# Machine learning and data analytics for the IoT

arXiv: https://arxiv.org/abs/2007.04093 · HTML: https://mlsanity.com/paper/2007.04093/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://mlsanity.com/paper/2209.08018/) (2022)
- [IoT-LM: Large Multisensory Language Models for the Internet of Things](https://mlsanity.com/paper/2407.09801/) (2024)
- [Collaborative Algorithms for Online Personalized Mean Estimation](https://mlsanity.com/paper/2208.11530/) (2022)
- [Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology](https://mlsanity.com/paper/2404.04205/) (2024)
- [Scalable Decentralized Algorithms for Online Personalized Mean Estimation](https://mlsanity.com/paper/2402.12812/) (2024)
- [OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sensing Framework](https://mlsanity.com/paper/2308.05757/) (2023)
- [CSM-H-R: A Context Modeling Framework in Supporting Reasoning Automation for Interoperable Intelligent Systems and Privacy Protection](https://mlsanity.com/paper/2308.11066/) (2023)

## Abstract (arXiv)

The Internet of Things (IoT) applications have grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e., cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data are processed for machine learning analysis and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.