Resource-constrained Computing

Resource-Constrained Computing is a computational paradigm characterized by the design and execution of computing tasks on systems with severely limited processing power, memory, storage, and energy resources, distinguishing it from general-purpose computing through its emphasis on efficiency, optimization, and adaptability within embedded and edge devices, particularly in IoT ecosystems.

1.1K

Publications

62.9K

Citations

3.7K

Authors

1.1K

Institutions

Publications per year

2017–2026

775

Authors

3.7K

  1. ZH
    Zhu Han

    University of Houston, United States

    11 publications · 678 citations

    11

    h-index

  2. FR
    F. Richard Yu

    Carleton University, Canada

    10 publications · 627 citations

    10

    h-index

  3. SG
    Song Guo

    Hong Kong Polytechnic University, Hong Kong SAR China

    8 publications · 1.1K citations

    8

    h-index

  4. FZ
    Fuhui Zhou

    Nanjing University of Aeronautics and Astronautics, China

    7 publications · 1.3K citations

    7

    h-index

  5. VC
    Victor C. M. Leung

    University of British Columbia, Canada

    6 publications · 343 citations

    6

    h-index

1–5 of 3.7K

Venues

  1. IEEE Internet of Things Journal

    114 publications · 7.1K citations

    50

    h-index

  2. IEEE Access

    53 publications · 2.5K citations

    29

    h-index

  3. IEEE Transactions on Wireless Communications

    27 publications · 4.8K citations

    26

    h-index

  4. Sensors

    25 publications · 938 citations

    17

    h-index

  5. arXiv (Cornell University)

    Repository · 23 publications · 844 citations

    16

    h-index

1–5