In-network Processing

In-Network Processing is a computational paradigm in distributed sensor networks where data aggregation, filtering, or analysis is performed within the network by intermediate sensor nodes rather than at a centralized sink, thereby reducing communication overhead and enabling efficient resource utilization in wireless and IoT sensor environments.

263

Publications

26.8K

Citations

758

Authors

264

Institutions

Publications per year

2017–2026

39

Authors

758

  1. DE
    Deborah Estrin

    University of California, Los Angeles, United States

    10 publications · 6.9K citations

    10

    h-index

  2. RG
    Ramesh Govindan

    University of Southern California, United States

    8 publications · 6.3K citations

    8

    h-index

  3. JH
    John Heidemann

    University of Southern California, United States

    7 publications · 990 citations

    7

    h-index

  4. SM
    Samuel Madden

    Massachusetts Institute of Technology, United States

    7 publications · 4.1K citations

    7

    h-index

  5. MW
    Matt Welsh

    Harvard University Press, United States

    6 publications · 639 citations

    6

    h-index

1–5 of 758

Venues

  1. IEEE Transactions on Mobile Computing

    TMCCI · 5 publications · 785 citations

    5

    h-index

  2. Sensors

    5 publications · 115 citations

    5

    h-index

  3. ACM Transactions on Sensor Networks

    4 publications · 726 citations

    4

    h-index

  4. IEEE Transactions on Signal Processing

    4 publications · 719 citations

    4

    h-index

  5. IEEE Sensors Journal

    4 publications · 248 citations

    4

    h-index

1–5