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Estimation of event loss duration for energy harvested wireless body sensor node

Author

Listed:
  • Ritwik Haldar

    (NIT Silchar)

  • Ashraf Hossain

    (NIT Silchar)

  • Kirtan Gopal Panda

    (NIT Silchar)

Abstract

Energy harvesting (EH) body sensor nodes (BSNs) operate independently in the system and are the emerging solution to multiple replacements of battery operated BSNs. After deployment, the stored energy of the BSN falls to a minimum level due to uncertain energy harvesting process. Therefore, the node is unable to transmit the occurred events to the base station and stores them in storage buffer (SB) in a queue. Due to the queue overflow in SB, the BSN is unable to store the occurred events, therefore it is lost. In health monitoring system, loss of emergency or critical information has a bad impact on quality of service in the network. It is essential to have an estimate of the duration to occur an event loss in order to take precautions and prior control on nodes in critical situations for medical applications. We calculate the duration after which event loss occurs in SB by absorbing discrete-time Markov chain (DTMC) model to evaluate performance of the EH BSN with temporal death. We also derive a closed form expression of event loss duration which reduces the computational complexity of the conventional DTMC model. The analytical results are validated by Monte Carlo simulation using MATLAB.

Suggested Citation

  • Ritwik Haldar & Ashraf Hossain & Kirtan Gopal Panda, 2019. "Estimation of event loss duration for energy harvested wireless body sensor node," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 70(2), pages 231-244, February.
    • Handle: RePEc:spr:telsys:v:70:y:2019:i:2:d:10.1007_s11235-018-0491-8
    DOI: 10.1007/s11235-018-0491-8
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    References listed on IDEAS

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    1. Salar Chamanian & Sajjad Baghaee & Hasan Ulusan & Özge Zorlu & Haluk Külah & Elif Uysal-Biyikoglu, 2014. "Powering-up Wireless Sensor Nodes Utilizing Rechargeable Batteries and an Electromagnetic Vibration Energy Harvesting System," Energies, MDPI, vol. 7(10), pages 1-17, October.
    2. Seyedeh Narjes Fallah & Ravinesh Chand Deo & Mohammad Shojafar & Mauro Conti & Shahaboddin Shamshirband, 2018. "Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions," Energies, MDPI, vol. 11(3), pages 1-31, March.
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