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Intelligent data analytics in energy optimization for the internet of underwater things

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Abstract

The development of the Internet of Things (IoT) widened its definition to incorporate aquatic conditions. The submarine sensor structures and intelligent underwater linked devices have been built into the IoT environment as the Internet of Underwater Things. Energy-sensitive and accurate data collection is carried out using extremely secure communications in underwater sensor networks that face major drawbacks like timing and place-dependent connectivity. Therefore in this paper, Optimized energy planning based intelligent data analytics has been proposed to offer a programming system for distributing intelligent data analytics underwater with high energy efficiency. IDA implements two stages: the first stage is to overcome a drawback caused by secret and expose terminals by a possibility-based disputing method. The second stage investigates the possibilities for slight specificity recovery by adding a space focused on transmitter and receiver. OEP is used to capture data through an activity that uses intelligent data focused on self-learning to identify highly secure and effective route directions across communication gaps in a sensor network. By balancing data traffic loading in a vast network, the OEP transport system minimizes greater energy usage and delay issues. In a controversial approach, IDA resolves a limitation of confidentiality and reveals terminals based on choice. OEP collects data via smart self-learning information to track safety and productive paths through connectivity holes in a sensor network. The experimental findings illustrate the improved results have been built in terms of the high packet distribution rate of 97.11% and low latency, and less energy consumption.

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Acknowledgements

The authors are grateful to the Taif University ResearchersSupporting Project (Number TURSP-2020/36), Taif University, Taif, Saudi Arabia

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Authors and Affiliations

  1. School of Computer Science & Engineering (SCOPE), Vellore Institute of Technology (VIT), Chennai, India

    Rajakumar Arul

  2. Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia

    Roobaea Alroobaea

  3. Miracl Laboratory, University of Sfax, Sfax, Tunisia

    Seifeddine Mechti

  4. Department of Industrial and Systems Engineering, University of Jeddah, Jeddah, Saudi Arabia

    Saeed Rubaiee & Murad Andejany

  5. College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia

    Usman Tariq

  6. Department of Information Technology and Computing, Arab Open University, Riyadh, Saudi Arabia

    Saman Iftikhar

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  1. Rajakumar Arul
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  2. Roobaea Alroobaea
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  3. Seifeddine Mechti
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Correspondence to Roobaea Alroobaea.

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Communicated by Vicente Garcia Diaz.

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Arul, R., Alroobaea, R., Mechti, S. et al. Intelligent data analytics in energy optimization for the internet of underwater things. Soft Comput 25, 12507–12519 (2021). https://doi.org/10.1007/s00500-021-06002-x

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