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Sustainability Model for the Internet of Health Things (IoHT) Using Reinforcement Learning with Mobile Edge Secured Services

Author

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  • Amjad Rehman

    (College of Computer & Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia)

  • Tanzila Saba

    (College of Computer & Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia)

  • Khalid Haseeb

    (Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia
    Department of Computer Science, Islamia College Peshawar, Peshawar 25000, Pakistan)

  • Teg Alam

    (Department of Industrial Engineering, College of Engineering, Prince Sattam bin Abdul Aziz University, Al-Kharj 11942, Saudi Arabia)

  • Jaime Lloret

    (Insituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politecnica de Valencia, C/Paranimf, 1, 46370 Valencia, Grao de Gandia, Spain)

Abstract

In wireless multimedia networks, the Internet of Things (IoT) and visual sensors are used to interpret and exchange vast data in the form of images. The digital images are subsequently delivered to cloud systems via a sink node, where they are interacted with by smart communication systems using physical devices. Visual sensors are becoming a more significant part of digital systems and can help us live in a more intelligent world. However, for IoT-based data analytics, optimizing communications overhead by balancing the usage of energy and bandwidth resources is a new research challenge. Furthermore, protecting the IoT network’s data from anonymous attackers is critical. As a result, utilizing machine learning, this study proposes a mobile edge computing model with a secured cloud (MEC-Seccloud) for a sustainable Internet of Health Things (IoHT), providing real-time quality of service (QoS) for big data analytics while maintaining the integrity of green technologies. We investigate a reinforcement learning optimization technique to enable sensor interaction by examining metaheuristic methods and optimally transferring health-related information with the interaction of mobile edges. Furthermore, two-phase encryptions are used to guarantee data concealment and to provide secured wireless connectivity with cloud networks. The proposed model has shown considerable performance for various network metrics compared with earlier studies.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12185-:d:925546
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    References listed on IDEAS

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    1. 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.
    2. Wenjing Guo & Cairong Yan & Ting Lu, 2019. "Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing," International Journal of Distributed Sensor Networks, , vol. 15(2), pages 15501477198, February.
    3. Acar, Müge & Kaya, Onur, 2019. "A healthcare network design model with mobile hospitals for disaster preparedness: A case study for Istanbul earthquake," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 130(C), pages 273-292.
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    Cited by:

    1. Amr Mohammed Drwish & Amany Ahmed Al-Dokhny & Ahlam Mohammed Al-Abdullatif & Hibah Khalid Aladsani, 2023. "A Sustainable Quality Model for Mobile Learning in Post-Pandemic Higher Education: A Structural Equation Modeling-Based Investigation," Sustainability, MDPI, vol. 15(9), pages 1-19, April.
    2. Vishnu Kumar Kaliappan & Sundharamurthy Gnanamurthy & Abid Yahya & Ravi Samikannu & Muhammad Babar & Basit Qureshi & Anis Koubaa, 2023. "Machine Learning Based Healthcare Service Dissemination Using Social Internet of Things and Cloud Architecture in Smart Cities," Sustainability, MDPI, vol. 15(6), pages 1-15, March.

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