Abstract
Energy is the crucial factor for the lifetime of wireless sensor networks. Nonlinear battery effects and nonuniform workload distribution can lead to early node failures. This makes it necessary to manage energy consumption. But to manage energy it is essential to know how much energy is spent by the system. Additionally, for a more fine-grained management it is necessary, to know where the energy is spent. This can be a complicated task, since nodes are not identical due to device variations and the consumption can change over time.
In this paper we present an online energy accounting approach which focuses on simplicity instead on fine granularity and timing accuracy. We argue that the efficacy of an energy accounting model depends more on the input consumption data than on exact timing, especially when the real consumption varies between nodes and in time. Results show that this approach is capable of correctly accounting the energy that nodes spend in scenarios with deviating environment conditions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Landsiedel, O., Wehrle, K., Götz, S.: Accurate prediction of power consumption in sensor networks. In: EmNets 2005 Proceedings of the 2nd IEEE Workshop on Embedded Networked Sensors (2005)
Levis, P., Madden, S., Polastre, J., Szewczyk, R., Whitehouse, K., Woo, A., Gay, D., Hill, J., Welsh, M., Brewer, E., Culler, D.: TinyOS: An operating system for sensor networks. In: Ambient Intelligence (2004)
Dunkels, A., Gronvall, B., Voigt, T.: Contiki - a lightweight and fexible operating system for tiny networked sensors. In: Proceedings of the First IEEE Workshop on Embedded Networked Sensors (2004)
Walther, K., Nolte, J.: A Flexible Scheduling Framework for Deeply Embedded Systems. In: Proc. of 4th IEEE International Symposium on Embedded Computing (2007)
Linden, D.: Handbook of batteries, 2nd edn. McGraw-Hill Companies (1995)
Sieber, A., Nolte, J.: Device Management for Limiting the Load Applied to Batteries, 11. GI/ITG KuVS Fachgesprch Sensornetze (2012)
Park, C., Lahiri, K., Raghunathan, A.: Batterydischarge characteristics of wireless sensor nodes: An experimental analysis. In: Proceedings of the IEEE Conf. on Sensor and Ad-hoc Communications and Networks (SECON), Santa Clara, pp. 430–440 (2005)
Park, S., Saviddes, A., Srivastava, M.B.: Battery Capacity Measurement and Analysis Using Lithium Coin Cell Battery. In: Proc. Int. Symp. Low Power Electronics & Design (2001)
Woehrle, M., Beutel, J., Lim, R., Yuecel, M., Thiele, L.: Power monitoring and testing in wireless sensor network development. In: Workshop on Energy in Wireless Sensor Networks (2008)
Texas Instruments, bq26231 Low Cost Battery Coulomb Counter For Embedded Portable Applications, Webpage http://www.ti.com
Jiang, X., Dutta, P., Culler, D., Stoica, I.: Micro power meter for energy monitoring of wireless sensor networks at scale. In: Proceedings of the 6th International Conference on Information Processing in Sensor Networks (2007)
Dutta, P., Feldmeier, M., Paradiso, J., Culler, D.: Energy Metering for Free: Augmenting Switching Regulators for Real-Time Monitoring. In: Proceedings of the 7th International Conference on Information Processing in Sensor Networks (2008)
Fonseca, R., Dutta, P., Levis, P., Stoica, I.: Quanto: tracking energy in networked embedded systems. In: Proceedings of the 8th USENIX Conference on Operating Systems Design and Implementation (2008)
Kellner, S., Bellosa, F.: Energy accounting support in TinyOS. 2. GI/ITG KuVS Fachgesprch Systemsoftware und Energiebewusste Systeme (2007)
Kellner, S.: Flexible Online Energy Accounting in TinyOS. In: Marron, P.J., Voigt, T., Corke, P., Mottola, L. (eds.) REALWSN 2010. LNCS, vol. 6511, pp. 62–73. Springer, Heidelberg (2010)
Dunkels, A., Osterlind, F., Tsiftes, N., He, Z.: Software-based on-line energy estimation for sensor nodes. In: Proceedings of the 4th Workshop on Embedded Networked Sensors (2007)
Cho, Y., Kim, Y., Chang, N.: PVS: passive voltage scaling for wireless sensor networks. In: Proceedings of the Symposium on Low Power Electronics and Design (2007)
Wanner, L., Apte, C., Balani, R., Gupta, P., Srivastava, M.: A case for opportunistic embedded sensing in presence of hardware power variability. In: Proceedings of the 2010 International Conference on Power Aware Computing and Systems (2010)
Texas Instruments, eZ430-Chronos Development Tool Datasheet, Webpage http://www.ti.com
Texas Instruments, CC430F61xx 16-Bit Ultra-Low-Power MCU Datasheet, Webpage http://www.ti.com
Hitex Development Tools GmbH, PowerScale Datasheet, Webpage http://www.hitex.com
HAMEG Instruments GmbH, HM8143 Datasheet, Webpage http://www.hameg.com
ON Semiconductor, NCP1400A Datasheet, Webpage http://onsemi.com
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sieber, A., Nolte, J. (2013). Online Device-Level Energy Accounting for Wireless Sensor Nodes. In: Demeester, P., Moerman, I., Terzis, A. (eds) Wireless Sensor Networks. EWSN 2013. Lecture Notes in Computer Science, vol 7772. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36672-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-36672-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36671-0
Online ISBN: 978-3-642-36672-7
eBook Packages: Computer ScienceComputer Science (R0)