Incorporating the Markov Chain Model in WBSN for Improving Patients� Remote Monitoring Systems
Body sensor networks; Data mining; Data transfer; Data transfer rates; Electric power utilization; Energy efficiency; Markov processes; Medical applications; Medium access control; Remote control; Soft computing; Contention access periods; Efficiency and reliability; Human arm; Markov chain models;...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Springer
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-25782 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-257822023-05-29T16:14:12Z Incorporating the Markov Chain Model in WBSN for Improving Patients� Remote Monitoring Systems Ali R.R. Mostafa S.A. Mahdin H. Mustapha A. Gunasekaran S.S. 57200536163 37036085800 35759460000 57200530694 55652730500 Body sensor networks; Data mining; Data transfer; Data transfer rates; Electric power utilization; Energy efficiency; Markov processes; Medical applications; Medium access control; Remote control; Soft computing; Contention access periods; Efficiency and reliability; Human arm; Markov chain models; Media access control; Real-time environment; Remote monitoring system; Wireless body sensor networks; Remote patient monitoring Wireless body sensor network (WBSN) allows remote monitoring for different types of applications in security, healthcare and medical domains. Medical applications involve monitoring a large number of patients in real-time environments. The WBSNs in such environments have to be efficient and reliable in terms of data transfer rate, accuracy, latency, and power consumption. This work focuses on studying the slotted access protocol variables in the Contention Access Period (CAP) with the acknowledged uplink traffic (nodes-to-coordinator) under the WBSN channel. This paper proposes a Markov Chain model in WBSN (MC-WBSN) for improving the efficiency and reliability of patients� remote monitoring systems. The application of the model includes propagating human arm sensory data and analyzing the latency, power consumption, throughput, and higher path loss channel of the WBSN. The results show that the hidden nodes have a great impact on WBSNs performance and throughput. This issue is highly associated with the capacity of the transmitted power. � Springer Nature Switzerland AG 2020. Final 2023-05-29T08:14:12Z 2023-05-29T08:14:12Z 2020 Conference Paper 10.1007/978-3-030-36056-6_4 2-s2.0-85078402042 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078402042&doi=10.1007%2f978-3-030-36056-6_4&partnerID=40&md5=7e975e3db7ec1490e9536808e293a383 https://irepository.uniten.edu.my/handle/123456789/25782 978 AISC 35 46 Springer Scopus |
| institution |
Universiti Tenaga Nasional |
| building |
UNITEN Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Tenaga Nasional |
| content_source |
UNITEN Institutional Repository |
| url_provider |
http://dspace.uniten.edu.my/ |
| description |
Body sensor networks; Data mining; Data transfer; Data transfer rates; Electric power utilization; Energy efficiency; Markov processes; Medical applications; Medium access control; Remote control; Soft computing; Contention access periods; Efficiency and reliability; Human arm; Markov chain models; Media access control; Real-time environment; Remote monitoring system; Wireless body sensor networks; Remote patient monitoring |
| author2 |
57200536163 |
| author_facet |
57200536163 Ali R.R. Mostafa S.A. Mahdin H. Mustapha A. Gunasekaran S.S. |
| format |
Conference Paper |
| author |
Ali R.R. Mostafa S.A. Mahdin H. Mustapha A. Gunasekaran S.S. |
| spellingShingle |
Ali R.R. Mostafa S.A. Mahdin H. Mustapha A. Gunasekaran S.S. Incorporating the Markov Chain Model in WBSN for Improving Patients� Remote Monitoring Systems |
| author_sort |
Ali R.R. |
| title |
Incorporating the Markov Chain Model in WBSN for Improving Patients� Remote Monitoring Systems |
| title_short |
Incorporating the Markov Chain Model in WBSN for Improving Patients� Remote Monitoring Systems |
| title_full |
Incorporating the Markov Chain Model in WBSN for Improving Patients� Remote Monitoring Systems |
| title_fullStr |
Incorporating the Markov Chain Model in WBSN for Improving Patients� Remote Monitoring Systems |
| title_full_unstemmed |
Incorporating the Markov Chain Model in WBSN for Improving Patients� Remote Monitoring Systems |
| title_sort |
incorporating the markov chain model in wbsn for improving patients� remote monitoring systems |
| publisher |
Springer |
| publishDate |
2023 |
| _version_ |
1806428173097762816 |
| score |
13.214268 |