The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0
Open accessAbstract
Edge computing paradigm has attracted many interests in the last few years as a valid alternative to the standard cloud-based approaches to reduce the interaction timing and the huge amount of data coming from Internet of Things (IoT) devices toward the Internet. In the next future, Edge-based approaches will be essential to support time-dependent applications in the Industry 4.0 context; thus, the paper proposes BodyEdge, a novel architecture well suited for human-centric applications, in the context of the emerging healthcare industry. It consists of a tiny mobile client module and a performing edge gateway supporting multiradio and multitechnology communication to collect and locally process data coming from different scenarios; moreover, it also exploits the facilities made available from both private and public cloud platforms to guarantee a high flexibility, robustness, and adaptive service level. The advantages of the designed software platform have been evaluated in terms of reduced transmitted data and processing time through a real implementation on different hardware platforms. The conducted study also highlighted the network conditions (data load and processing delay) in which BodyEdge is a valid and inexpensive solution for healthcare application scenarios.
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Cites
Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733
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
Cited by
Khan, W. Z., Ahmed, E., Hakak, S., Yaqoob, I., & Ahmed, A. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.02.050 · Cited by 1,157
Medical 4.0 technologies for healthcare: Features, capabilities, and applications
Haleem, A., Javaid, M., Pratap Singh, R., & Suman, R. (2022) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2022.04.001 · Cited by 468
Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3329061 · Cited by 315
IoMT amid COVID-19 pandemic: Application, architecture, technology, and security
Mohd Aman, A. H., Hassan, W. H., Sameen, S., Attarbashi, Z. S., Alizadeh, M., & Latiff, L. A. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102886 · Cited by 237
A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic
Sufian, A., Ghosh, A., Sadiq, A. S., & Smarandache, F. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101830 · Cited by 156
Metadata
- Authors
- Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., & Liotta, A.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2019-01
- Volume
- 15
- Issue
- 1
- Pages
- 481-489
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2805182940
- Cited by
- 392
- References
- 2
- FWCI
- 27.07
- Type
- journal article
- Language
- en
- Open access
- green
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- Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., & Liotta, A. (2019). An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2018.2843169
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