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Efficient data interpretation and artificial intelligence enabled IoT based smart sensing system

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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.

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This Research Received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

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Authors and Affiliations

  1. WMG, University of Warwick, Coventry, CV74AL, United Kingdom

    Achyut Shankar

  2. Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248002, India

    Achyut Shankar

  3. School of Computer Science Engineering, Lovely Professional University, Phagwara, Punjab, 144411, India

    Achyut Shankar

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  1. Achyut Shankar
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The authors have developed a Artificial Intelligence-based Effective Data Interpretation Approach (AI-EDIA) that has been proposed to improve the underwater wireless sensor network communication and less computational Time in IoT platforms.

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Correspondence to Achyut Shankar.

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Shankar, A. Efficient data interpretation and artificial intelligence enabled IoT based smart sensing system. Artif Intell Rev 56, 15053–15077 (2023). https://doi.org/10.1007/s10462-023-10519-y

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