Machine learning and data analytics for the IoT

被引:1
|
作者
Erwin Adi
Adnan Anwar
Zubair Baig
Sherali Zeadally
机构
[1] The University of New South Wales Canberra at ADFA,UNSW Canberra Cyber
[2] Deakin University,School of Information Technology
[3] University of Kentucky,College of Communication and Information
来源
关键词
Cybersecurity; Internet of Things; Intelligent systems; Machine learning;
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:16205 / 16233
页数:28
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