IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12185-d925546.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12185/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/12185/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chou, Chang-Chi & Chiang, Wen-Chu & Chen, Albert Y., 2022. "Emergency medical response in mass casualty incidents considering the traffic congestions in proximity on-site and hospital delays," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    2. Rafiei, Rezvan & Huang, Kai & Verma, Manish, 2022. "Cash versus in-kind transfer programs in humanitarian operations: An optimization program and a case study," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    3. Gilani Larimi, Niloofar & Azhdari, Abolghasem & Ghousi, Rouzbeh & Du, Bo, 2022. "Integrating GIS in reorganizing blood supply network in a robust-stochastic approach by combating disruption damages," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    4. Govindan, Kannan & Mina, Hassan & Alavi, Behrouz, 2020. "A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    5. Rodríguez-Espíndola, Oscar & Ahmadi, Hossein & Gastélum-Chavira, Diego & Ahumada-Valenzuela, Omar & Chowdhury, Soumyadeb & Dey, Prasanta Kumar & Albores, Pavel, 2023. "Humanitarian logistics optimization models: An investigation of decision-maker involvement and directions to promote implementation," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    6. Pouraliakbari-Mamaghani, Mahsa & Saif, Ahmed & Kamal, Noreen, 2023. "Reliable design of a congested disaster relief network: A two-stage stochastic-robust optimization approach," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    7. Shaker Ardakani, Elham & Gilani Larimi, Niloofar & Oveysi Nejad, Maryam & Madani Hosseini, Mahsa & Zargoush, Manaf, 2023. "A resilient, robust transformation of healthcare systems to cope with COVID-19 through alternative resources," Omega, Elsevier, vol. 114(C).
    8. Kundu, Tanmoy & Sheu, Jiuh-Biing & Kuo, Hsin-Tsz, 2022. "Emergency logistics management—Review and propositions for future research," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    9. Wang, Changjun & Chen, Shutong, 2020. "A distributionally robust optimization for blood supply network considering disasters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 134(C).
    10. Mahsa Pouraliakbari-Mamaghani & Ali Ghodratnama & Seyed Hamid Reza Pasandideh & Ahmed Saif, 2022. "A robust possibilistic programming approach for blood supply chain network design in disaster relief considering congestion," Operational Research, Springer, vol. 22(3), pages 1987-2032, July.
    11. Abdorrrahman Haeri & Seyyed-Mahdi Hosseini-Motlagh & Mohammad Reza Ghatreh Samani & Marziehsadat Rezaei, 2022. "An integrated socially responsible-efficient approach toward health service network design," Annals of Operations Research, Springer, vol. 319(1), pages 463-516, December.
    12. Arunita Chaukiyal, 2024. "Improving performance of WSNs in IoT applications by transmission power control and adaptive learning rates in reinforcement learning," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 87(3), pages 575-591, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12185-:d:925546. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.