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Trends in IoT based solutions for health care: Moving AI to the edge
Open accessAbstract
In recent times, we assist to an ever growing diffusion of smart medical sensors and Internet of things devices that are heavily changing the way healthcare is approached worldwide. In this context, a combination of Cloud and IoT architectures is often exploited to make smart healthcare systems capable of supporting near realtime applications when processing and performing Artificial Intelligence on the huge amount of data produced by wearable sensor networks. Anyway, the response time and the availability of cloud based systems, together with security and privacy, still represent critical issues that prevents Internet of Medical Things (IoMT) devices and architectures from being a reliable and effective solution to the aim. Lately, there is a growing interest towards architectures and approaches that exploit Edge and Fog computing as an answer to compensate the weaknesses of the cloud. In this paper, we propose a short review about the general use of IoT solutions in health care, starting from early health monitoring solutions from wearable sensors up to a discussion about the latest trends in fog/edge computing for smart health.
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“The computing paradigm is currently shifting from cloud computing to end-edge-cloud computing, which also supports AI evolving from a centralized AI deployment to a distributed artificial intelligence (DAI).”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [41]
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Cites
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2444095 · Cited by 8,630
Large Scale Distributed Deep Networks
Dean, J. B., Corrado, G. S., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P. A., Yang, K., et al. (2012) · Cited by 3,202
The Internet of Things for Health Care: A Comprehensive Survey
Islam, S. M. R., Kwak, D., Kabir, M. H., Hossain, M., & Kwak, K.-S. (2015) · IEEE Access · doi:10.1109/access.2015.2437951 · Cited by 3,199
Fog and IoT: An Overview of Research Opportunities
Chiang, M., & Zhang, T. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2584538 · Cited by 2,362
Pruning Convolutional Neural Networks for Resource Efficient InferencePreprint
Molchanov, P. A., Tyree, S., Karras, T., Aila, T., & Kautz, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.06440 · Cited by 1,487
(2019) · doi:10.1145/3308558 · Cited by 932
Developing Vehicular Data Cloud Services in the IoT Environment
Wu, H., Yan, G., & Xu, L. D. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2299233 · Cited by 565
Real-Time Signal Quality-Aware ECG Telemetry System for IoT-Based Health Care Monitoring
Satija, U., Ramkumar, B., & Manikandan, M. S. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2670022 · Cited by 375
Liu, C., Cao, Y., Luo, Y., Chen, G., Vokkarane, V., Yunsheng, M., Chen, S., & Hou, P. (2018) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2017.2662008 · Cited by 333
Research works printed but not linked: 3
- Azimi, 2017, HiCH: hierarchical fog-assisted computing architecture for healthcare IoT, ACM Trans. Embed. Comput. Syst. (TECS), 16, 174
- Park, 2019, Wireless network intelligence at the edge, Proc. IEEE, 107, 2204, 10.1109/JPROC.2019.2941458 · DOI · Paper page
- Wang, 2020, Convergence of edge computing and deep learning: acomprehensive survey, IEEE Commun. Surv. Tutor. (Early Access), 1
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Chen, C., Ding, S., & Wang, J. (2023) · Nature Medicine · doi:10.1038/s41591-023-02391-8 · Cited by 553
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Rejeb, A., Rejeb, K., Treiblmaier, H., Appolloni, A., Alghamdi, S., Alhasawi, Y., & Iranmanesh, M. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100721 · Cited by 515
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Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366
Jagatheesaperumal, S. K., Rahouti, M., Ahmad, K., Al-Fuqaha, A. I., & Guizani, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3139827 · Cited by 200
Artificial intelligence in healthcare: opportunities and risk for future
Sunarti, S., Fadzlul Rahman, F., Naufal, M., Risky, M., Febriyanto, K., & Masnina, R. (2021) · Gaceta Sanitaria · doi:10.1016/j.gaceta.2020.12.019 · Cited by 274
Edge Intelligence and Internet of Things in Healthcare: A Survey
Amin, S. U., & Hossain, M. S. (2020) · IEEE Access · doi:10.1109/access.2020.3045115 · Cited by 264
The promise of digital healthcare technologies
Yeung, A. W. K., Torkamani, A., Butte, A. J., Glicksberg, B. S., Schuller, B. W., Rodríguez, B., Ting, D. S. W., Bates, D. W., Schaden, E., Peng, H., et al. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1196596 · Cited by 284
A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications
Bharadwaj, H. K., Agarwal, A., Chamola, V., Lakkaniga, N. R., Hassija, V., Guizani, M., & Sikdar, B. (2021) · IEEE Access · doi:10.1109/access.2021.3059858 · Cited by 208
IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution
Ndiaye, M., Oyewobi, S. S., Abu-Mahfouz, A. M. I., Hancke, G. P., Kurien, A. M., & Djouani, K. (2020) · IEEE Access · doi:10.1109/access.2020.3030090 · Cited by 175
An Internet-of-Medical-Things-Enabled Edge Computing Framework for Tackling COVID-19
Rahman, M. A., & Hossain, M. S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3051080 · Cited by 176
Rasool, R. U., Ahmad, H. F., Rafique, W., Qayyum, A., & Qadir, J. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103332 · Cited by 177
Heydari, S., & Mahmoud, Q. H. (2025) · Sensors · doi:10.3390/s25103191 · Cited by 223
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Putra, K. T., Arrayyan, A. Z., Hayati, N. Q., Firdaus, F., Damarjati, C., Bakar, A., & Chen, H. (2024) · IEEE Access · doi:10.1109/access.2024.3358827 · Cited by 164
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Navaz, A. N., Serhani, M. A., Kassabi, H. T. E., Al-Qirim, N. A. Y., & Ismail, H. (2021) · IEEE Access · doi:10.1109/access.2021.3079217 · Cited by 109
Edge AI for Internet of Medical Things: A literature review
Rocha, A. R. D., Monteiro, M. S., Mattos, C. L. C., Dias, M. L. D., Soares, J. B., Magalhães, R. P., & de Macêdo, J. A. F. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109202 · Cited by 99
Edge-Based Hybrid System Implementation for Long-Range Safety and Healthcare IoT Applications
Wu, F., Qiu, C., Wu, T., & Yuce, M. R. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3050445 · Cited by 96
Internet of things in health: Requirements, issues, and gaps
Calvillo‐Arbizu, J., Román, I., & Reina‐Tosina, J. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106231 · Cited by 82
Diab, M. S., & Rodriguez–Villegas, E. (2022) · IEEE Access · doi:10.1109/access.2022.3206782 · Cited by 92
Investigating the Spread of Coronavirus Disease via Edge-AI and Air Pollution Correlation
Gomathy, V., Janarthanan, K., Al-Turjman, F. M., Sitharthan, R., Rajesh, M., Vengatesan, K., & Reshma, T. P. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3424222 · Cited by 78
Navigating the nexus of AI and IoT: A comprehensive review of data analytics and privacy paradigms
Marengo, A. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101318 · Cited by 93
Application of artificial intelligence in medical technologies: a systematic review of main trends.
Bitkina, O. V., Park, J., & Kim, H. K. (2023) · Digital Health · doi:10.1177/20552076231189331 · Cited by 66
Kamruzzaman, M. M., Alrashdi, I., & Alqazzaz, A. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/2950699 · Cited by 68
RetractedNew opportunities, challenges, and applications of edge-ai for connected healthcare in smart cities
Kamruzzaman, M. M. (2021) · 2021 IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps52748.2021.9682055 · Cited by 67
Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery
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Investigating Healthcare 4.0 Transition Through a Knowledge Management Perspective
Abbate, S., Centobelli, P., Cerchione, R., Oropallo, E., & Riccio, E. (2022) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2022.3200889 · Cited by 65
Security of Cloud-Based Medical Internet of Things (MIoTs)
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Shakya, S. (2020) · Journal of Ubiquitous Computing and Communication Technologies · doi:10.36548/jucct.2020.2.005 · Cited by 58
Cesare, M. G. D., Perpetuini, D., Cardone, D., & Merla, A. (2024) · Sensors · doi:10.3390/s24051499 · Cited by 58
Prediction of health monitoring with deep learning using edge computing
Gupta, P., Chouhan, A., Wajeed, M. A., Tiwari, S., Bist, A. S., & Puri, S. C. (2022) · Measurement Sensors · doi:10.1016/j.measen.2022.100604 · Cited by 57
Edge-Assisted Solutions for IoT-Based Connected Healthcare Systems: A Literature Review
Hayyolalam, V., Aloqaily, M., Özkasap, Ö., & Guizani, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3135200 · Cited by 59
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Khadidos, A. O., Selvarajan, S., Khadidos, A. O., Sangeetha, K., & Alyoubi, K. H. (2022) · Journal of Sensors · doi:10.1155/2022/8457116 · Cited by 52
Utilizing the Internet of Things (IoT) to address uncertain home health care supply chain network
Salehi-Amiri, A., Jabbarzadeh, A., Hajiaghaei–Keshteli, M., & Chaabane, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118239 · Cited by 53
El-Aziz, R. M. A., Rayan, A., Shahin, O. R., Elhadad, A., Abozeid, A., Taloba, A. I., & Al–Shalabi, R. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7425846 · Cited by 49
Coelho, D., Costa, D., Rocha, E. M., Almeida, D., & Santos, J. P. O. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.01.318 · Cited by 49
Wireless Battery-free and Fully Implantable Organ Interfaces
Bhatia, A., Hanna, J., Stuart, T., Kasper, K. A., Clausen, D. M., & Gutruf, P. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00425 · Cited by 50
Fog Computing in Healthcare: A Review
Pareek, K., Tiwari, P. K., & Bhatnagar, V. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1099/1/012025 · Cited by 48
Development of Edge-IoMT Computing Architecture for Smart Healthcare Monitoring Platform
Dilibal, C. (2020) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit50672.2020.9254501 · Cited by 47
LoRaWAN-based hybrid internet of wearable things system implementation for smart healthcare
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Dave, R., Seliya, N., & Siddiqui, N. (2021) · Journal of Computer Sciences and Applications · doi:10.12691/jcsa-9-1-3 · Cited by 43
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Edge-Assisted IoMT-Based Smart-Home Monitoring System for the Elderly With Chronic Diseases
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Chen, Y., Kao, Y., Hsu, C., Chen, C., Ma, Y., Shen, Y., Liu, T., Hsu, S., Lin, H., Wang, J., et al. (2021) · Academic Emergency Medicine · doi:10.1111/acem.14339 · Cited by 31
Compressing Deep Model With Pruning and Tucker Decomposition for Smart Embedded Systems
Dai, C., Liu, X., Cheng, H., Yang, L. T., & Deen, M. J. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3116316 · Cited by 31
Gupta, D., Bhatia, M. P. S., & Kumar, A. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3095853 · Cited by 30
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Jovanović, L., Damaševičius, R., Matić, R., Kabiljo, M., Šimić, V., Kunjadić, G., Antonijević, M., Zivkovic, M. Z., & Bačanin, N. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2031 · Cited by 30
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Lederman, R., Ben‐Assuli, O., & Vo, T. H. (2021) · Health Policy and Technology · doi:10.1016/j.hlpt.2021.100552 · Cited by 27
Tang, Z., Hu, H., Xu, C., & Zhao, K. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18179037 · Cited by 31
Paper-based field-effect transistor sensors
Bushra, K. A., & Prasad, K. S. (2021) · Talanta · doi:10.1016/j.talanta.2021.123085 · Cited by 27
The Role of AI in Biochips for Early Disease Detection
Sukumaran, C., Indhumathi, K., Balamurugan, P., Ambilwade, R. P., Sunthari, P. M., & Natrayan, L. (2023) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs59847.2023.10390419 · Cited by 25
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Towards the optimality of service instance selection in mobile edge computing
Zou, G., Qin, Z., Deng, S., Li, K., Gan, Y., & Zhang, B. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106831 · Cited by 24
Panicker, R. M., & Chandrasekaran, B. (2022) · Sport Sciences for Health · doi:10.1007/s11332-021-00885-x · Cited by 22
Energy-efficient fuzzy data offloading for IoMT
Singh, N., & Das, A. K. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109127 · Cited by 22
An intelligent blockchain strategy for decentralised healthcare framework
Goel, A., & Neduncheliyan, S. (2023) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-022-01429-x · Cited by 22
Cai, W., Gao, M., Ding, Y., Ning, X., Bai, X. P., & Qian, P. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3320044 · Cited by 22
Data Mining on Smartphones: An Introduction and Survey
Yates, D., & Islam, M. Z. (2022) · ACM Computing Surveys · doi:10.1145/3529753 · Cited by 21
Awotunde, J. B., Imoize, A. L., Ayoade, O. B., Abiodun, M. K., Do, D., Silva, A., & Sur, S. N. (2022) · Sensors · doi:10.3390/s22249689 · Cited by 21
Fundus Imaging-Based Healthcare: Present and Future
Kumar, V., & Paul, K. (2023) · ACM Transactions on Computing for Healthcare · doi:10.1145/3586580 · Cited by 21
Castañeda, W. A. C., & Bertemes-Filho, P. (2024) · Sensors · doi:10.3390/s24247965 · Cited by 24
IoT-Based Real-Time Monitoring System for Epidemic Diseases Patients: Design and Evaluation
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Edge AI-based Respiratory Disease Recognition from Exhaled Breath Signatures
Ooko, S. O., Mukanyiligira, D., Munyampundu, J. P., & Nsenga, J. (2021) · IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) · doi:10.1109/jeeit53412.2021.9634140 · Cited by 19
A secure framework for IoT ‐based healthcare using blockchain and IPFS
Rani, D., Kumar, R., & Chauhan, N. (2023) · Security and Privacy · doi:10.1002/spy2.348 · Cited by 21
Kielienyu, S., Kantarcı, B., Turgut, D., & Khan, S. (2020) · doi:10.1145/3416012.3424624 · Cited by 19
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An efficient lightweight speck technique for edge-IoT-based smart healthcare systems
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Al‐Quayed, F., Humayun, M., & Tahir, S. (2023) · Healthcare · doi:10.3390/healthcare11162257 · Cited by 24
Federated clustered multi-domain learning for health monitoring
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Mohan, H. M., Anitha, S., Chai, R., & Ling, S. H. (2021) · Advances in Human-Computer Interaction · doi:10.1155/2021/6483003 · Cited by 18
Machine learning and IoT in healthcare: Recent advancements, challenges & future direction
Zonayed, M., Tasnim, R., Jhara, S. S., Mimona, M. A., Hussein, M. R., Mobarak, M. H., & Salma, U. (2025) · Advances in Biomarker Sciences and Technology · doi:10.1016/j.abst.2025.08.006 · Cited by 24
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Goethals, T., Volckaert, B., & Turck, F. D. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3116437 · Cited by 17
Acceleration-based gait analysis for frailty assessment in older adults
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Seamless Transition From Machine Learning on the Cloud to Industrial Edge Devices With Thinger.io
Bustamante, Á. L., Patricio, M. A., Berlanga, A., & Molina, J. M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3268771 · Cited by 17
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Metadata
- Authors
- Greco, L., Percannella, G., Ritrovato, P., Tortorella, F., & Vento, M.
- Affiliations
- University of Salerno (IT)
- Corresponding author
- Greco, Luca
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 135
- Pages
- 346-353
- ISSN
- 0167-8655 (ISSN-L)
- PMC
- PMC7217772
- OpenAlex
- W3025245379
- Cited by
- 318
- References
- 61
- Citations by year
- 2026: 15 · 2025: 36 · 2024: 53 · 2023: 72 · 2022: 65 · 2021: 67 · 2020: 10
- FWCI
- 25.14
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Pattern Recognition Letters (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
- Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Keywords
- Internet of Medical Things, edge computing, fog computing, wearable sensors, smart health monitoring, artificial intelligence in healthcare, response time, privacy and security, near real-time processing
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