Abstract
Due to non-homogeneous spread of sunlight, sensing nodes typically have non-uniform energy profiles in rechargeable Wireless Sensor Networks (WSNs). An energy-aware work load distribution is therefore necessary for good data accuracy while ensuring an energy-neutral operation. Recently proposed signal approximation strategies, in form of Compressive Sensing, assume uniform sampling and thus cannot be deployed to facilitate energy neutral operation in rechargeable WSNs. We propose a sparse approximation driven sensing technique (EAST) that adapts sensor node sampling workload according to solar energy availability. To the best of our knowledge, we are the first to propose sparse approximation for modeling energy-aware work load distribution in order to improve signal approximation from rechargeable WSNs. Experimental result, by using data from an outdoor WSN deployment, suggests that EAST significantly improves the approximation accuracy while supporting approximately 50% higher sensor on-time compared to an approach that assumes uniform energy profile of the nodes.
This work was done while Rajib Rana was an intern at CSIRO ICT center, Australia.
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
Habitat monitoring on great duck island, http://www.greatduckisland.net/index.php
Alon, N., Matias, Y., Szegedy, M.: The space complexity of approximating the frequency moments. Journal of Computer and System Sciences, 20–29 (1996)
Anastasi, G., Conti, M., Francesco, M., Passarella, A.: Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks 7(3), 537–568 (2009)
Bajwa, W., Haupt, J., Sayeed, A., Nowak, R.: Compressive wireless sensing. In: IPSN 2006, 134–142 (2006)
Alippi, C., Anastasi, G., Francesco, M.D., Roveri, M.: An adaptive sampling algorithm for effective energy management in wireless sensor networks with energy-hungry sensors. In: IEEE-Transactions on Instrumentation and Measurement
Candés, E.: Compressive sampling. In: Proc. of the Int. Congress of Mathematics (2006)
Chou, C.T., Rana, R., Hu, W.: Energy efficient information collection in wireless sensor networks using adaptive compressive sensing. In: Proc. of the 34th Annual IEEE Conference on Local Computer Networks (LCN 2009), pp. 443–450 (2009)
Deshpande, A., Guestrin, C., Madden, S., Hellerstein, J., Hong, W.: Model-driven data acquisition in sensor networks. In: VLDB 2004, pp. 588–599. VLDB Endowment (2004)
Duarte, M.F., Wakin, M.B., Baron, D., Baraniuk, R.G.: Universal distributed sensing via random projections. In: IPSN 2006, pp. 177–185 (2006)
Gupta, H., Navda, V., Das, S., Chowdhary, V.: Efficient gathering of correlated data in sensor networks. ACM Trans. Sen. Netw. 4(1), 1–31 (2008)
Hu, W., Bulusu, N., Chou, C.T., Jha, S., Taylor, A., Nghia, V.: Design and evaluation of a hybrid sensor network for cane toad monitoring. ACM Trans. Sen. Netw. 5(1), 1–28 (2009)
Ji, S., Xue, Y., Carin, L.: Bayesian compressive sensing. IEEE Trans. Signal Processing (2007)
Kansal, A., Hsu, J., Zahedi, S., Srivastava, M.B.: Power management in energy harvesting sensor networks. Trans. on Embedded Computing Sys. 6(4), 32 (2007)
Liu, C., Wu, K., Pei, J.: An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation. IEEE Trans. Parallel Distrib. Syst. 18(7), 1010–1023 (2007)
Polastre, J.R.: PhD thesis
Quer, G., Masiero, R., Munaretto., D., Rossi, M., Widmer, J., Zorzi, M.: On the interplay between routing and signal representation for compressive sensing in wireless sensor networks. In: ITA (2007)
Raghunathan, V., Ganeriwal, S., Srivastava, M.: Emerging techniques for long lived wireless sensor networks. IEEE Communications Magazine 44(4), 108–114 (2006)
Raghunathan, V., Kansal, A., Hsu, J., Friedman, J., Srivastava, M.B.: Design considerations for solar energy harvesting wireless embedded systems. In: IPSN 2005, p. 64 (2005)
Reboul, S., Benjelloun, M.: Joint segmentation of the wind speed and direction. Signal Process. 86(4), 744–759 (2006)
Wang, W., Garofalakis, M., Ramchandran, K.: Distributed sparse random projections for refinable approximation. In: IPSN 2007, pp. 331–339 (2007)
Wark, T., Hu, W., Corke, P., Hodge, J., Keto, A., Mackey, B., Foley, G., Sikka, P., Brunig, M.: Springbrook: Challenges in developing a long-term rainforest wireless sensor network. In: ISSNIP (December 2008)
Willett, R., Martin, A., Nowak, R.: Backcasting: adaptive sampling for sensor networks. In: IPSN 2004, pp. 124–133 (2004)
Zhou, J., De Roure, D.: Floodnet: Coupling adaptive sampling with energy aware routing in a flood warning system. J. Comput. Sci. Technol. 22(1), 121–130 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rana, R., Hu, W., Chou, C.T. (2010). Energy-Aware Sparse Approximation Technique (EAST) for Rechargeable Wireless Sensor Networks. In: Silva, J.S., Krishnamachari, B., Boavida, F. (eds) Wireless Sensor Networks. EWSN 2010. Lecture Notes in Computer Science, vol 5970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11917-0_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-11917-0_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11916-3
Online ISBN: 978-3-642-11917-0
eBook Packages: Computer ScienceComputer Science (R0)