Publication:
Incorporating the Markov Chain Model in WBSN for Improving Patients� Remote Monitoring Systems

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dc.citedby3
dc.contributor.authorAli R.R.en_US
dc.contributor.authorMostafa S.A.en_US
dc.contributor.authorMahdin H.en_US
dc.contributor.authorMustapha A.en_US
dc.contributor.authorGunasekaran S.S.en_US
dc.contributor.authorid57200536163en_US
dc.contributor.authorid37036085800en_US
dc.contributor.authorid35759460000en_US
dc.contributor.authorid57200530694en_US
dc.contributor.authorid55652730500en_US
dc.date.accessioned2023-05-29T08:14:12Z
dc.date.available2023-05-29T08:14:12Z
dc.date.issued2020
dc.descriptionBody 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 monitoringen_US
dc.description.abstractWireless 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-030-36056-6_4
dc.identifier.epage46
dc.identifier.scopus2-s2.0-85078402042
dc.identifier.spage35
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85078402042&doi=10.1007%2f978-3-030-36056-6_4&partnerID=40&md5=7e975e3db7ec1490e9536808e293a383
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25782
dc.identifier.volume978 AISC
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleAdvances in Intelligent Systems and Computing
dc.titleIncorporating the Markov Chain Model in WBSN for Improving Patients� Remote Monitoring Systemsen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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