# Deep Learning for IoT Big Data and Streaming Analytics: A Survey

arXiv: https://arxiv.org/abs/1712.04301 · HTML: https://mlsanity.com/paper/1712.04301/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2017.

## Built on by (most cited first, 30 of 59)

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- [Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder](https://mlsanity.com/paper/2304.03420/) (2023)
- [IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://mlsanity.com/paper/2209.08018/) (2022)
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- [Unsupervised Deep Learning for IoT Time Series](https://mlsanity.com/paper/2302.03284/) (2023)
- [Stochastic Coded Federated Learning with Convergence and Privacy Guarantees](https://mlsanity.com/paper/2201.10092/) (2022)
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- [Randomly Initialized Convolutional Neural Network for the Recognition of COVID-19 using X-ray Images](https://mlsanity.com/paper/2105.08199/) (2021)
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- [Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis](https://mlsanity.com/paper/2502.21286/) (2025)
- [Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networks](https://mlsanity.com/paper/2107.03520/) (2021)
- [Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach](https://mlsanity.com/paper/2108.03872/) (2021)
- [A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models](https://mlsanity.com/paper/2112.09293/) (2021)
- [Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS](https://mlsanity.com/paper/2204.01379/) (2022)
- [Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions](https://mlsanity.com/paper/2205.01296/) (2022)
- [Multi-module based CVAE to predict HVCM faults in the SNS accelerator](https://mlsanity.com/paper/2304.10639/) (2023)
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- [Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2210.13547/) (2022)
- [Artificial Intelligence for Smart Transportation](https://mlsanity.com/paper/2308.07457/) (2023)

## Abstract (arXiv)

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely Deep Learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.