3 research outputs found
Machine Learning for Internet of Things Data Analysis: A Survey
Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements 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 these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its 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 are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the 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 a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration
Machine learning for Internet of Things data analysis: A survey
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.Comment: Digital Communications and Networks (2017