ASC Performance Prediction for Medical IoT Communication Networks. (28th May 2021)
- Record Type:
- Journal Article
- Title:
- ASC Performance Prediction for Medical IoT Communication Networks. (28th May 2021)
- Main Title:
- ASC Performance Prediction for Medical IoT Communication Networks
- Authors:
- Yin, Fagen
Xiao, Pingping
Li, Zefeng - Other Names:
- Li Xingwang Academic Editor.
- Abstract:
- Abstract : Wearable devices are gradually entering the medical health field. Medical Internet of Things (IoT) has been widely used in all walks of medical health. With the complexity of medical health application scenarios, the medical IoT communication networks face complex environments. The secure communication issue is very important for medical IoT communication networks. This paper investigates the secrecy performance of medical IoT communication networks. To improve the secrecy performance, we adopt a cooperative communication strategy. We also use the average secrecy capacity (ASC) as a metric, and the expressions are first derived. Then, a secrecy performance intelligent prediction algorithm is proposed. The extensive simulations are used to verify the proposed method. Compared with other methods, the proposed algorithm realizes a better prediction precision.
- Is Part Of:
- Journal of healthcare engineering. Volume 2021(2021)
- Journal:
- Journal of healthcare engineering
- Issue:
- Volume 2021(2021)
- Issue Display:
- Volume 2021, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 2021
- Issue:
- 2021
- Issue Sort Value:
- 2021-2021-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-05-28
- Subjects:
- Hospital buildings -- Environmental engineering -- Periodicals
Medical technology -- Periodicals
Medical informatics -- Periodicals
610.28 - Journal URLs:
- http://www.hindawi.com/journals/jhe/ ↗
http://multi-science.metapress.com/content/r03085752427/?p=bacc87ee7c194c1aa6a045ab293b1f0f&pi=2 ↗ - DOI:
- 10.1155/2021/6265520 ↗
- Languages:
- English
- ISSNs:
- 2040-2295
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 17010.xml