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Machine Learning for Internet of Things Data Analysis: A Survey

Mahdavinejad, Mohammad Saeid, Rezvan, Mohammadreza, Barekatain, Mohammadamin, Adibi, Peyman, Barnaghi, Payam and Sheth, Amit P. (2017) Machine Learning for Internet of Things Data Analysis: A Survey Digital Communications and Networks, 4 (3). pp. 161-175.

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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 25-50 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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Mahdavinejad, Mohammad Saeid
Rezvan, Mohammadreza
Barekatain, Mohammadamin
Adibi, Peyman
Sheth, Amit P.
Date : 12 October 2017
DOI : 10.1016/j.dcan.2017.10.002
Copyright Disclaimer : © 2017 Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( Open Access funded by Chongqing University of Posts and Telecommunications.
Uncontrolled Keywords : Machine Learning; Internet of Things; Smart Data; Smart City
Depositing User : Clive Harris
Date Deposited : 12 Oct 2017 08:41
Last Modified : 19 Oct 2018 09:54

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