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Energy-Efficient Context Aware Power Management with Asynchronous Protocol for Body Sensor Network

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Abstract

MEMS sensor technology and advances in electronics, low-power processors and communication have enabled ubiquitous monitoring, providing significant opportunities for a wide range of applications including wearable devices for fitness and health tracking. However, due to the limited form factor required, there remains a challenging issue that limits even more the success of wearable devices: the limited lifetime due to the small energy storages that supply the devices. This limitation affects usability and forces the data processing to keep low-complexity to match the power constraints. As wireless communication is typically the most power hungry activity in wearable sensors devices, many techniques focus on reducing the communication power consumption. For this reason, advanced power management can be exploited to increase the lifetime of the devices. In this work, we present a wireless body area network with an adaptive power management strategy combining an ultra-low power wake up radio with context awareness. The context aware power manager is based on activity recognition, which is evaluated to decide which other nodes must be activated. The nano-power wake up receiver is used to reduce the idle listening power of the main radio and enable an asynchronous ultra-low power protocol. In order to evaluate the benefit, we present a real world application to assist elderly people in gait rehabilitation through a closed loop feedback. Experimental results demonstrate the benefit of the proposed power management in terms of energy efficiency. We evaluate the overall power consumption of the system and the lifetime extension, which can increase up to a factor of 4 depending on the amount of time the system can be placed in sleep mode.

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Acknowledgments

This work was supported by “Transient Computing Systems”, SNF project (200021_157048), by SCOPES SNF project (IZ74Z0_160481), and ETHZ Grant funding.

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Authors and Affiliations

  1. D-ITET, ETH Zurich, Gloriastrasse 35, 8092, Zurich, Switzerland

    Michele Magno, Andres Gomez & Luca Benini

  2. DEI, University of Bologna, Viale del Resorgimento 2, 40132, Bologna, Italy

    Michele Magno, Tommaso Polonelli, Filippo Casamassima & Luca Benini

  3. ICT Center, Fondazione Bruno Kessler, Via Sommarive 18 , 38123 Povo (TN), Trento, Italy

    Elisabetta Farella

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  1. Michele Magno
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  2. Tommaso Polonelli
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  3. Filippo Casamassima
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  4. Andres Gomez
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  5. Elisabetta Farella
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  6. Luca Benini
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Correspondence to Michele Magno.

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Magno, M., Polonelli, T., Casamassima, F. et al. Energy-Efficient Context Aware Power Management with Asynchronous Protocol for Body Sensor Network. Mobile Netw Appl 22, 814–824 (2017). https://doi.org/10.1007/s11036-016-0755-z

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  • DOI: https://doi.org/10.1007/s11036-016-0755-z

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