AI augmented edge and fog computing for Internet of Health Things (IoHT)
Patients today seek a more advanced and personalized health-care system that keeps up with the pace of modern living. Cloud computing delivers resources over the Internet and enables the deployment of an infinite number of applications to provide services to many sectors. The primary limitation of t...
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2431.pdf |
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| author | Deepika Rajagopal Pradeep Kumar Thimma Subramanian |
| author_facet | Deepika Rajagopal Pradeep Kumar Thimma Subramanian |
| author_sort | Deepika Rajagopal |
| collection | DOAJ |
| description | Patients today seek a more advanced and personalized health-care system that keeps up with the pace of modern living. Cloud computing delivers resources over the Internet and enables the deployment of an infinite number of applications to provide services to many sectors. The primary limitation of these cloud frameworks right now is their limited scalability, which results in their inability to meet needs. An edge/fog computing environment, paired with current computing techniques, is the answer to fulfill the energy efficiency and latency requirements for the real-time collection and analysis of health data. Additionally, the Internet of Things (IoT) revolution has been essential in changing contemporary healthcare systems by integrating social, economic, and technological perspectives. This requires transitioning from unadventurous healthcare systems to more adapted healthcare systems that allow patients to be identified, managed, and evaluated more easily. These techniques allow data from many sources to be integrated to effectively assess patient health status and predict potential preventive actions. A subset of the Internet of Things, the Internet of Health Things (IoHT) enables the remote exchange of data for physical processes like patient monitoring, treatment progress, observation, and consultation. Previous surveys related to healthcare mainly focused on architecture and networking, which left untouched important aspects of smart systems like optimal computing techniques such as artificial intelligence, deep learning, advanced technologies, and services that includes 5G and unified communication as a service (UCaaS). This study aims to examine future and existing fog and edge computing architectures and methods that have been augmented with artificial intelligence (AI) for use in healthcare applications, as well as defining the demands and challenges of incorporating fog and edge computing technology in IoHT, thereby helping healthcare professionals and technicians identify the relevant technologies required based on their need for developing IoHT frameworks for remote healthcare. Among the crucial elements to take into account in an IoHT framework are efficient resource management, low latency, and strong security. This review addresses several machine learning techniques for efficient resource management in the IoT, where machine learning (ML) and AI are crucial. It has been noted how the use of modern technologies, such as narrow band-IoT (NB-IoT) for wider coverage and Blockchain technology for security, is transforming IoHT. The last part of the review focuses on the future challenges posed by advanced technologies and services. This study provides prospective research suggestions for enhancing edge and fog computing services for healthcare with modern technologies in order to give patients with an improved quality of life. |
| format | Article |
| id | doaj-art-841d212f6bd54f258df7e98b69054273 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-841d212f6bd54f258df7e98b690542732025-02-01T15:05:21ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e243110.7717/peerj-cs.2431AI augmented edge and fog computing for Internet of Health Things (IoHT)Deepika RajagopalPradeep Kumar Thimma SubramanianPatients today seek a more advanced and personalized health-care system that keeps up with the pace of modern living. Cloud computing delivers resources over the Internet and enables the deployment of an infinite number of applications to provide services to many sectors. The primary limitation of these cloud frameworks right now is their limited scalability, which results in their inability to meet needs. An edge/fog computing environment, paired with current computing techniques, is the answer to fulfill the energy efficiency and latency requirements for the real-time collection and analysis of health data. Additionally, the Internet of Things (IoT) revolution has been essential in changing contemporary healthcare systems by integrating social, economic, and technological perspectives. This requires transitioning from unadventurous healthcare systems to more adapted healthcare systems that allow patients to be identified, managed, and evaluated more easily. These techniques allow data from many sources to be integrated to effectively assess patient health status and predict potential preventive actions. A subset of the Internet of Things, the Internet of Health Things (IoHT) enables the remote exchange of data for physical processes like patient monitoring, treatment progress, observation, and consultation. Previous surveys related to healthcare mainly focused on architecture and networking, which left untouched important aspects of smart systems like optimal computing techniques such as artificial intelligence, deep learning, advanced technologies, and services that includes 5G and unified communication as a service (UCaaS). This study aims to examine future and existing fog and edge computing architectures and methods that have been augmented with artificial intelligence (AI) for use in healthcare applications, as well as defining the demands and challenges of incorporating fog and edge computing technology in IoHT, thereby helping healthcare professionals and technicians identify the relevant technologies required based on their need for developing IoHT frameworks for remote healthcare. Among the crucial elements to take into account in an IoHT framework are efficient resource management, low latency, and strong security. This review addresses several machine learning techniques for efficient resource management in the IoT, where machine learning (ML) and AI are crucial. It has been noted how the use of modern technologies, such as narrow band-IoT (NB-IoT) for wider coverage and Blockchain technology for security, is transforming IoHT. The last part of the review focuses on the future challenges posed by advanced technologies and services. This study provides prospective research suggestions for enhancing edge and fog computing services for healthcare with modern technologies in order to give patients with an improved quality of life.https://peerj.com/articles/cs-2431.pdfArtificial intelligenceEdge computingFog computingInternet of Things (IoT)Internet of Health Things (IoHT)Machine learning (ML) algorithms for IoT |
| spellingShingle | Deepika Rajagopal Pradeep Kumar Thimma Subramanian AI augmented edge and fog computing for Internet of Health Things (IoHT) PeerJ Computer Science Artificial intelligence Edge computing Fog computing Internet of Things (IoT) Internet of Health Things (IoHT) Machine learning (ML) algorithms for IoT |
| title | AI augmented edge and fog computing for Internet of Health Things (IoHT) |
| title_full | AI augmented edge and fog computing for Internet of Health Things (IoHT) |
| title_fullStr | AI augmented edge and fog computing for Internet of Health Things (IoHT) |
| title_full_unstemmed | AI augmented edge and fog computing for Internet of Health Things (IoHT) |
| title_short | AI augmented edge and fog computing for Internet of Health Things (IoHT) |
| title_sort | ai augmented edge and fog computing for internet of health things ioht |
| topic | Artificial intelligence Edge computing Fog computing Internet of Things (IoT) Internet of Health Things (IoHT) Machine learning (ML) algorithms for IoT |
| url | https://peerj.com/articles/cs-2431.pdf |
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