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Artificial Intelligence of Things: Architectures, Applications and Challenges

Ahvar, E, Ahvar, S and Lee, GM Artificial Intelligence of Things: Architectures, Applications and Challenges. In: Springer Handbook of Internet of Things. Springer. ISBN 978-3-031-39649-6 (Accepted)

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Internet of Things (IoT), with the help of some technologies such as 5G, Fog Computing and Artificial Intelligence (AI), is driving a significant shift to support emerging applications with specific requirements (e.g., real-time processing of a high volume of data) with greater flexibility and efficiency. In this regard, AI subfields such as data analytics and learning techniques play an important role to support the emerging IoT-related applications. AI integrated with the IoT heralds the era of Artificial Intelligence of Things (AIoT). While AI functions traditionally need centralized data collection and processing, it may not be feasible for modern AIoT-related applications because of some features such as the high volume of generated data, scalable and distributed data sources, high scalability of networks and data privacy concerns. In recent years, we can see noticeable efforts in the AIoT domain ranging from AI efficiency (e.g., proposing lightweight AI units) to system (e.g., proposing more powerful AIoT end devices) and architecture efficiency (e.g., proposing new AI deployment methods for AIoT architecture). This chapter presents recent progress in AIoT research domain from AI, system and architecture perspectives. We then introduce some recent and promising applications of AIoT. Finally, the important challenges facing AIoT as well as some potential research opportunities will be presented.

Item Type: Book Section
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science & Mathematics
Publisher: Springer
Date Deposited: 01 Mar 2022 12:51
Last Modified: 26 Feb 2024 11:53
DOI or ID number: 10.1007/978-3-031-39650-2_19
URI: https://researchonline.ljmu.ac.uk/id/eprint/16439
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