Machine learning for Internet of Things data analysis: A survey

By Mohammad Saeid Mahdavinejad, Mohammadreza Rezvan, Mohammadamin Barekatain, Peyman Adibi, Payam M. Barnaghi, Amit P. Sheth. 2017

970 citations recorded by the source corpus.

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.

Subfields: Building and Construction, Media Technology, Transportation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Dacheng Tao
2. Ekram Hossain
3. Syed Ali Hassan
4. Rasheed Hussain
5. Fatima Hussain

In Media Technology

1. Dacheng Tao
2. Ekram Hossain
3. Jing Zhang

In Transportation

1. Indranil Gupta
2. Oluwasanmi Koyejo
3. Cong Xie
4. Jing Zhang

Citations in the corpus

• Practical Distributed Learning: Secure Machine Learning with Communication-Efficient Local Updates (2019, background): “More and more big-data applications are moving from cloud to edge, including for machine-learning tasks (Cao et al., 2015; Mahdavinejad et al., 2018; Zeydan et al., 2016).”
• Asynchronous Federated Optimization (2019, background): “At the same time, machine-learning applications are also moving from cloud to edge [3, 18, 21].”
• Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, background): “For the first intersection between IoT and learning domain, [20]–[23] have significant contributions.”
• Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, background): “In this context, Machine Learning (ML) is considered to be one of the most suitable computational paradigms to provide embedded intelligence in the IoT devices [18].”
• Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, neutral): “[19] Elsevier 2018 148 ML applications in the internet of things ≈ ≈ × ≈”
• Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, methodology): “Data mining and machine learning approaches have been used for IoT data processing and analysis [52], [53].”
• Edge Machine Learning for AI-Enabled IoT Devices: A Review (2020, background): “The Internet of Things (IoT) scenario [1,2] has gained a lot of notoriety in recent years.”
• SLSGD: Secure and Efficient Distributed On-device Machine Learning (2019, background): “More and more big-data applications are moving from the cloud to the edge, including for machine-learning tasks [5,21,30].”