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Dynamic voltage-frequency scaling in body area sensor networks using COTS components

Authors:

Harry C. Powell,

Adam T. Barth,

John LachAuthors Info & Claims

BodyNets '09: Proceedings of the Fourth International Conference on Body Area Networks

Article No.: 15, Pages 1 - 8

https://doi.org/10.4108/ICST.BODYNETS2009.6015

Published: 01 April 2009 Publication History

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Abstract

Body area sensor networks (BASNs) have implicit stringent power requirements to meet battery life and form factor expectations, especially in long-term medical monitoring applications. The largest power consumer in BASNs is typically the wireless transceiver, so recent research has focused on increasing on-node signal processing to reduce the number of bits for wireless transmission. This shift increases the importance of power efficient signal processing. Given that the processing workloads and throughput requirements can change dynamically in a BASN, dynamic voltage-frequency scaling (DVFS) becomes an attractive option for providing the necessary processing rate with the minimum power. However, commercial off the shelf (COTS) components typically used in BASN nodes are not designed for DVFS. This paper characterizes the DVFS capabilities of a COTS processor commonly used on BASN nodes -- the TI MSP430 -- and explores the usefulness of these capabilities within the context of BASN applications.

References

[1]

H. C. Powell Jr., M. A. Hanson, and J. Lach. 2007. A Wearable Inertial Sensing Technology for Clinical Assessment of Tremor. IEEE Biomedical Circuits and Systems Conference, pp. 9--12.

Google Scholar

[2]

G. Yang. 2006. "Body Sensor Networks." Springer-Verlag London Limited, pp. 10--21, pp. 138--141, pp. 152--173, pp. 183--185.

Google Scholar

[3]

Y. I. Nechayev, P. S. Hall, C. C. Constantinou, Y. Hao, A. Alomainy, R. Dubrovka, and C. G. Parini. 2005. On-Body Path Gain Variations with Changing Body Posture and Antenna Position. Antennas and Propagation Society International Symposium, vol. 1B, pp. 731--734.

Google Scholar

[4]

D. Neirynck, C. Williams, A. R. Nix, and M. A. Beach. 2005. Bristol Repository of Scholarly Eprints (ROSE): Channel Characterisation for Personal Area Networks. Virtual Centre of Excellence in Mobile&Personal Communications. www.mobilevce.com.

Google Scholar

[5]

S. Lin, J. Zhang, G. Zhou, L. Gu, J. A. Stankovic, and T. He. 2006. ATPC: Adaptive Transmission Power Control for Wireless Sensor Networks. ACM International Conference on Embedded Networked Sensor Systems, pp. 223--236.

Digital Library

Google Scholar

[6]

T. Pering, T. Burd, and R. Brodersen. 1998. Dynamic Voltage Scaling and the Design of a Low-Power Microprocessor System, Power Driven Microarchitecture Workshop, International Symposium on Computer Architecture.

Google Scholar

[7]

B. H. Calhoun and A. Chandrakasan. 2004. Characterizing and Modeling Minimum Energy Operation for Subthreshold Circuits. ACM International Symposium on Low Power Electronics and Design, pp. 90--95.

Digital Library

Google Scholar

[8]

J. Polastre, R. Szewczyk, and D. Culler. 2005. Telos: enabling ultra-low power wireless research. Proceedings of the 4th international symposium on Information processing in sensor networks, Los Angeles, California, p. 48.

Digital Library

Google Scholar

[9]

Lo, B., Thiemjarus, S., King R., and Yang, G. 2005. Body Sensor Network--A Wireless Sensor Platform for Pervasive Healthcare Monitoring. Proceedings of the 3 ^{rd^{International Conference on Pervasive Computing (PERVASIVE'05), May, pp.77--80.}}

Google Scholar

[10]

Texas Instruments MSP430F2013 datasheet, http://www.ti.com

Google Scholar

[11]

Linear Technology LTC4150 datasheet, http://linear.com

Google Scholar

[12]

Analog Devices AD5161 datasheet, http://www.analog.com

Google Scholar

[13]

B. Greenstein et al. Nov. 2006. Capturing high-frequency phenomena using a bandwidth-limited sensor network. In Proc. 4th ACM Conference on Embedded Networked Sensor Systems (SenSys '06).

Digital Library

Google Scholar

[14]

I. Lazaridis and S. Mehrotra. 2003. Capturing sensor-generated time series with quality guarantees. Proceedings of ICDE.

Google Scholar

Cited By

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Ahmed SAin QSiddiqui JMottola LAlizai MValois FJulien CMurphy AGnawali O(2020)Intermittent Computing with Dynamic Voltage and Frequency ScalingProceedings of the 2020 International Conference on Embedded Wireless Systems and Networks10.5555/3400306.3400319(97-107)Online publication date: 17-Feb-2020
https://dl.acm.org/doi/10.5555/3400306.3400319
Magno MMarinkovic SSrbinovski BPopovici E(2014)Wake-up radio receiver based power minimization techniques for wireless sensor networksMicroelectronics Journal10.1016/j.mejo.2014.08.01045:12(1627-1633)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1016/j.mejo.2014.08.010
Hörmann LGlatz PHein KSteger CWeiss RGuérin Lassous IIgartua MCuomo F(2012)A hardware/software simulation environment for energy harvesting wireless sensor networksProceedings of the 9th ACM symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks10.1145/2387027.2387039(61-68)Online publication date: 21-Oct-2012
https://dl.acm.org/doi/10.1145/2387027.2387039

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Dynamic voltage-frequency scaling in body area sensor networks using COTS components

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Published In

BodyNets '09: Proceedings of the Fourth International Conference on Body Area Networks

April 2009

161 pages

ISBN:9789639799417

General Chair:
William Kaiser
UCLA

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ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)

Brussels, Belgium

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Published: 01 April 2009

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Ahmed SAin QSiddiqui JMottola LAlizai MValois FJulien CMurphy AGnawali O(2020)Intermittent Computing with Dynamic Voltage and Frequency ScalingProceedings of the 2020 International Conference on Embedded Wireless Systems and Networks10.5555/3400306.3400319(97-107)Online publication date: 17-Feb-2020
https://dl.acm.org/doi/10.5555/3400306.3400319
Magno MMarinkovic SSrbinovski BPopovici E(2014)Wake-up radio receiver based power minimization techniques for wireless sensor networksMicroelectronics Journal10.1016/j.mejo.2014.08.01045:12(1627-1633)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1016/j.mejo.2014.08.010
Hörmann LGlatz PHein KSteger CWeiss RGuérin Lassous IIgartua MCuomo F(2012)A hardware/software simulation environment for energy harvesting wireless sensor networksProceedings of the 9th ACM symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks10.1145/2387027.2387039(61-68)Online publication date: 21-Oct-2012
https://dl.acm.org/doi/10.1145/2387027.2387039

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Cited By

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Recommendations

Dynamic Voltage Scaling Techniques for Distributed Microsensor Networks

The limit of dynamic voltage scaling and insomniac dynamic voltage scaling

TEMPO 3.1: A Body Area Sensor Network Platform for Continuous Movement Assessment

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