The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities
Open accessAbstract
Smart city advancements are driving massive transformations of healthcare, the largest global industry. The drivers include increasing demands for ubiquitous, preventive, and personalized healthcare, to be provided to the public at reduced risks and costs. Mobile cloud computing could potentially meet the future healthcare demands by enabling anytime, anywhere capture and analyses of patients' data. However, network latency, bandwidth, and reliability are among the many challenges hindering the realization of next-generation healthcare. This paper proposes a ubiquitous healthcare framework, UbeHealth, that leverages edge computing, deep learning, big data, high-performance computing (HPC), and the Internet of Things (IoT) to address the aforementioned challenges. The framework enables an enhanced network quality of service using its three main components and four layers. Deep learning, big data, and HPC are used to predict network traffic, which in turn are used by the Cloudlet and network layers to optimize data rates, data caching, and routing decisions. Application protocols of the traffic flows are classified, enabling the network layer to meet applications' communication requirements better and to detect malicious traffic and anomalous data. Clustering is used to identify the different kinds of data originating from the same application protocols. A proof of concept UbeHealth system has been developed based on the framework. A detailed literature review is used to capture the design requirements for the proposed system. The system is described in detail including the algorithmic implementation of the three components and four layers. Three widely used data sets are used to evaluate the UbeHealth system.
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.
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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Fog computing and its role in the internet of things
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012) · Proceedings of the first edition of the MCC workshop on Mobile cloud computing · doi:10.1145/2342509.2342513 · Cited by 6,244
The case for vm-based cloudlets in mobile computing
Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2009) · IEEE Pervasive Computing · doi:10.1109/mprv.2009.82 · Cited by 3,660
Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076
A survey of mobile cloud computing: architecture, applications, and approaches
Dinh, H. T., Lee, C., Niyato, D., & Wang, P. (2013) · Wireless Communications and Mobile Computing · doi:10.1002/wcm.1203 · Cited by 2,391
Low power wide area networks: an overview
Raza, U., Kulkarni, P., & Sooriyabandara, M. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2652320 · Cited by 2,047
Rahmani, A. M., Gia, T. N., Negash, B., Anzanpour, A., Azimi, I., Jiang, M., & Liljeberg, P. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2017.02.014 · Cited by 1,198
An IoT-Aware Architecture for Smart Healthcare Systems
Catarinucci, L., de Donno, D., Mainetti, L., Palano, L., Patrono, L., Stefanizzi, M. L., & Tarricone, L. (2015) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2417684 · Cited by 1,167
Internet traffic classification demystified: myths, caveats, and the best practices
Kim, H., Claffy, K., Fomenkov, M., Barman, D., Faloutsos, M., & Lee, K. (2008) · Proceedings of the 2008 ACM CoNEXT Conference on - CONEXT '08 · doi:10.1145/1544012.1544023 · Cited by 485
Cited by
On big data, artificial intelligence and smart cities
Allam, Z., & Dhunny, Z. A. (2019) · Cities · doi:10.1016/j.cities.2019.01.032 · Cited by 1,178
FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring
Wu, Q., Chen, X., Zhou, Z., & Zhang, J. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3045266 · Cited by 392
A survey of data fusion in smart city applications
Lau, B. P. L., Marakkalage, S. H., Zhou, Y., Hassan, N. U., Yuen, C., Zhang, M., & Tan, U.-X. (2019) · Information Fusion · doi:10.1016/j.inffus.2019.05.004 · Cited by 320
SPEED: A Deep Learning Assisted Privacy-Preserved Framework for Intelligent Transportation Systems
Usman, M., Jan, M. A., & Jolfaei, A. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3031721 · Cited by 32
Toma, C., Popa, M., Iancu, B., Doinea, M., Pascu, A., & Ioan-Dutescu, F. (2022) · Electronics · doi:10.3390/electronics11213507 · Cited by 22
Metadata
- Authors
- Muhammed, T., Mehmood, R., Albeshri, A., & Katib, I.
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 32258-32285
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2808064527
- Cited by
- 263
- References
- 13
- FWCI
- 20.07
- Type
- journal article
- Language
- en
- Open access
- gold
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities. https://app.cochat.ai/papers/10.1109/access.2018.2846609
- The paper
- Muhammed, T., Mehmood, R., Albeshri, A., & Katib, I. (2018). UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities. IEEE Access. https://doi.org/10.1109/access.2018.2846609
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.