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An adaptive QoS computation for medical data processing in intelligent healthcare applications

  • Intelligent Biomedical Data Analysis and Processing
  • Published:
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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.

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Acknowledgements

This work is partially supported by Natural Science Foundation of China 6171101169, National Key R&D Plan-Key Special Plan on Public Security Risk Mitigation/Response 2017YFC0804003, Technologies and Equipment Guangdong Education Bureau Fund 2017KTSCX166, the Science and Technology Innovation Committee Foundation of Shenzhen JCYJ20170817112037041, Science and Technology Innovation Committee Foundation of Shenzhen (Grant No. ZDSYS201703031748284). Also for this research, Andrei Gurtov was supported by the Center for Industrial Information Technology (CENIIT) Project 17.01. This work is supported in part by the HEC Pakistan under the START-UP RESEARCH GRANT PROGRAM (SRGP)#21-1465/SRGP/R&D/HEC/2016, and Sukkur IBA University, Sukkur, Sindh, Pakistan.

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Authors and Affiliations

  1. Electrical Engineering Department, Sukkur IBA University, Sukkur, 65200, Pakistan

    Ali Hassan Sodhro

  2. IDA-Computer and Information Science Department, Linkoping University, 58183, Linköping, Sweden

    Ali Hassan Sodhro

  3. Department of Biomedical Engineering, Kyunghee University South Korea, Suwon, 16705, Korea

    Abdul Sattar Malokani

  4. Department of Physics, Shah Abdul Latif University, Khairpur Mirs, Sindh, 66111, Pakistan

    Gul Hassan Sodhro

  5. Department of Computer Science, Bahria University, Islamabad, Pakistan

    Muhammad Muzammal

  6. Shenzhen Key Laboratory of Computational Intelligence, Department of the Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Luo Zongwei

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  1. Ali Hassan Sodhro
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  3. Gul Hassan Sodhro
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  4. Muhammad Muzammal
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  5. Luo Zongwei
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Correspondence to Luo Zongwei.

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Sodhro, A.H., Malokani, A.S., Sodhro, G.H. et al. An adaptive QoS computation for medical data processing in intelligent healthcare applications. Neural Comput & Applic 32, 723–734 (2020). https://doi.org/10.1007/s00521-018-3931-1

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