan38108%5
48New Zealand3886%6
49Mexico36172%5
50Thailand32107%4
Table
Table 4. Internet of Things, top 20 authors with most publications, total number of documents, h-index, most cited document, and top related research topics for the period 2006 to 2016.
Table 4. Internet of Things, top 20 authors with most publications, total number of documents, h-index, most cited document, and top related research topics for the period 2006 to 2016.
N.AuthorTotal Documentsh-IndexMost Cited Top Author Topics
1Zhang, Y.13012[40]RFID, security, Electric vehicle
2Liu, Y.11511[33]RFID, name service, ZigBee
3Li, Y.979[41]RFID, big data, database
4Wang, Y.975[42]RFID, smart grid, secirity
5Li, J.969[43]RFID, ZigBee, standarized breeding
6Zhang, J.826[44]RFID, WSN, monitoring system
7Wang, J.808[45]RFID, 5G, sampling
8Zhang, L.7913[46]Cloud computing, cloud manufacturing, ZigBee
9Wang, X.786[47]RFID, ZigBee, service selection
10Chen, Y.728[48]RFID, WSN, ZigBee
11Jara, A.J.7213[49]6LoWPAN, smart cities, big data
12Zhang, X.726[50]Logistics, RFID, WSN
13Li, H.717[51]RFID, authentication, security
14Wang, H.709[52]RFID, monitoring, cloud computing
15Li, X.678[53]RFID, recommendation, smart grid
16Liu, J.6510[54]Cloud computing, RFID, security
17Kim, J.627[55]WSN, video streaming, HEVC
18Wang, Z.608[56]RFID, GPRS, EPC network
19Liu, X.599[57]Cloud computing, RFID, Landsenses ecology
20Kim, D.587[58]EPCIS, 6LoWPAN, security
Table
Table 5. Internet of Things top 10 documents with most citations, divided by document type, including position number (N.), first author, document reference, times cited, publication year, and first author corresponding address for the period 2002 to 2016.
Table 5. Internet of Things top 10 documents with most citations, divided by document type, including position number (N.), first author, document reference, times cited, publication year, and first author corresponding address for the period 2002 to 2016.
N.First AuthorDocument ReferenceTimes CitedPublication YearCountry
Articles documents
1Atzori L[12]32392010Italy
2Gubbi J[13]13692013Australia
3Miorandi D[59]7212012Italy
4Kortuem G[65]5062010United Kingdom
5Ganti RK[66]4942011United States
6Bobadilla J[67]4822013Spain
7Li B-H[46]4712010China
8Perera C[68]4542014Australia
9Zanella A[69]4042014Italy
10Chen M[70]3702014China
Conference and proceedings documents
1Bonomi F[60]5082012United States
2Tao F[61]2282011China
3Tan L[62]1692010China
4Spiess P[71]1562009Not specified
5Mainetti L[72]1422011Italy
6Zhu Q[33]1342010China
7Dohr A[73]1322010Austria
8Kovatsch M[74]1122011Switzerland
9Khan R[75]1012012Italy
10Su KH[43]972011China
Review documents
1Gershenfeld N[21]2712004United States
2Meng X[63]1722013China
3Aziz AA[64]1392013Malaysia
4Domingo MC[76]1382012Spain
5Borgia E[14]1322014Italy
6Hancke GP[77]1012013South Africa
7Wang ZL[78]982010United States
8Wang SH[79]832015United States
9Keoh SL[80]692014Singapore
10Malhotra A[81]642013United States
Table
Table 6. Internet of Things Open Systems Interconnection model (OSI model) communication protocols.
Table 6. Internet of Things Open Systems Interconnection model (OSI model) communication protocols.
LayerIoT Communication Protocols
Host layers7. ApplicationCoAP, MQTT, JSON, iBeacon
6. Presentation
5. Session
4. TransportTCP, UDP, DTLS
Media layers3. NetworkIPv6, 6LowPAN, ZigBee, BLE, RPL
2. Data linkRFID, 802.15.4, WiFi, BLE, 5G
1. Physical
Abbreviations definition: Constrained Application Protocol (CoAP), Message Queue Telemetry Transport (MQTT), JavaScript Object Notation (JSON), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Datagram Transport Layer Security (DTLS), Internet Protocol version 6 (IPv6), IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN), Bluetooth Low Energy (BLE), Routing Protocol for Low power and Lossy Networks (RPL), Radio-frequency identification (RFID).

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MDPI and ACS Style

Ruiz-Rosero, J.; Ramirez-Gonzalez, G.; Williams, J.M.; Liu, H.; Khanna, R.; Pisharody, G. Internet of Things: A Scientometric Review. Symmetry 2017, 9, 301. https://doi.org/10.3390/sym9120301

AMA Style

Ruiz-Rosero J, Ramirez-Gonzalez G, Williams JM, Liu H, Khanna R, Pisharody G. Internet of Things: A Scientometric Review. Symmetry. 2017; 9(12):301. https://doi.org/10.3390/sym9120301

Chicago/Turabian Style

Ruiz-Rosero, Juan, Gustavo Ramirez-Gonzalez, Jennifer M. Williams, Huaping Liu, Rahul Khanna, and Greeshma Pisharody. 2017. "Internet of Things: A Scientometric Review" Symmetry 9, no. 12: 301. https://doi.org/10.3390/sym9120301

APA Style

Ruiz-Rosero, J., Ramirez-Gonzalez, G., Williams, J. M., Liu, H., Khanna, R., & Pisharody, G. (2017). Internet of Things: A Scientometric Review. Symmetry, 9(12), 301. https://doi.org/10.3390/sym9120301

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