期刊
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
卷 45, 期 -, 页码 308-318出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ijinfomgt.2018.08.004
关键词
Mobile edge computing; Medical healthcare; QoS; Window-based rate control algorithm; BSA
资金
- HEC Pakistan under the START-UP RESEARCH GRANT PROGRAM (SRGP) [21-1465/SRGP/RD/HEC/2016]
- Sukkur IBA University, Sukkur, Sindh, Pakistan
- DISP LAB University Lumiere Lyon 2, under Erasmus Mundus's SMARTLINK Program2017
- Natural Science Foundation of China [6171101169]
- Guangdong Education Bureau Fund [2017KTSCX166]
- Science and Technology Innovation Committee Foundation of Shenzhen [JCYJ20170817112037041, ZDSYS201703031748284]
- SUSTech Startup Fund [Y01236215/Y01236115]
Emerging trends in mobile edge computing for developing the efficient healthcare application such as, remote monitoring of the patients with central electronics clouds (e-Clouds) and their increasing voluminous multimedia have caught the attention of everyone in industry and academia. So, clear visualization, big sensing level, and better quality of service (QoS) is the foremost priority. This paper proposes the window-based Rate Control Algorithm (w-RCA) to optimize the medical quality of service (m-QoS) in the mobile edge computing based healthcare by considering the network parameters for instance, peak-to-mean ratio (PMR), standard deviation (Std.dev), delay and jitter during 8 min medical video stream named Navigation to the Uterine Horn, transection of the horn and re-anastomosis' transmission over 5 G networks. The performance of the proposed w-RCA is evaluated and compared with the conventional battery smoothing algorithm (BSA) and Baseline by using MPEG-4 encoder for optimizing m-QoS at the source or the server side. The experimental results demonstrate that the w-RCA outperforms the BSA and Baseline by optimizing QoS in remote healthcare application i.e., Telesurgery. Besides, it is observed and analyzed that w-RCA produces better and effective results at small buffer and window sizes unlike BSA and Baseline by adopting large buffer size during QoS optimization.
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