Abstract
Using solar power in wireless sensor networks (WSNs) requires adaptation to a highly varying energy supply. From an application’s perspective, however, it is often preferred to operate at a constant quality level as opposed to changing application behavior frequently. Reconciling the varying supply with the fixed demand requires good tools for predicting supply such that its average is computed and demand is fixed accordingly. In this paper, we describe a probabilistic observation-based model for harvested solar energy, which accounts for both long-term tendencies and temporary environmental conditions. Based on this model, we develop a time-slot-based energy allocation scheme to use the periodically harvested solar energy optimally, while minimizing the variance in energy allocation. Our algorithm is tested on both outdoor and indoor testbeds, demonstrating the efficacy of the approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Corke, P., Valencia, P., Sikka, P., Wark, T., Overs, L.: Long-duration solar-powered wireless sensor networks. In: EmNets (2007)
Minami, M., Morito, T., Morikawa, H., Aoyama, T.: Solar Biscuit: a battery-less wireless sensor network system for environmental monitoring applications. In: INSS (2005)
Simjee, F., Chou, P.H.: Everlast: Longlife, supercapacitoroperated wireless sensor node. In: ISLPED (2006)
Taneja, J., Jeong, J., Culler, D.: Design, modeling and capacity planning for micro-solar power sensor networks. In: IPSN (2008)
Maleki, M., Dantu, K., Pedram, M.: Lifetime prediction routing in mobile ad hoc networks. In: WCNC (2003)
Shah, R.C., Rabaey, J.M.: Energy aware routing for low energy ad hoc sensor networks. In: WCNC (2002)
Younis, M., Youssef, M., Arisha, K.: Energy-aware routing in cluster-based sensor networks. In: MASCOT (2002)
Zhao, J., Govindan, R., Estrin, D.: Residual energy scans for monitoring wireless sensor networks. In: WCNC (2002)
Mini, R.A.F., Nath, B., Loureiro, A.A.F.: A probabilistic approach to predict the energy consumption in wireless sensor networks. In: IV Workshop de Comunicao sem Fio e Computao Mvel. Sas Paulo (2002)
Kansal, A., Srivastava, M.B.: An environmental energy harvesting framework for sensor networks. In: ISLPED (2003)
Voigt, T., Ritter, H., Schiller, J.: Utilizing solar power in wireless sensor networks. In: LCN (2003)
Kansal, A., Hsu, J., Zahedi, S., Srivastava, M.B.: Power management in energy harvesting sensor networks. ACM Transactions on Embedded Computing Systems 6(4), 1–38 (2007)
Vigorito, C.M., Ganesan, D., Barto, A.G.: Adaptive control of duty cycling in energy-harvesting wireless sensor networks. In: SECON (2007)
Kumar, P., Varaiya, P.: Stochastic Systems: Estimation, Identification and Adaptive Control. Prentice-Hall, Inc., Englewood Cliffs (1986)
Yang, Y., Wang, L., Noh, D.K., Le, H.K., Abdelzaher, T.: SolarStore: Enhancing data reliability in solar-powered storage-centric sensor networks. In: MobiSys (2009)
Wang, L., Noh, D.K., Yang, Y., Le, H.K., Abdelzaher, T.: AdaptSens: An adaptive data collection and storage service for solar-powered sensor networks. In: SECON 2009 (in submission) (2009), http://www.cs.uiuc.edu/homes/dnoh/Lili09.pdf
X10: Smart Home Controller, http://www.x10.com/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Noh, D.K., Wang, L., Yang, Y., Le, H.K., Abdelzaher, T. (2009). Minimum Variance Energy Allocation for a Solar-Powered Sensor System. In: Krishnamachari, B., Suri, S., Heinzelman, W., Mitra, U. (eds) Distributed Computing in Sensor Systems. DCOSS 2009. Lecture Notes in Computer Science, vol 5516. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02085-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-02085-8_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02084-1
Online ISBN: 978-3-642-02085-8
eBook Packages: Computer ScienceComputer Science (R0)