Embedded intelligence and the data-driven future of application-specific Internet of Things for smart environments

Li-Minn Ang, Kah Phooi Seng and Monica Wachowicz

International Journal of Distributed Sensor Networks, 2022, vol. 18, issue 6, 15501329221102371

Abstract: The advances and convergence in sensor technology, information and communication technology, and intelligent analytics have given rise to the Internet of Things or also known as the Internet of Everything or the Industrial Internet. The research and development works for the Internet of Things can be seen to have progressed in two main phases: (1) In the first phase, the earlier works for the Internet of Things focused on developing the building blocks and enabling technologies such as the sensors and RFID technologies, communications and wireless protocols, machine-to-machine interfaces, energy efficiency of nodes, and energy harvesting technologies, and (2) in the second phase, the latter and recent works focused on the addition of, and embedding value to application-specific Internet of Things using technologies for smart environments and applications such as intelligent analytics and machine learning, embedded vision and image processing, augmented reality, and autonomous systems. We associate the term of embedded intelligence and analytics with the data-driven future for application-specific Internet of Things. In this article, we give an introduction and review recent developments of embedded intelligence for the Internet of Things; the various embedded intelligence computational frameworks such as edge, fog, and cloud for the application-specific Internet of Things; and highlight the techniques, challenges, and opportunities for effective deployment of application-specific Internet of Things technology to address complex problems for various smart environments and applications.

Keywords: Artificial intelligence; deep learning; embedded analytics; Internet of Things; smart environments (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:18:y:2022:i:6:p:15501329221102371

DOI: 10.1177/15501329221102371

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