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Energy-Efficient Adaptive Sensing Technique for Smart Healthcare in Connected Healthcare Systems

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Innovations in Computational Intelligence and Computer Vision

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1424))

Abstract

Nowadays, keeping good and strong health is one of the main concerns of the government’s health monitoring systems. These systems are based on the data gathered by biosensors deployed on the body of the patients. One of the main challenges for health-based sensing applications is the big and heterogenic gathered data by these biomedical sensors. Since the biosensors have limited resources in terms of memory, energy and computation and they transmit a large amount of data periodically, therefore, it is important to reduce the sent data to save energy while preserving the accuracy of data at the coordinator. In this paper, an energy-efficient adaptive sensing technique (EASeT) for Smart Healthcare in connected healthcare systems is proposed. EASeT is executed at each biosensor. It works into rounds. The round contains two periods. It consists of two steps: detection of emergency and biosensor sensing adaptation. First, EASeT employed National Early Warning Score (NEWS) to eliminate redundant medical-sensed data before transmitting them to the coordinator. Second, the adaptive sensing rate algorithm is applied after each two periods to adjust the sensing rate according to the situation of the patient for two consecutive periods. The simulation results are achieved based on real sensed data of patients show that the proposed EASeT technique can reduce the transmitted data and decrease the consumed energy while maintaining the suitable accuracy of data at the coordinator in comparison with an existing approach.

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Authors and Affiliations

  1. Department of Computer Science, University of Babylon, Babylon, Iraq

    Duaa Abd Alhussein & Ali Kadhum Idrees

  2. Computer Science Department, American University of Culture and Education (AUCE), Nabatiyeh/Tyre, Lebanon

    Hassan Harb

Authors
  1. Duaa Abd Alhussein
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  2. Ali Kadhum Idrees
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  3. Hassan Harb
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Corresponding author

Correspondence to Ali Kadhum Idrees .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, India

    Satyabrata Roy

  2. Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India

    Deepak Sinwar

  3. Department of Computer Science and Information Technology, Universiti Putra Malaysia, Selangor, Malaysia

    Thinagaran Perumal

  4. Department of Computer Engineering, Koszalin University of Technology, Koszalin, Poland

    Adam Slowik

  5. Departamento de Engenharia Mecânica Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

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Alhussein, D.A., Kadhum Idrees, A., Harb, H. (2022). Energy-Efficient Adaptive Sensing Technique for Smart Healthcare in Connected Healthcare Systems. In: Roy, S., Sinwar, D., Perumal, T., Slowik, A., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision . Advances in Intelligent Systems and Computing, vol 1424. Springer, Singapore. https://doi.org/10.1007/978-981-19-0475-2_22

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