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Secure Healthcare Data Aggregation and Transmission in IoT—A Survey
Open accessAbstract
Internet of medical things (IoMT) is getting researchers' attention due to its wide applicability in healthcare. Smart healthcare sensors and IoT enabled medical devices exchange data and collaborate with other smart devices without human interaction to securely transmit collected sensitive healthcare data towards the server nodes. Alongside data communications, security and privacy is also quite challenging to securely aggregate and transmit healthcare data towards Fog and cloud servers. We explored the existing surveys to identify a gap in literature that a survey of fog-assisted secure healthcare data collection schemes is yet contributed in literature. This paper presents a survey of different data collection and secure transmission schemes where Fog computing based architectures are considered. A taxonomy is presented to categorize the schemes. Fog assisted smart city, smart vehicle and smart grids are also considered that achieve secure, efficient and reliable data collection with low computational cost and compression ratio. We present a summary of these scheme along with analytical discussion. Finally, a number of open research challenges are identified. Moreover, the schemes are explored to identify the challenges that are addressed in each scheme.
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“Another challenging task is to manage several devices remotely and without any loss in time or management. Because in this type of framework, there is a need to integrate devices along with their data and information coming from different aspects [208].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [208]
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“Ullah et al. (2021) presented a brief survey on secure transmission and data collection schemes where author has considered the Fog computing architectures.”
BackgroundRetractedLiVoAuth: Liveness Detection in Voiceprint Authentication With Random Challenges and Detection Modes
Zhang, R., Yan, Z., Wang, X., & Deng, R. H. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3213830 · Cited by 10
“6) We assume that the communication channels among UA, RP, and UAP are secure with mutual authentication due to the employment of security protocols [22].”
BackgroundWiryen, Y. B., Woguia, A. V. N., Ngono, J. M., & Fono, L. A. (2023) · International Journal of Smart Security Technologies · doi:10.4018/ijsst.333852 · Cited by 10
A Lightweight Aggregate Authentication Protocol for Internet of Drones
Bhattarai, I., Pu, C., & Choo, K. R. (2024) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc51664.2024.10454703 · Cited by 10
“Lately, several data aggregation techniques have been investigated to reduce data redundancy and improve communication efficiency in the IoT setting [10]–[12].”
BackgroundRole of IoT in Building Smart Healthcare Infrastructure
Rajora, R., Kumar, A., Malhotra, S., & Rajora, A. (2023) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon58793.2023.10270462 · Cited by 9
“Because of poor connectivity and weak network in rural areas ad hoc communication modes like Bluetooth, ZigBee, and WLAN are very popular[34]Different techniques, for example, encryption-decryption, cryptographic[23], steganographic[37][43], machine learning and algorithms are purposed for data security during data tr…”
BackgroundMardenov, Y., Adamova, A., Zhukabayeva, T. K., & Othman, M. (2023) · Journal of Robotics and Control (JRC) · doi:10.18196/jrc.v4i6.20216 · Cited by 9
“There are traditional methods for detecting a faulty sensor node, but their performance is low under various conditions, for example, when deploying a network in adverse conditions [32]-[34].”
MethodCellular Automata, S-Box and DNA Coding Based SPN for Image Encryption
Omran, O., & Alexan, W. (2023) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/hora58378.2023.10156745 · Cited by 8
A Distributed Model for IoT Anomaly Detection Using Federated Learning
Tahir, S., & Zaheer, A. (2023) · Advances in logistics, operations, and management science book series · doi:10.4018/978-1-6684-7625-3.ch003 · Cited by 8
Satyanarayana, P., Rao, G. V. S. P., Dasam, S., Arun, M., Kalpana, D., & Krishnan, V. G. (2023) · International Conference on Mobile Networks and Wireless Communications (ICMNWC) · doi:10.1109/icmnwc60182.2023.10435907 · Cited by 8
“Ullah et al. [18] review the strategies for secure data aggregation, a critical aspect in IoT applications within MANETs.”
BackgroundProvably secure optimal homomorphic signcryption for satellite-based internet of things
Farrea, K. A., Baig, Z. A., Doss, R., & Liu, D. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110516 · Cited by 8
Veena, R. S., Khambra, G., Sayal, R., Singh, N., Veerasamy, B. D., & Sar, S. K. (2024) · SN Computer Science · doi:10.1007/s42979-024-03195-4 · Cited by 8
Designing an Ethical and Secure Pain Estimation System Using AI Sandbox for Contactless Healthcare
Khan, U. A., & Alamäki, A. V. (2023) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v19i15.43663 · Cited by 8
Muniteja, M., Bee, M. K. M., & Suresh, V. (2022) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr56254.2022.9995881 · Cited by 7
“Our group has broad information and examination experience that has convert into excellent distributions [1], [4]–[8], [20], [22], [23], [25], [26], [29], [32], [36], [38], [39], [42] The proposed strategy manages clinical demonstrated information and improves at class lopsidedness and overtraining issues, the exhibit…”
BackgroundRetractedElsisi, M., Su, C., Lin, C.-H., & Ku, T. (2024) · IEEE/IAS Industrial and Commercial Power Systems Technical Conference · doi:10.1109/icps60943.2024.10563334 · Cited by 7
“The IoT has the ability to address issues associated with the conversion of older energy networks to more contemporary smart grids since it offers distributed processing power and bidirectional communication [10, 11].”
BackgroundPimple, J. (2024) · Journal of Electrical Systems · doi:10.52783/jes.696 · Cited by 7
“The authors of [10] enhanced security in medical IoT data and electronic health records (EHRs) within S-Healthcare Services by integrating data access control and data usage auditing mechanisms.”
BackgroundMethodHira, F. A., Khalid, H. B., Rasid, S. Z. A., & Moshiul, A. M. (2022) · International Journal of Academic Research in Business and Social Sciences · doi:10.6007/ijarbss/v12-i5/13101 · Cited by 7
A Review on Secure Data Storage and Data Sharing Technics in Blockchain-based IoT Healthcare Systems
Nasab, S. S. F., Bahrepour, D., & Tabbakh, S. R. K. (2022) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke57176.2022.9959977 · Cited by 8
“On the other hand, some features such as reliability, confidentiality, privacy, and integrity are significant subjects to secure transferring, storage, and sharing of healthcare data [10-12].”
BackgroundComputational Intelligence Ethical Issues in Health Care
Sama, N. U., Zen, K. B., Jhanjhi, N. Z., & Humayun, M. (2024) · Studies in computational intelligence · doi:10.1007/978-981-99-8853-2_21 · Cited by 6
Self-citationMachine Learning and Internet-of-Things Solutions for Microgrid Resilient Operation
Su, C., Rouhani, S. H., Elsisi, M., Ali, Z., Nhat, H. L. Q., & Sadiq, M. (2024) · Power systems · doi:10.1007/978-981-97-6623-9_8 · Cited by 7
Elsisi, M., Su, C., Lin, C.-H., & Ku, T. (2025) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2025.3547704 · Cited by 6
Kumar, A., Verma, D. K., & Dwivedi, R. K. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113209 · Cited by 7
Machine Learning Architecture for Signature-based IoT Intrusion Detection in Smart Energy Grids
Yadav, N., Truong, L. M., Troja, E., & Aliasgari, M. (2022) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon53508.2022.9843007 · Cited by 6
Kumar, K. K., & Kanimozhi, K. V. (2022) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats54253.2022.9759044 · Cited by 6
Cyber Security for Connected wearable Devices
Alshammari, M., & Alshammari, M. (2022) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats54253.2022.9758932 · Cited by 6
“The processor analyzes the information and then sends commands to sensors or other actuators to collect further information or perform preset tasks [1].”
BackgroundSirajudheen, A. M., & David, D. J. (2023) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci56745.2023.10128207 · Cited by 6
“In this view, lightweight cryptography is required for a secure and trustworthy environment to transfer data to the internet [45].”
BackgroundEdge-Enabled Smart Traffic Management System: An IoT Implementation for Urban Mobility
Jabakumar, A. K. (2023) · Research Journal of Computer Systems and Engineering · doi:10.52710/rjcse.85 · Cited by 6
Analytical Optimal Control of Delayed Worm Propagation Model in Heterogeneous IoT Systems
Murthy, B. S. N., Madhusudanan, V., Srinivas, M. N., Guerrini, L., Zeb, A., Dao, N., & Cho, S. (2025) · IEEE Access · doi:10.1109/access.2025.3559146 · Cited by 5
“Researchers have used epidemic models to analyze the behavior of malicious entities on the Internet, providing valuable information on the management of worm propagation within systems [13], [14], [15], [16], [17], [18].”
Jayawardane, O., Induwara, M., Gunathilaka, H. H. C., Fernando, M. M. P. M., Weerasinghe, W. M. S. K. B., & Wijewardhana, U. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3662482 · Cited by 5
“To add to the discussion, the security of the aggregation process also warrants attention, given the sensitivity of health-care data and the potential vulnerabilities of aggregator nodes, such as cluster heads (CHs), to attacks and system failures in hardware, software, or networks [14], [15].”
Influential
Metadata
- Authors
- Ullah, A., Azeem, M., Ashraf, H., Alaboudi, A. A., Humayun, M., & Jhanjhi, N.
- Affiliations
- National University of Modern Languages (PK)
- International Islamic University, Islamabad (PK)
- Shaqra University (SA)
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 16849-16865
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3126450618
- Cited by
- 213
- References
- 123
- Citations by year
- 2026: 16 · 2025: 34 · 2024: 46 · 2023: 49 · 2022: 53 · 2021: 15
- FWCI
- 18.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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
- Internet of Medical Things, secure data transmission, fog computing, healthcare data privacy, data compression, smart cities, intelligent vehicles, smart grid
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