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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jingle, I Diana Jeba | - |
dc.contributor.author | Paul, P Mano | - |
dc.date.accessioned | 2023-12-19T05:08:54Z | - |
dc.date.available | 2023-12-19T05:08:54Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Vol. 1333 AISC; pp. 155-160 | en_US |
dc.identifier.isbn | 9789813369658 | - |
dc.identifier.isbn | 9789813369665 | - |
dc.identifier.issn | 2194-5357 | - |
dc.identifier.issn | 2194-5365 | - |
dc.identifier.uri | https://doi.org/10.1007/978-981-33-6966-5_16 | - |
dc.identifier.uri | http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2570 | - |
dc.description.abstract | Fog computing is an emerging technology that offers high-quality cloud services by providing high bandwidth, low latency, and efficient computational power and storage capacity. Although cloud computing is an efficient solution so far to store and retrieve the huge data of IoT devices, it is expected to limit its performance due to low latency and storage capacity. Fog computing addresses these limitations by extending its services to the cloud at the edge of the network. In this paper, we use a fog computing network approach for efficiently retrieving the real-time patient data. The performance of our proposed approach has been compared with the cloud computing approach in terms of retrieval time of real-time data. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Advances in Intelligent Systems and Computing: ISSIP 2020 | en_US |
dc.subject | Adafruit | en_US |
dc.subject | Fog computing | en_US |
dc.subject | Internet of things | en_US |
dc.subject | Raspberry Pi | en_US |
dc.title | A Fog-Based Retrieval of Real-Time Data For Health Applications | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
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