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
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
Items 101–200 of 318
Raeve, P. D., Davidson, P. M., Shaffer, F. A., Pol, E., Pandey, A. K., & Adams, E. (2021) · Open Research Europe · doi:10.12688/openreseurope.13231.1 · Cited by 15
Incorporating Deep Learning Methodologies into the Creation of Healthcare Systems
Khan, S., Ch, V., Sekaran, K., Joshi, K. K., Roy, C. K., & Tiwari, M. (2023) · International Conference on Artificial Intelligence and Smart Communication (AISC) · doi:10.1109/aisc56616.2023.10085651 · Cited by 15
IoT-Based Smart Air Conditioner as a Preventive in the Post-COVID-19 Era: A Review
Saputra, D. I. S., Suarnatha, I. P. D., Mahardika, F., Wijanarko, A., & Handani, S. W. (2023) · Journal of Robotics and Control (JRC) · doi:10.18196/jrc.v4i1.17090 · Cited by 21
Survey on Edge, Fog Assisted IoT Framework Using Intelligent Learning Techniques
Saraf, P. D., Bartere, M. M., & Lokulwar, P. P. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-6407-6_17 · Cited by 14
Stool Image Analysis for Digital Health Monitoring By Smart Toilets
Zhou, J., McNabb, J., DeCapite, N., Ruiz, J. R., Fisher, D. A., Grego, S., & Chakrabarty, K. (2022) · Springer eBooks · doi:10.1007/978-3-032-09253-3_5 · Cited by 15
Towards Edge AI and Varied Approaches of Digital Wellness in Healthcare Administration: A Study
Vyas, S., Gupta, S., & Shukla, V. K. (2023) · International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) · doi:10.1109/iccike58312.2023.10131857 · Cited by 15
Few-shot transfer learning for wearable IMU-based human activity recognition
Ganesha, H. S., Gupta, R., Gupta, S. H., & Rajan, S. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09645-7 · Cited by 16
Social Media and the Internet of Things for Emergency and Disaster Medicine Management
Benis, A. (2022) · Studies in health technology and informatics · doi:10.3233/shti220011 · Cited by 14
Grgurić, A., Khan, O. A., Ortega-Gil, A., Markakis, E., Pozdniakov, K., Christos Kloukinas, Z., Medrano, A., Gaeta, E., Fico, G., & Koloutsou, K. (2021) · Electronics · doi:10.3390/electronics10141616 · Cited by 13
Melanoma Detection using Advanced Deep Neural Network
Sharma, P., Gautam, A., Nayak, R., & Balabantaray, B. K. (2022) · International Conference on Energy, Power and Environment (ICEPE) · doi:10.1109/icepe55035.2022.9798123 · Cited by 13
Utilizing ferrorestorable polarization in energy-storage ceramic capacitors
Matsuo, H., Utsunomiya, M., & Noguchi, Y. (2022) · NPG Asia Materials · doi:10.1038/s41427-022-00426-z · Cited by 13
Dao, T. C., Tam, N. T., & Bình, H. T. T. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107463 · Cited by 12
A new priority aware routing protocol for efficient emergency data transmissions in MANETs
Özen, Y., & ÖZEN, G. Z. (2024) · Ad Hoc Networks · doi:10.1016/j.adhoc.2024.103592 · Cited by 12
A collaborative WSN‐IoT‐Animal for large‐scale data collection
Aziz, H. A., Ari, A. A. A., Njoya, A. N., Djedouboum, A. C., Mohamadou, A., & Thiaré, O. (2024) · IET Smart Cities · doi:10.1049/smc2.12089 · Cited by 12
Yu, T. S., Song, S., Yea, J., & Jang, K. (2024) · Advanced Sensor Research · doi:10.1002/adsr.202400048 · Cited by 14
RADIoT: The Unifying Framework for IoT, Radiomics and Deep Learning Modeling
Folorunso, S. O., Awotunde, J. B., Ayo, F. E., & Abdullah, K.-K. A. (2021) · Intelligent systems reference library · doi:10.1007/978-981-16-2972-3_6 · Cited by 12
Effectiveness of Machine and Deep Learning in IOT-Enabled Devices for Healthcare System
Kumar, Y., & Singla, R. P. (2022) · Internet of things · doi:10.1007/978-3-030-81473-1_1 · Cited by 12
Development and Performance Evaluation of an IoT-Integrated Breath Analyzer
Khamis, A. A., Idris, A., Abdellatif, A., Rom, N. A. M., Khamis, T., Karim, M. S. A., Janasekaran, S., & Rashid, R. B. A. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20021319 · Cited by 12
Review of Recent Technologies for Tackling COVID-19
Alharbi, A. A., & Rahman, M. A. (2021) · SN Computer Science · doi:10.1007/s42979-021-00841-z · Cited by 14
Role of artificial intelligence in health monitoring using IoT based wearable sensors: A survey
Shaw, L., & Gohel, H. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101761 · Cited by 11
Real-time arrhythmia detection using convolutional neural networks
Vu, T., Petty, T., Yakut, K., Usman, M., Xue, W., Haas, F. M., Hirsh, R. A., & Zhao, X. (2023) · Frontiers in Big Data · doi:10.3389/fdata.2023.1270756 · Cited by 12
Wearable approaches for non-invasive monitoring of tendons: A scoping review
Kárason, H., Ritrovato, P., Maffulli, N., Boccaccini, A. R., & Tortorella, F. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101199 · Cited by 14
Self-citationNalini, M., Malarvizhi, N., Abi, N. E., Seerangan, K., Balusamy, B., & Selvarajan, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-71506-z · Cited by 10
Deep Learning-Based Speech Enhancement for Robust Sound Classification in Security Systems
Mensah, S. Y., Zhang, T., Mahmud, N. A., & Geng, Y. (2025) · Electronics · doi:10.3390/electronics14132643 · Cited by 9
Dogani, J., Farahmand, M., & Daryanavard, H. (2021) · ETRI Journal · doi:10.4218/etrij.2021-0044 · Cited by 10
IoT-Inspired Reliable Irregularity-Detection Framework for Education 4.0 and Industry 4.0
Verma, A., Anand, D., Singh, A. D., Vij, R., Alharbi, A., Alshammari, M., & Ortega, A. (2022) · Electronics · doi:10.3390/electronics11091436 · Cited by 10
Smart Healthcare, IoT and Machine Learning: A Complete Survey
Bellandi, V., Ceravolo, P., Damiani, E., & Siccardi, S. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-83620-7_13 · Cited by 10
Lim, S., & Rahmani, R. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.10.050 · Cited by 11
Fog Computing in Healthcare: Systematic Review
Navakauskas, D., & Kazlauskas, M. (2023) · Informatica · doi:10.15388/23-infor525 · Cited by 9
A Multi-Cache System for On-Chip Memory Optimization in FPGA-Based CNN Accelerators
Pacini, T., Rapuano, E., Dinelli, G., & Fanucci, L. (2021) · Electronics · doi:10.3390/electronics10202514 · Cited by 9
Cognitive Hexagon-Controlled Intelligent Speech Interaction System
Chaurasiya, H. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2022.3168807 · Cited by 9
IoT Applications for Health Care
Agarwal, S. (2022) · Advanced technologies and societal change · doi:10.1007/978-981-19-0770-8_8 · Cited by 9
IoT enabled lung cancer detection and routing algorithm using CBSOA ‐based ShCNN
Raj, E. S. G. S., Jingle, I. D. J., Maram, B., & Ananth, J. P. (2022) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.3518 · Cited by 9
RETRACTED ARTICLE: Latency aware smart health care system using edge and fog computing
Ullah, A., Yasin, S., & Alam, T. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16899-1 · Cited by 9
RetractedThe Internet of Medical Things in the Patient-Centered Digital Clinic’s Ecosystem
Kryvenko, I., Hrynzovskyi, A., & Chalyy, K. A. (2023) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-35467-0_31 · Cited by 9
Machine Learning Applications for IoT Healthcare
Agarwal, N., Singh, P., Singh, N., Singh, K. K., & Jain, R. (2021) · Wiley eBooks · doi:10.1002/9781119761884.ch6 · Cited by 10
Real-Time Ecg Analysis with Recurrent Neural Networks in Cloud-Based Healthcare
Bhardwaj, D., Bordoloi, D., Deepak, A., Srivastava, A. P., Mayuri, K., & Khan, A. K. (2023) · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) · doi:10.1109/icaiihi57871.2023.10489623 · Cited by 8
Multi-Layered Continuous Reasoning for Cloud-IoT Application Management
Herrera, J. L., Berrocal, J., Forti, S., Antonio Brogi, A., & Murillo, J. M. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3451239 · Cited by 8
IoT Enabled Smart Healthcare Assistance for Early Prediction of Health Abnormality
Girish, M., Pallam, A., Divyashree, P., Khare, A., & Dwivedi, P. (2021) · IEEE International Symposium on Smart Electronic Systems (iSES) · doi:10.1109/ises52644.2021.00063 · Cited by 8
Design and Implementation of an Interoperable IoT Based Health Monitoring System for Diabetes
Bollimuntha, M., & Murugan, K. (2023) · International Conference on Smart Systems and Inventive Technology (ICSSIT) · doi:10.1109/icssit55814.2023.10061057 · Cited by 8
Hassan, A. A., Tütüncü, K., Abdullahi, H. O., & Ali, A. F. (2023) · Instrumentation Mesure Métrologie · doi:10.18280/i2m.220602 · Cited by 8
Yang, E., Parvathy, V. S., Selvi, P. P., Shankar, K., Seo, C., Joshi, G. P., & Yi, O. (2020) · Mathematics · doi:10.3390/math8111871 · Cited by 8
An Integrated Secure Efficient Computing Architecture for Embedded and Remote ECG Diagnosis
Bhattarai, A., & Peng, D. (2022) · SN Computer Science · doi:10.1007/s42979-022-01465-7 · Cited by 8
Amangeldy, B., Imankulov, T., Tasmurzayev, N., Aibagarov, S., Azatbekuly, N., Dikhanbayeva, G., & Mukhanbet, A. (2025) · Buildings · doi:10.3390/buildings15152694 · Cited by 7
Alegria, B., Wong, L., & Bedriniana, D. (2022) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct56874.2022.9953893 · Cited by 7
Patient Health Monitoring using Fog and Edge Computing
Gowrishankar, V., Jayakumar, T., Parameswaran, S., Senthilkumar, M., Lekashri, S., & Rajesh Kumar, B. (2023) · International Conference on Sustainable Communication Networks and Application (ICSCNA) · doi:10.1109/icscna58489.2023.10370652 · Cited by 7
Health Monitoring Transforming Using IoT: A Review
Nidhi, N., Rawat, B. S., Srivastava, A., & Garg, N. (2024) · IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) · doi:10.1109/ic2pct60090.2024.10486795 · Cited by 7
Hybrid AI and IoT Approaches Used in Health Care for Patients Diagnosis
Mathur, S., Sharma, A., & Meesad, P. (2021) · Intelligent systems reference library · doi:10.1007/978-981-16-2972-3_5 · Cited by 7
Internet of Things-based smart helmet to detect possible COVID-19 infections
Ahlawat, C., & Krishnamurthi, R. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824557-6.00004-2 · Cited by 7
RETRACTED: Analytical review on deep learning and IoT for smart healthcare monitoring system
Yempally, S., Singh, S. K., & Sarveshwaran, V. (2022) · International Journal of Intelligent Unmanned Systems · doi:10.1108/ijius-02-2022-0019 · Cited by 7
RetractedGeetha, G., Krishna, R. R., Vyas, S., Sukhwal, I., Jain, A. K., Chaturvedi, A., & Shah, M. A. (2022) · Journal of Food Quality · doi:10.1155/2022/4281237 · Cited by 7
RetractedYang, C.-H., Liu, Y.-Y., Chiang, C.-H., & Su, Y.-W. (2022) · Annals of Operations Research · doi:10.1007/s10479-022-05016-4 · Cited by 7
Survey: An IOT Based Approach for Hybrid Security Management in Health Care for Secure Cloud Storage
Gogte, P., Purve, S., Bhave, R., Pethe, D., Chandankhede, P. H., & Thakre, B. P. (2023) · International Conference on Emerging Trends in Engineering and Technology (ICETET) · doi:10.1109/icetet-sip58143.2023.10151578 · Cited by 7
Ethics of Digital Health in Islamic Perspective
Gamon, A. M. I. (2023) · Journal of Science and Technology · doi:10.20428/jst.v28i1.1993 · Cited by 10
Quality and Performance Measures in Healthcare Systems Using Fog Computing
Patel, R., Acharjya, A., Patel, P., Sivaiah, B., & Sahoo, B. (2023) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-6684-4466-5.ch006 · Cited by 6
Machine Learning and IoT in Health 4.0
Darwish, D. A. M. (2024) · Studies in computational intelligence · doi:10.1007/978-981-97-5624-7_7 · Cited by 6
A Review on Classification and Retrieval of Biomedical Images Using Artificial Intelligence
Greeshma, K. V., & Gripsy, J. V. (2021) · Internet of things · doi:10.1007/978-3-030-75220-0_3 · Cited by 6
A Secure Healthcare Monitoring System for Disease Diagnosis in the IoT Environment
Verma, A., Gupta, A. K., Kumar, V., Rajak, A., Kumar, S. G., & Panda, R. N. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19131-w · Cited by 6
Kesavan, R., Palanichamy, N., & Thirumurugan, T. (2023) · JOIV International Journal on Informatics Visualization · doi:10.62527/joiv.7.4.2399 · Cited by 6
Entrust: usEr ceNtric plaTform foR continoUS healThcare
D’Aniello, G., Greco, L., Pupo, F., & Savaglio, C. (2024) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot61029.2024.00093 · Cited by 6
Self-citationDigital Advancements in Retinal Models of Care in the Post–COVID-19 Lockdown Era
Ong, J., Tan, G. S. W., Ang, M., & Chhablani, J. K. (2022) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000533 · Cited by 6
Gušev, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824054-0.00020-4 · Cited by 6
Fall Detection with Artificial Intelligence and IoT Health Monitoring System
Guamán-Egas, J., & Castro-Martin, A. P. (2023) · IEEE Ecuador Technical Chapters Meeting (ETCM) · doi:10.1109/etcm58927.2023.10308997 · Cited by 5
Simulating Fog of Medical Things: Research Challenges and Opportunities
Pati, A., Panigrahi, A., Parhi, M., Pattanayak, B. K., Sahu, B., & Kant, S. (2024) · IEEE Access · doi:10.1109/access.2024.3468015 · Cited by 5
IoT based on Health Caring Systems Survey
Mahmood, A. F., & Rafaa, M. M. (2021) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/100194 · Cited by 5
Remote Attestation as a Service for Edge-Enabled IoT
Calvo, M., & Beltrán, M. (2021) · IEEE International Conference on Services Computing · doi:10.1109/scc53864.2021.00046 · Cited by 5
Zhang, X., Wang, F., Wang, D., Xiang, Y., & Zhang, Z. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/9689769 · Cited by 5
RetractedCloud and Machine Learning Based Solutions for Healthcare and Prevention
Jumani, A. K., Siddique, W. A., & Laghari, A. A. (2023) · Springer eBooks · doi:10.1007/978-3-031-22959-6_10 · Cited by 5
eHealth: Enabling technologies, opportunities and challenges
Makina, H., Letaïfa, A. B., & Rachedi, A. (2023) · Advances in computers · doi:10.1016/bs.adcom.2023.04.001 · Cited by 6
Security of Medical Internet of Things (MIoT)- A Bibliometric Analysis
Patil, R. Y., Patil, Y. H., & Bannore, A. (2023) · Journal of Engineering Science and Technology Review · doi:10.25103/jestr.163.22 · Cited by 5
MUMIPLOP: optimal multi-core IoT service placement in fog environments
Herrera, J. L., Scotece, D., Galán–Jiménez, J., Berrocal, J., Modica, G., & Foschini, L. (2025) · Computing · doi:10.1007/s00607-025-01460-9 · Cited by 5
Bibliometric Analysis of Scientific Productivity around Edge Computing and the Internet of Things
Moreno-Guerrero, A.-J., Lucena, F. J. H., Navas-Parejo, M. R., & García, G. G. (2020) · IoT · doi:10.3390/iot1020024 · Cited by 6
Artificial Intelligence for Smart Data Storage in Cloud-Based IoT
Singh, P., Singh, N., Luxmi, P. R., & Saxena, A. (2022) · Springer eBooks · doi:10.1007/978-3-030-86749-2_1 · Cited by 5
Бердутин, В. А., Abaeva, O. P., Romanova, T. E., & Romanov, S. V. (2023) · Sociology of Medicine · doi:10.17816/socm106054 · Cited by 5
Deep Neural Artificial Intelligence for IoT Based Tele Health Data Analytics
Sivakumar, N. R., & Ibrahim, A. Z. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.019041 · Cited by 5
Next-Gen Telehealth: A Low-Latency IoT and Edge AI Framework for Personalized Remote Diagnosis
Pradeep, M., Jyotsna, K. A., Vedantham, R., Bolisetty, V. V., Yadav, G. R., & Kanakaprabha, S. (2025) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict64420.2025.11004688 · Cited by 4
Ravikumar, S., Chandralekha, E., Kannan, E., & Vijay, K. (2023) · International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) · doi:10.1109/rmkmate59243.2023.10368818 · Cited by 4
Kakade, E. A. S. V. (2024) · Journal of Electrical Systems · doi:10.52783/jes.650 · Cited by 4
Dantalion: Digital Twinning the Computing Continuum
Laso, S., Martín, L., Herrera, J. L., Galán–Jiménez, J., Berrocal, J., & Murillo, J. M. (2023) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps58843.2023.10464899 · Cited by 4
IMHS: An IoT-Based Machine Learning Healthcare System
Goyal, S., Kaur, A., Sharma, V., & Batra, N. (2024) · International Conference on Disruptive Technologies (ICDT) · doi:10.1109/icdt61202.2024.10489127 · Cited by 4
A Developer-Focused Genetic Algorithm for IoT Application Placement in the Computing Continuum
Herrera, J. L., Moya, A., Berrocal, J., Murillo, J. M., & Navarro, E. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3556641 · Cited by 4
Development of an Anomaly Alert System Triggered by Unusual Behaviors at Home
Rodriguez-Goncalves, R., García-Crespo, Á., Matheus-Chacin, C. A., & Ruiz-Arroyo, A. (2021) · Sensors · doi:10.3390/s21165454 · Cited by 4
EVBLB: Efficient Voronoi Tessellation-Based Load Balancing in Edge Computing Networks
Sohrabi, V., Esmaeili, M. E., Dolati, M., Khonsari, A., & Dadlani, A. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685358 · Cited by 4
Ubiquitous Healthcare System Using Recent ICT
Mohapatra, S., Sahoo, A., Mohanty, S., & Patra, P. K. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-0901-6_8 · Cited by 4
Laso, S., Flores-Martin, D., Herrera, J. L., Galán–Jiménez, J., & Berrocal, J. (2022) · Electronics · doi:10.3390/electronics12010075 · Cited by 4
IoT-based ECG monitoring system for smart health-care data applications
Chidambaram, S. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-95876-9.00007-0 · Cited by 4
A Novel Edge-Assisted IoT-ML-Based Smart Healthcare Framework for COVID-19
Mir, M. H., Jamwal, S., Iqbal, U., Mehbodniya, A., Webber, J. L., & Khan, U. H. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.027173 · Cited by 4
Aouani, N., Hervé, A., & Nefzaoui, E. (2023) · IEEE International Workshop on Metrology for Living Environment (MetroLivEnv) · doi:10.1109/metrolivenv56897.2023.10164066 · Cited by 4
Ali, A., Alqurashi, F. A., Alsolami, F. J., & Qaiyum, S. (2023) · Mathematics · doi:10.3390/math11183894 · Cited by 4
HYBRID MODEL USED FOR REDUCING LATENCY IN SMART HEALTHCARE SYSTEMS
Şahin, C. B., Dinler, Ö. B., & Aznaoui, H. (2024) · Journal of advancement in computing. · doi:10.36755/jac.v2i1.57 · Cited by 4
Elkhidr, E. A., Elrashdi, A. S., Khiralla, S. E., Altawerghi, N. A., & Hasan, F. M. (2024) · International Conference on Emerging Smart Technologies and Applications (eSmarTA) · doi:10.1109/esmarta62850.2024.10638972 · Cited by 4
Owen, J. P., Blazes, M. S., Manivannan, N., Lee, G. C., Yu, S., Durbin, M. K., Nair, A., Singh, R. P., Talcott, K. E., Melo, A. G. R., et al. (2021) · Biomedical Optics Express · doi:10.1364/boe.433432 · Cited by 4
Moya, A., Herrera, J. L., López-Rodenas, J., Berrocal, J., Murillo, J. M., & Navarro, E. (2026) · Internet of Things · doi:10.1016/j.iot.2026.101894 · Cited by 3
Yu, X., Fan, L., Fan, S., & Hong, L. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1703605 · Cited by 3
Bringing Deep Learning to the Fields and Forests: Leaf Reconstruction and Shape Estimation
Silva, M. C., Bianchi, A. G. C., Ribeiro, S. P., & Oliveira, R. (2022) · SN Computer Science · doi:10.1007/s42979-022-01082-4 · Cited by 3
Ge Enrichment of Ge–Sb–Te Alloys as Keystone of Flexible Edge Electronics
Calvi, S., Bertelli, M., Simone, S. D., Maita, F., Prili, S., Fattorini, A. D., Matteis, F. D., Mussi, V., Riva, F. R., Longo, M., et al. (2024) · Advanced Electronic Materials · doi:10.1002/aelm.202400184 · Cited by 3
Sharma, M., Sharma, A., & Kumar, P. (2024) · International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC) · doi:10.1109/icec59683.2024.10837196 · Cited by 4
Efficient Limb Range of Motion Analysis from a Monocular Camera for Edge Devices
Yan, X., Zhang, L., Liu, B., & Qu, G. (2025) · Sensors · doi:10.3390/s25030627 · Cited by 3
SMEC: Sensor Mobile Edge Computing
Raychaudhuri, A., Mukherjee, A., & De, D. (2021) · Springer eBooks · doi:10.1007/978-3-030-69893-5_5 · Cited by 3
Automated Deep Learning for Medical Imaging
O’Byrne, C., Raja, L., Struyven, R., Korot, E., & Keane, P. A. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_269 · Cited by 3
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