Adaptive Data Processing

Adaptive Data Processing is a data processing paradigm within edge computing that dynamically modifies processing strategies in response to changing data characteristics, resource availability, and Quality of Service requirements to optimize performance, efficiency, and accuracy at the network edge.

170

Publications

6.8K

Citations

522

Authors

254

Institutions

Publications per year

2017–2026

42

Authors

522

  1. TS
    Thomas Steinke

    Zuse Institute Berlin, Germany

    4 publications · 195 citations

    4

    h-index

  2. MC
    Michael C. Wicks

    United States Air Force Research Laboratory, United States

    3 publications · 124 citations

    3

    h-index

  3. AS
    Adam Smith

    Pennsylvania State University, United States

    3 publications · 178 citations

    3

    h-index

  4. ZH
    Zishu He

    University of Electronic Science and Technology of China, China

    3 publications · 132 citations

    3

    h-index

  5. JU
    Jonathan Ullman

    Harvard University, United States

    3 publications · 178 citations

    3

    h-index

1–5 of 522

Venues

  1. IEEE Transactions on Signal Processing

    10 publications · 872 citations

    10

    h-index

  2. 7

    h-index

  3. IEEE Geoscience and Remote Sensing Letters

    GRSL · 6 publications · 266 citations

    6

    h-index

  4. arXiv (Cornell University)

    Repository · 5 publications · 155 citations

    5

    h-index

  5. Sensors

    4 publications · 100 citations

    4

    h-index

1–5