mlsanityData to 7 Oct 2026

Artificial Intelligence of Things: A Survey

arXiv 2410.19998 ↗First posted 25 Oct 2024cs.NI cs.AIcited by 12

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Abstract (arXiv)

The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we provide a systematic and comprehensive review of AIoT research. We examine AIoT literature related to sensing, computing, and networking & communication, which form the three key components of AIoT. In addition to advancements in these areas, we review domain-specific AIoT systems that are designed for various important application domains. We have also created an accompanying GitHub repository, where we compile the papers included in this survey: https://github.com/AIoT-MLSys-Lab/AIoT-Survey. This repository will be actively maintained and updated with new research as it becomes available. As both IoT and AI become increasingly critical to our society, we believe AIoT is emerging as an essential research field at the intersection of IoT and modern AI. We hope this survey will serve as a valuable resource for those engaged in AIoT research and act as a catalyst for future explorations to bridge gaps and drive advancements in this exciting field.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 25 Oct 2024.

Builds on

6 of 16 references.

References · 6

  1. 2020Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Thingscited by 110
  2. 2019SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overheadcited by 82
  3. 2023FedMultimodal: A Benchmark For Multimodal Federated Learningcited by 51
  4. 2022Enable Deep Learning on Mobile Devices: Methods, Systems, and Applicationscited by 32
  5. 2022DeepMix: Mobility-aware, Lightweight, and Hybrid 3D Object Detection for Headsetscited by 10
  6. 2020MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensingcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 5.

12 papers.