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Efficient data interpretation and artificial intelligence enabled IoT based smart sensing system
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Shankar, Achyut (2023) Efficient data interpretation and artificial intelligence enabled IoT based smart sensing system. Artificial Intelligence Review, 56 . pp. 15053-15077. doi:10.1007/s10462-023-10519-y ISSN 0269-2821.
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WRAP-Efficient-data-artificial-intelligence-enabled-IoT-smart-sensing-23.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only until 14 June 2024. Contact author directly, specifying your specific needs. - Requires a PDF viewer. Download (1293Kb) |
Official URL: http://doi.org/10.1007/s10462-023-10519-y
Abstract
Underwater wireless communications (UWC), based on acoustic waves, radio frequency waves, and optical waves, are currently deployed using underwater communications networks. Wireless sensor communications are among the most common UWC technologies because they offer connectivity over long distances. However, the UWC complex problems include restricted bandwidth, multitrack loss, limited battery power, and latency in propagation. Hence in this paper, Artificial Intelligence based Effective Data Interpretation Approach (AI-EDIA) has been proposed to improve the underwater wireless sensor network communication and less computational Time in IoT platform. The proposed AI-EIDA utilizes the discrete cosine transform (DCT) with frequency modulation multiplexing (FMM) for underwater acoustic communication. Underwater acoustic channels are categorized as double Time and frequency distribution channels. Therefore, the reverse DCT structure provides the orthogonal characteristic of the traditional FMM with the additional advantages of reduced execution and improved speed when the actual calculations are needed. Thus the experimental results show that AI-EDIA decreases energy usage and less delay rate to 0.45 s.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QC Physics T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
Library of Congress Subject Headings (LCSH): | Artificial intelligence, Discrete cosine transforms , Underwater acoustics, Underwater acoustics -- Data processing, Underwater acoustic telemetry, Wireless communication systems | ||||||||
Journal or Publication Title: | Artificial Intelligence Review | ||||||||
Publisher: | Springer | ||||||||
ISSN: | 0269-2821 | ||||||||
Official Date: | December 2023 | ||||||||
Dates: |
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Volume: | 56 | ||||||||
Page Range: | pp. 15053-15077 | ||||||||
DOI: | 10.1007/s10462-023-10519-y | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Re-use Statement: | “This version of the article has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10462-023-10519-y. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/acceptedmanuscript-terms”. | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 1 September 2023 |
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