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Status已发表Published
TitleAn adaptive QoS computation for medical data processing in intelligent healthcare applications
Creator
Date Issued2020
Source PublicationNEURAL COMPUTING & APPLICATIONS
ISSN0941-0643
Volume32Issue:3Pages:723-734
Abstract

Efficient computation of quality of service (QoS) during medical data processing through intelligent measurement methods is one of the mandatory requirements of the medial healthcare world. However, emergency medical services often involve transmission of critical data, thus having stringent requirements for network quality of service (QoS). This paper contributes in three distinct ways. First, it proposes the novel adaptive QoS computation algorithm (AQCA) for fair and efficient monitoring of the performance indicators, i.e., transmission power, duty cycle and route selection during medical data processing in healthcare applications. Second, framework of QoS computation in medical applications is proposed at physical, medium access control (MAC) and network layers. Third, QoS computation mechanism with proposed AQCA and quality of experience (QoE) is developed. Besides, proper examination of QoS computation for medical healthcare application is evaluated with 4-10 inches large-screen user terminal (UT) devices (for example, LCD panel size, resolution, etc.). These devices are based on high visualization, battery lifetime and power optimization for ECG service in emergency condition. These UT devices are used to achieve highest level of satisfaction in terms, i.e., less power drain, extended battery lifetime and optimal route selection. QoS parameters with estimation of QoE perception identify the degree of influence of each QoS parameters on the medical data processing is analyzed. The experimental results indicate that QoS is computed at physical, MAC and network layers with transmission power (- 15 dBm), delay (100 ms), jitter (40 ms), throughput (200 Bytes), duty cycle (10%) and route selection (optimal). Thus it can be said that proposed AQCA is the potential candidate for QoS computation than Baseline for medical healthcare applications.

KeywordAdaptive QoS computation Medical data processing QoS-QoE correlation Intelligent healthcare applications
DOI10.1007/s00521-018-3931-1
Indexed BySCIE
Language英语English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000512022900009
Scopus ID2-s2.0-85059454670
Citation statistics
Cited Times:62[WOS]   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Identifierhttp://repository.uic.edu.cn/handle/39GCC9TT/7406
CollectionResearch outside affiliated institution
Corresponding AuthorLuo, Zongwei
Affiliation
1.Electrical Engineering Department, Sukkur IBA University, Sukkur, 65200, Pakistan
2.IDA-Computer and Information Science Department, Linkoping University, Linköping, 58183, Sweden
3.Department of Biomedical Engineering, Kyunghee University South Korea, Suwon, 16705, South Korea
4.Department of Physics, Shah Abdul Latif University, Khairpur Mirs, 66111, Sindh, Pakistan
5.Department of Computer Science, Bahria University, Islamabad, Pakistan
6.Shenzhen Key Laboratory of Computational Intelligence, Department of the Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
Recommended Citation
GB/T 7714
Sodhro, Ali Hassan,Malokani, Abdul Sattar,Sodhro, Gul Hassanet al. An adaptive QoS computation for medical data processing in intelligent healthcare applications[J]. NEURAL COMPUTING & APPLICATIONS, 2020, 32(3): 723-734.
APA Sodhro, Ali Hassan, Malokani, Abdul Sattar, Sodhro, Gul Hassan, Muzammal, Muhammad, & Luo, Zongwei. (2020). An adaptive QoS computation for medical data processing in intelligent healthcare applications. NEURAL COMPUTING & APPLICATIONS, 32(3), 723-734.
MLA Sodhro, Ali Hassan,et al."An adaptive QoS computation for medical data processing in intelligent healthcare applications". NEURAL COMPUTING & APPLICATIONS 32.3(2020): 723-734.
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