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Machine and Deep Learning Techniques in IoT and Cloud

Machine and Deep Learning Techniques in IoT and Cloud
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Author(s): J. Fenila Naomi (Sri Krishna College of Engineering and Technology, India), Kavitha M. (Sri Krishna College of Engineering and Technology, India)and Sathiyamoorthi V. (Sona College of Technology, India)
Copyright: 2021
Pages: 23
Source title: Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing
Source Author(s)/Editor(s): Sathiyamoorthi Velayutham (Sona College of Technology, India)
DOI: 10.4018/978-1-7998-3111-2.ch013

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Abstract

In establishing a healthy environment for connectivity devices, it is essential to ensure that privacy and security of connectivity devices are well protected. The modern world lives on data, information, and connectivity. Various kinds of sensors and edge devices stream large volumes of data to the cloud platform for storing, processing, and deriving insights. An internet of things (IoT) system poses certain difficulties in discretely identifying, remotely configuring, and controlling the devices, and in the safe transmission of data. Mutual authentication of devices and networks is crucial to initiate secure communication. It is important to keep the data in a secure manner during transmission and in store. Remotely operated devices help to monitor, control, and manage the IoT system efficiently. This chapter presents a review of the approaches and methodologies employed for certificate provisioning, device onboarding, monitoring, managing, and configuring of IoT systems. It also examines the real time challenges and limitations in and future scope for IoT systems.

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