Machine Learning-based Localization

Machine Learning-Based Localization is a machine learning-driven approach to estimating the physical position of Internet of Things (IoT) devices by leveraging data-driven models to infer location from sensor or signal measurements, distinguished from conventional geometric or probabilistic methods by its reliance on learned patterns from training data.

506

Publications

21.2K

Citations

1.7K

Authors

600

Institutions

Publications per year

2017–2026

393

Authors

1.7K

  1. WX
    Wendong Xiao

    University of Science and Technology Beijing, China

    7 publications · 311 citations

    8

    h-index

  2. HZ
    Han Zou

    Nanyang Technological University, Singapore

    7 publications · 516 citations

    7

    h-index

  3. JY
    Jaehyun Yoo

    Seoul National University, South Korea

    6 publications · 214 citations

    7

    h-index

  4. QY
    Qiang Yang

    Hong Kong University of Science and Technology, Hong Kong SAR China

    7 publications · 576 citations

    7

    h-index

  5. LX
    Lihua Xie

    Nanyang Technological University, Singapore

    6 publications · 416 citations

    6

    h-index

1–5 of 1.7K

Venues

  1. IEEE Access

    27 publications · 2K citations

    24

    h-index

  2. Sensors

    27 publications · 1.2K citations

    19

    h-index

  3. IEEE Sensors Journal

    18 publications · 687 citations

    17

    h-index

  4. IEEE Internet of Things Journal

    13 publications · 523 citations

    12

    h-index

  5. IEEE Communications Letters

    9 publications · 437 citations

    10

    h-index

1–5