Machine learning and data analytics for the IoT
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S.I. : APPLYING ARTIFICIAL INTELLIGENCE TO THE INTERNET OF THINGS
Machine learning and data analytics for the IoT Erwin Adi1 • Adnan Anwar2 • Zubair Baig2 • Sherali Zeadally3 Received: 17 January 2020 / Accepted: 20 March 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract 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. Keywords Cybersecurity Internet of Things Intelligent systems Machine learning
1 Introduction The Internet of Things (IoT) paradigm is both revolutionary as well as an enabler of automated and convenient lifestyles for modern day humans. The evolution of the IoT can be attributed to a confluence in advances that took place over the past decade in computing, communication, and application design. The resulting sphere of influence of IoT has expanded rapidly to cover the whole human race. IoT devices in common use to facilitate our daily activities include the smart phones, home assistants such as Google
& Zubair Baig [email protected] Erwin Adi [email protected] Adnan Anwar [email protected] Sherali Zeadally [email protected] 1
UNSW Canberra Cyber, The University of New South Wales Canberra at ADFA, Canberra, Australia
2
School of Information Technology, Deakin University, Geelong, Australia
3
College of Communication and Information, University of Kentucky, Lexington, USA
Play, smart vehicles, building automation systems comprising smart elevators and temperature control systems, and unmanned aerial vehicles such as drones for environmental monitoring and leisure. The large-scale proliferation of IoT devices stretch beyond these devices to within the storage centers such as back-end cloud facilities which are geographically dispersed. As a result, a large volume of data is generated by IoT devices and their supporting platforms, for transfer and subsequent storage and processing at back-end cloud storage centers. IoT devices generate a constant stream of raw data, which cannot be discerned for meaningful knowledge unless the data are processed through application of tech
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