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IEEE Internet of Things Journal
H-index 140

IEEE Internet of Things Journal

2327-4662

Published by: IEEE

https://iot.ieee.org/

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Electronics and Electrical Engineering 2 768 3100 111
Computer Science 3 1284 4631 138

Additional Metrics

Number of Best Scientists*: 2046
Documents by Best Scientists*: 5717
Top 100 Ranked Scientists*: 58
SCIMAGO H-index: 208
SCIMAGO SJR: 2.483
Impact Factor: 8.9

Overview

Top Research Topics at IEEE Internet of Things Journal?

The discussions in IEEE Internet of Things Journal mainly cover the fields of Computer network, Distributed computing, Wireless, Cloud computing and Real-time computing. The journal facilitates discussions on Computer network that incorporate concepts from other fields like Energy consumption, Throughput and Communication channel. Most of the works presented in IEEE Internet of Things Journal deals with Energy consumption but it intersects with the subject of Efficient energy use.

While work presented in IEEE Internet of Things Journal provided substantial information on Distributed computing, it also covered topics in Scalability, Resource allocation, Scheduling (computing), Server and Reinforcement learning. In addition to Wireless research, it aims to explore topics under Wireless sensor network and Transmission (telecommunications). The Wireless sensor network study featured in it draws connections with the study of Node (networking).

The Cloud computing works featured in the journal incorporate elements from Computer security and Encryption. The journal features Computer security research that overlaps with concepts in The Internet. The journal explores studies in Edge computing as part of the wider topic of Enhanced Data Rates for GSM Evolution.

  • Computer network (24.13%)
  • Distributed computing (17.11%)
  • Wireless (14.25%)

What are the most cited papers published in the journal?

  • Internet of Things for Smart Cities (3387 citations)
  • Edge Computing: Vision and Challenges (2971 citations)
  • Fog and IoT: An Overview of Research Opportunities (1390 citations)

Research areas of the most cited articles at IEEE Internet of Things Journal:

Computer network, Cloud computing, Distributed computing, Wireless and Computer security are the main subjects of interest in the journal publications. The journal articles explore issues in Computer network which can be linked to other research areas like Energy consumption, Scalability and Communication channel. The most cited publications feature studies on Cloud computing, including topics such as Edge computing.

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

The top authors publishing in IEEE Internet of Things Journal (based on the number of publications) are:

  • Mohsen Guizani (65 papers) published 33 papers at the last edition, 22 more than at the previous edition,
  • Zhu Han (42 papers) published 25 papers at the last edition, 21 more than at the previous edition,
  • Dusit Niyato (40 papers) published 20 papers at the last edition, 8 more than at the previous edition,
  • Kim-Kwang Raymond Choo (38 papers) published 7 papers at the last edition, 7 less than at the previous edition,
  • Houbing Song (36 papers) published 19 papers at the last edition, 15 more than at the previous edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Only papers with recognized affiliations are considered

The top affiliations publishing in IEEE Internet of Things Journal (based on the number of publications) are:

  • Beijing University of Posts and Telecommunications (222 papers) published 100 papers at the last edition, 52 more than at the previous edition,
  • University of Electronic Science and Technology of China (171 papers) published 65 papers at the last edition, 31 more than at the previous edition,
  • Xidian University (150 papers) published 66 papers at the last edition, 19 more than at the previous edition,
  • Tsinghua University (119 papers) published 53 papers at the last edition, 22 more than at the previous edition,
  • Chinese Academy of Sciences (109 papers) published 42 papers at the last edition, 16 more than at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 16.17% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 26.88% were posted by at least one author from the top 10 institutions publishing in the journal. Another 11.24% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 20.15% of all publications and 41.72% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Career Opportunities in the Field of Internet of Things

If you are interested in the topics discussed in the IEEE Internet of Things Journal and want to delve deeper into related fields, one possible career path you might consider is becoming an educator who specializes in these areas. In such a role, you could contribute to the advancement of these fields while inspiring and mentoring the next generation of researchers and professionals.

As an example, if you're interested in teaching English in a unique and beautiful location, you might consider a position in Hawaii. Not only would you have the opportunity to enjoy the island's unique culture and lifestyle, you would also have the chance to educate students about the Internet of Things and its applications in different fields.

The specific steps to becoming an English teacher in Hawaii vary depending on your existing qualifications and the teaching level you are interested in, but the general process typically includes earning a Bachelor's degree, completing a teacher preparation program, and gaining certification. To learn more about the specific requirements to become an English teacher in Hawaii, you can visit this link.

As a teacher, you will have the opportunity to influence the next generation, introducing them to vital concepts and research areas that you have discovered in the IEEE Internet of Things Journal. This career path will not only allow you to share your passion for technology but will also contribute to the future of research and development in Internet of Things.

Top Publications

  • Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

    Shuiguang Deng;Hailiang Zhao;Weijia Fang;Jianwei Yin

    (2020)
    957 Citations
  • A Survey on the Metaverse: The State-of-the-Art, Technologies, Applications, and Challenges

    Unknown

    (2023)
    888 Citations
  • Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

    Jing Zhang;Dacheng Tao

    (2021)
    721 Citations
  • A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond

    Sawsan Abdulrahman;Hanine Tout;Hakima Ould-Slimane;Azzam Mourad

    (2021)
    696 Citations
  • Anomaly Detection for IoT Time-Series Data: A Survey

    Andrew A. Cook;Goksel Misirli;Zhong Fan

    (2020)
    686 Citations
  • Enabling Massive IoT Toward 6G: A Comprehensive Survey

    Fengxian Guo;F. Richard Yu;Heli Zhang;Xi Li

    (2021)
    620 Citations
  • A Learning-Based Incentive Mechanism for Federated Learning

    Yufeng Zhan;Peng Li;Zhihao Qu;Deze Zeng

    (2020)
    539 Citations
  • Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges

    Dinh C. Nguyen;Ming Ding;Quoc-Viet Pham;Pubudu N. Pathirana

    (2021)
    464 Citations

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