# Machine learning for Internet of Things data analysis: A survey

arXiv: https://arxiv.org/abs/1802.06305 · HTML: https://mlsanity.com/paper/1802.06305/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2018.

## Built on by (most cited first, 29 of 29)

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- [Modeling Time-evolving Causality over Data Streams](https://mlsanity.com/paper/2502.08963/) (2025)
- [Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction](https://mlsanity.com/paper/2105.03905/) (2021)
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- [Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach](https://mlsanity.com/paper/2410.21346/) (2024)
- [An Efficient One-Class SVM for Anomaly Detection in the Internet of Things](https://mlsanity.com/paper/2104.11146/) (2021)
- [A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models](https://mlsanity.com/paper/2112.09293/) (2021)
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- [Baechi: Fast Device Placement of Machine Learning Graphs](https://mlsanity.com/paper/2301.08695/) (2023)
- [SunBlock: Cloudless Protection for IoT Systems](https://mlsanity.com/paper/2401.14332/) (2024)
- [Creation of AI-driven Smart Spaces for Enhanced Indoor Environments -- A Survey](https://mlsanity.com/paper/2412.14708/) (2024)
- [Application of Machine Learning-Based Pattern Recognition in IoT Devices: Review](https://mlsanity.com/paper/2202.02456/) (2022)
- [Strong Duality Relations in Nonconvex Risk-Constrained Learning](https://mlsanity.com/paper/2312.01110/) (2023)
- [k-Graph: A Graph Embedding for Interpretable Time Series Clustering](https://mlsanity.com/paper/2502.13049/) (2025)
- [Roughsets-based Approach for Predicting Battery Life in IoT](https://mlsanity.com/paper/2102.06026/) (2021)
- [Analyzing Wearables Dataset to Predict ADLs and Falls: A Pilot Study](https://mlsanity.com/paper/2209.04785/) (2022)
- [Graph Coordinates and Conventional Neural Networks -- An Alternative for Graph Neural Networks](https://mlsanity.com/paper/2312.01342/) (2023)
- [SpinML: Customized Synthetic Data Generation for Private Training of Specialized ML Models](https://mlsanity.com/paper/2503.03160/) (2025)
- [Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis](https://mlsanity.com/paper/2509.09251/) (2025)
- [An Efficient Variant of One-Class SVM with Lifelong Online Learning Guarantees](https://mlsanity.com/paper/2512.11052/) (2025)
- [Transfer learning-based method for automated ewaste recycling in smart cities](https://mlsanity.com/paper/2606.23286/) (2026)

## Abstract (arXiv)

Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this Big Data is the key to developing smart IoT applications. This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case. The key contribution of this study is presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for a more detailed exploration.