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A Survey on Internet of Things and Cloud Computing for Healthcare
Open accessAbstract
The fast development of the Internet of Things (IoT) technology in recent years has supported connections of numerous smart things along with sensors and established seamless data exchange between them, so it leads to a stringy requirement for data analysis and data storage platform such as cloud computing and fog computing. Healthcare is one of the application domains in IoT that draws enormous interest from industry, the research community, and the public sector. The development of IoT and cloud computing is improving patient safety, staff satisfaction, and operational efficiency in the medical industry. This survey is conducted to analyze the latest IoT components, applications, and market trends of IoT in healthcare, as well as study current development in IoT and cloud computing-based healthcare applications since 2015. We also consider how promising technologies such as cloud computing, ambient assisted living, big data, and wearables are being applied in the healthcare industry and discover various IoT, e-health regulations and policies worldwide to determine how they assist the sustainable development of IoT and cloud computing in the healthcare industry. Moreover, an in-depth review of IoT privacy and security issues, including potential threats, attack types, and security setups from a healthcare viewpoint is conducted. Finally, this paper analyzes previous well-known security models to deal with security risks and provides trends, highlighted opportunities, and challenges for the IoT-based healthcare future development.
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An IoT based robust healthcare model for continuous health monitoring
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Dynamically Adapting the Environment for Elderly People Through Smartwatch-Based Mood Detection
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Development of Wearable Pulse Oximeter Based on Internet of Things and Signal Processing Techniques
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Blood Pressure Measurement Using Finger ECG and Photoplethysmogram for IoT
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Biosignal Monitoring Platform Using Wearable IoT
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D-ECG: A Dynamic Framework for Cardiac Arrhythmia Detection from IoT-Based ECGs
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Internet of Things Based Cloud Smart Monitoring for Asthma Patient
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Advanced Tele-surgery with IoT Approach
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Metadata
- Authors
- Dang, L. M., Piran, M. J., Han, D., Min, K., & Moon, H.
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2019-07-09
- Volume
- 8
- Issue
- 7
- Pages
- 768
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W2956571997
- Cited by
- 640
- References
- 116
- FWCI
- 93.41
- 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 ›
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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.
