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

Amjad Rehman, Tanzila Saba, Khalid Haseeb, Teg Alam and Jaime Lloret ()
Additional contact information
Amjad Rehman: College of Computer & Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
Tanzila Saba: College of Computer & Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
Khalid Haseeb: Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia
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

Sustainability, 2022, vol. 14, issue 19, 1-14

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.

Keywords: data analytics; machine learning; internet of health things; sustainable network; security; data hiding; healthcare (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/19/12185/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/19/12185/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:19:p:12185-:d:925546

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().