Edge computing based secure health monitoring framework for electronic healthcare system
Open accessAbstract
Nowadays, Smart Healthcare Systems (SHS) are frequently used by people for personal healthcare observations using various smart devices. The SHS uses IoT technology and cloud infrastructure for data capturing, transmitting it through smart devices, data storage, processing, and healthcare advice. Processing such a huge amount of data from numerous IoT devices in a short time is quite challenging. Thus, technological frameworks such as edge computing or fog computing can be used as a middle layer between cloud and user in SHS. It reduces the response time for data processing at the lower level (edge level). But, Edge of Things (EoT) also suffers from security and privacy issues. A robust healthcare monitoring framework with secure data storage and access is needed. It will provide a quick response in case of the production of abnormal data and store/access the sensitive data securely. This paper proposed a Secure Framework based on the Edge of Things (SEoT) for Smart healthcare systems. This framework is mainly designed for real-time health monitoring, maintaining the security and confidentiality of the healthcare data in a controlled manner. This paper included clustering approaches for analyzing bio-signal data for abnormality detection and Attribute-Based Encryption (ABE) for bio-signal data security and secure access. The experimental results of the proposed framework show improved performance with maintaining the accuracy of up to 98.5% and data security.
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Experimental results of the proposed framework show improved performance, accuracy up to 98.5%, and data security.
“Experimental results of the proposed framework show an improved performance, an accuracy up to 98.5%, and data security.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [34]
A secure edge-computing-based framework for smart health systems focuses on real-time health monitoring and ensures data security and confidentiality through clustering for anomaly detection and attribute-based encryption.
“In addition, in [34], the authors proposed a secure edge-computing-based framework for smart health systems.This framework focuses on real-time health monitoring and ensures data security and confidentiality through clustering approaches for anomaly detection and attribute-based encryption (ABE) for secure access to biosignal data.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [34]
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References 36 · Cited by 69
Research works printed but not linked: 6
- [4]Ana JF Joan MM Josep J Towards the decentralised cloud: survey on approaches and challenges for mobile, ad hoc, and edge computing ACM Comput. Surv. (CSUR) 2019 51 6 1 36
- [5]Ola, S., Imad, E., Ayman, K., Ali, C.: Edge computing enabling the Internet of Things. In: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), pp. 603–608. IEEE (2015)
- [8]Vaiyapuri T Binbusayyis A Varadarajan V Security, Privacy and Trust in IoMT Enabled Smart Healthcare System: A Systematic Review of Current and Future Trends Int. J. Adv. Comput. Sci. Appl. 2021 12 731 737
- [9]Jaya Lakshmi G Ghonge M Obaid AJ Cloud based IoT Smart Healthcare System for Remote Patient Monitoring EAI Endorsed Trans. Pervasive Health Technol. 2021 7 28
- [17]Ke, W., Chien-Ming, C., Zhuoyu, T., Mohammad, S., Sachin, K., Saru, K.: Forward privacy preservation in IoT enabled Healthcare Systems. IEEE Trans. Indus. Inform. (2021)
- [31]Gagangeet SA Anish J A decoupled blockchain approach for edge-envisioned IoT-based healthcare monitoring IEEE J. Select. Areas Commun. 2020 39 2 491 499
- Authors
- Singh, A., & Chatterjee, K.
- Affiliations
- KIIT University (IN)
- National Institute of Technology Patna (IN)
- Corresponding author
- Chatterjee, Kakali
- Venue
- Cluster Computing
- Published
- 2022-09-02
- Volume
- 26
- Issue
- 2
- Pages
- 1205-1220
- ISSN
- 1386-7857 (ISSN-L)
- 1573-7543
- PMC
- PMC9438893
- OpenAlex
- W4294300713
- Cited by
- 69
- References
- 36
- Citations by year
- 2026: 19 · 2025: 30 · 2024: 14 · 2023: 5 · 2022: 1
- FWCI
- 5.54
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Cluster Computing (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- edge computing, smart healthcare, health monitoring, Internet of Things security, privacy preservation, anomaly detection, clustering, attribute-based encryption, secure data storage, real-time monitoring
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