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Energy-Efficient IoT e-Health Using Artificial Intelligence Model with Homomorphic Secret Sharing

Author

Listed:
  • Amjad Rehman

    (Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia)

  • Tanzila Saba

    (Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia)

  • Khalid Haseeb

    (Department of Computer Science, Islamia College Peshawar, Peshawar 25000, Pakistan)

  • Souad Larabi Marie-Sainte

    (Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia)

  • Jaime Lloret

    (Integrated Management Coastal Research Institute, Universitat Politecnica de Valencia, 46730 Valencia, Spain
    School of Computing and Digital Technologies, Staffordshire University, Stoke ST4 2DE, UK)

Abstract

Internet of Things (IoT) is a developing technology for supporting heterogeneous physical objects into smart things and improving the individuals living using wireless communication systems. Recently, many smart healthcare systems are based on the Internet of Medical Things (IoMT) to collect and analyze the data for infectious diseases, i.e., body fever, flu, COVID-19, shortness of breath, etc. with the least operation cost. However, the most important research challenges in such applications are storing the medical data on a secured cloud and make the disease diagnosis system more energy efficient. Additionally, the rapid explosion of IoMT technology has involved many cyber-criminals and continuous attempts to compromise medical devices with information loss and generating bogus certificates. Thus, the increase in modern technologies for healthcare applications based on IoMT, securing health data, and offering trusted communication against intruders is gaining much research attention. Therefore, this study aims to propose an energy-efficient IoT e-health model using artificial intelligence with homomorphic secret sharing, which aims to increase the maintainability of disease diagnosis systems and support trustworthy communication with the integration of the medical cloud. The proposed model is analyzed and proved its significance against relevant systems.

Suggested Citation

  • Amjad Rehman & Tanzila Saba & Khalid Haseeb & Souad Larabi Marie-Sainte & Jaime Lloret, 2021. "Energy-Efficient IoT e-Health Using Artificial Intelligence Model with Homomorphic Secret Sharing," Energies, MDPI, vol. 14(19), pages 1-15, October.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6414-:d:651352
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    References listed on IDEAS

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    1. Amjad Rehman & Khalid Haseeb & Tanzila Saba & Jaime Lloret & Zara Ahmed, 2021. "Mobility Support 5G Architecture with Real-Time Routing for Sustainable Smart Cities," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
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    Cited by:

    1. Amjad Rehman & Tanzila Saba & Khalid Haseeb & Teg Alam & Jaime Lloret, 2022. "Sustainability Model for the Internet of Health Things (IoHT) Using Reinforcement Learning with Mobile Edge Secured Services," Sustainability, MDPI, vol. 14(19), pages 1-14, September.

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