The DOI matches its Crossref record; 60 of the 61 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Edge computing in smart health care systems: Review, challenges, and research directions
Hartmann, M., Hashmi, U. S., & Imran, M. A. (2019) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3710 · Cited by 323
Maghded, H. S., Ghafoor, K. Z., Sadiq, A. S., Curran, K., Rawat, D. B., & Rabie, K. (2020) · 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI) · doi:10.1109/iri49571.2020.00033 · Cited by 279
Cloud computing for Internet of Things & sensing based applications
Rao, B. B. P., Saluia, P., Sharma, N., Mittal, A., & Sharma, S. (2012) · International Conference on Sensing Technology · doi:10.1109/icsenst.2012.6461705 · Cited by 275
Edge Computing for Smart Health: Context-Aware Approaches, Opportunities, and Challenges
Abdellatif, A. A., Mohamed, A. M. S., Chiasserini, C. F., Tlili, M., & Erbad, A. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800083 · Cited by 273
A Secure IoT-Based Healthcare System With Body Sensor Networks
Yeh, K. (2016) · IEEE Access · doi:10.1109/access.2016.2638038 · Cited by 246
Smart Health Solution Integrating IoT and Cloud: A Case Study of Voice Pathology Monitoring
Muhammad, G., Rahman, S. M. M., Alelaiwi, A., & Alamri, A. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600425cm · Cited by 244
A Novel Intelligent Medical Decision Support Model Based on Soft Computing and IoT
Abdel‐Basset, M., Manogaran, G., Gamal, A., & Chang, V. I. C. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2931647 · Cited by 238
A First Look at Deep Learning Apps on Smartphones
Xu, M., Liu, J., Liu, Y., Lin, F. X., Liu, Y., & Liu, X. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313591 · Cited by 190
Uddin, M. Z. (2018) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2018.08.010 · Cited by 188
Azimi, I., Anzanpour, A., Rahmani, A. M., Pahikkala, T., Levorato, M., Liljeberg, P., & Dutt, N. D. (2017) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3126501 · Cited by 177
Trust-Based Decision Making for Health IoT Systems
Al-Hamadi, H., & Chen, I. R. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2736446 · Cited by 156
Queralta, J. P., Gia, T. N., Tenhunen, H., & Westerlund, T. (2019) · International Conference on Telecommunications and Signal Processing (TSP) · doi:10.1109/tsp.2019.8768883 · Cited by 154
Delivering home healthcare through a Cloud-based Smart Home Environment (CoSHE)
Pham, M., Mengistu, Y., Do, H., & Sheng, W. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.10.040 · Cited by 144
Edge Computing with Cloud for Voice Disorder Assessment and Treatment
Muhammad, G., Alhamid, M. F., Alsulaiman, M., & Gupta, B. B. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700790 · Cited by 137
Machine Learning on Mobile: An On-device Inference App for Skin Cancer Detection
Dai, X., Spasić, ., Meyer, B., Chapman, S., & Andrès, F. (2019) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec.2019.8795362 · Cited by 123
Klonoff, D. C. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817717007 · Cited by 110
A Fog-Based Healthcare Framework for Chikungunya
Sood, S. K., & Mahajan, I. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2768407 · Cited by 110
Health monitoring based on IoT using Raspberry PI
Kaur, A., & Jasuja, A. (2017) · International Conference on Computing, Communication and Automation · doi:10.1109/ccaa.2017.8230004 · Cited by 106
Fog-assisted personalized healthcare-support system for remote patients with diabetes
Devarajan, M., Subramaniyaswamy, V., Vijayakumar, V., & Ravi, L. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01291-5 · Cited by 105
Empowering healthcare IoT systems with hierarchical edge-based deep learning
Azimi, I., Takalo-Mattila, J., Anzanpour, A., Rahmani, A. M., Soininen, J.-P., & Liljeberg, P. (2018) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1145/3278576.3278597 · Cited by 97
Sareen, S., Gupta, S. K., & Sood, S. K. (2017) · Enterprise Information Systems · doi:10.1080/17517575.2016.1277558 · Cited by 95
Fit: A Fog Computing Device for Speech Tele-Treatments
Monteiro, A., Dubey, H., Mahler, L. A., Yang, Q., & Mankodiya, K. (2016) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp.2016.7501692 · Cited by 90
Poniszewska-Marańda, A., Kaczmarek, D., Kryvinska, N., & Xhafa, F. (2018) · Computing · doi:10.1007/s00607-018-0680-z · Cited by 86
An edge-stream computing infrastructure for real-time analysis of wearable sensors data
Greco, L., Ritrovato, P., & Xhafa, F. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.10.058 · Cited by 85
Self-citationCloud, fog and edge: Cooperation for the future?
Bierzynski, K., Escobar-Molero, A., & Eberl, M. (2017) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec.2017.7946409 · Cited by 71
Monitoring and Detection Platform to Prevent Anomalous Situations in Home Care
González, G. V., Bajo, J., Paz, J. F. D., & Corchado, J. M. (2014) · Sensors · doi:10.3390/s140609900 · Cited by 70
Smart Health Care: An Edge-Side Computing Perspective
Akmandor, A. O., & Jha, N. K. (2017) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2017.2746096 · Cited by 68
Maghdid, H. S., Ghafoor, K. Z., Sadiq, A. S., Curran, K., Rawat, D. B., & Rabie, K. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.07434 · Cited by 67
WiSPH: A Wireless Sensor Network-Based Home Care Monitoring System
Magaña-Espinoza, P., Aquino-Santos, R., Cárdenas-Benítez, N., Aguilar-Velasco, J., Buenrostro-Segura, C., Edwards-Block, A., & Cass, A. M. (2014) · Sensors · doi:10.3390/s140407096 · Cited by 66
Fog-Enabled Smart Health: Toward Cooperative and Secure Healthcare Service Provision
Tang, W., Zhang, K., Zhang, D., Ren, J., Zhang, Y., & Shen, X. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1800234 · Cited by 62
Fog-to-cloud Computing (F2C): The key technology enabler for dependable e-health services deployment
Masip‐Bruin, X., Marín‐Tordera, E., Alonso, A., & García, J. (2016) · Annual Mediterranean Ad Hoc Networking Workshop · doi:10.1109/medhocnet.2016.7528425 · Cited by 60
Priyadarshini, R., Barik, R. K., & Dubey, H. (2018) · Computation · doi:10.3390/computation6040062 · Cited by 57
Dedicated real‐time monitoring system for health care using ZigBee
Alwan, O. S., & Rao, K. P. (2017) · Healthcare Technology Letters · doi:10.1049/htl.2017.0030 · Cited by 47
AutoTriage - An Open Source Edge Computing Raspberry Pi-based Clinical Screening SystemPreprint
Hegde, C., Jiang, Z., Suresha, P. B., Zelko, J. S., Seyedi, S. S., Smith, M. A., Wright, D. W., Kamaleswaran, R., Reyna, M. A., & Clifford, G. D. (2020) · medRxiv · doi:10.1101/2020.04.09.20059840 · Cited by 45
Stream query processing for healthcare bio-sensor applications
Chen, C., Agrawal, H., Cochinwala, M., & Rosenbluth, D. (2004) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2004.1320048 · Cited by 42
Reconstruction of Angular Kinematics From Wrist-Worn Inertial Sensor Data for Smart Home Healthcare
Villeneuve, E., Harwin, W., Holderbaum, W., Janko, B., & Sherratt, R. S. (2017) · IEEE Access · doi:10.1109/access.2016.2640559 · Cited by 36
Offloaded Execution of Deep Learning Inference at Edge: Challenges and Insights
Dey, S., Mondal, J., & Mukherjee, A. (2019) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2019.8730817 · Cited by 31
Mathur, N., Paul, G., Irvine, J. M., Abuhelala, M., Buis, A. W. P., & Glesk, I. (2016) · IEEE Access · doi:10.1109/access.2016.2622163 · Cited by 29
Edge computing health model using P2P-based deep neural networks
Chung, K., & Yoo, H. (2019) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-019-00738-y · Cited by 27
Ram, S. S., Apduhan, B. O., & Shiratori, N. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-24302-9_30 · Cited by 24
2016 Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net)
(2016) · doi:10.1109/med-hoc-net38159.2016 · Cited by 23
Vehicular Cloud and Fog Computing Architecture, Applications, Services, and Challenges
Gaba, P., & Raw, R. S. (2020) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-2570-8.ch014 · Cited by 15
New Trends in Intelligent Software Methodologies, Tools and Techniques
(2024) · Frontiers in artificial intelligence and applications · doi:10.3233/faia389 · Cited by 10
IoT and Cloud Computing Advancements in Vehicular Ad-Hoc Networks
(2020) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-2570-8 · Cited by 8
Recent Advances on Fog Health – A Systematic Literature Review
Hidayah, I. W. N., Ali, S., Krejcar, O., & Chaudhry, J. A. (2018) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-900-3-157 · Cited by 5
Flagged first, then by citations
1 of the 61 references couldn’t be matched to a known paper.
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
Cited by
Sorted by citations
Digital health for aging populations
Chen, C., Ding, S., & Wang, J. (2023) · Nature Medicine · doi:10.1038/s41591-023-02391-8 · Cited by 553
The Internet of Things (IoT) in healthcare: Taking stock and moving forward
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
A Comprehensive Survey on TinyML
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
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
