Abstract
Energy harvesting (EH) is a major step in solving the critical issue of availability of energy for sensor nodes. However, it throws many challenges. The applications built on the sensor networks powered by EH need to adapt their operations yet serve the purpose. We propose a distributed smart application for a multihop sensor network and in general in the future Internet of Things (IoT) where a network node executes an optimal number of policies to minimize the difference between available energy and consumed energy (called residual energy) for the execution of an application policy. We formulate this as a multi-criteria optimization problem and solve it using linear programming Parametric Analysis. We demonstrate our approach on a testbed with solar panels. We also use a realistic solar energy trace with a three year database including seasonality. The smart application is capable of adapting itself to its current energy level as well as that of the network. Our analytical results show a close match with the measurements conducted over testbed.
Chapter PDF
Similar content being viewed by others
Keywords
References
ZigBee Green, http://www.zigbee.org/Standards/Overview.aspx
Chalasani, S., Conrad, J.M.: A survey of energy harvesting sources for embedded systems. In: IEEE Southeastcon 2008 (April 2008)
Jiang, X., Polastre, J., Culler, D.: Perpetual environmentally powered sensor networks. In: Fourth International Symposium on Information Processing in Sensor Networks (2005)
Kansal, A., et al.: Power Management in Energy Harvesting Sensor Networks. ACM Transactions on Embedded Computing Systems (2007)
Audet, D., de Oliveira, L.C., MacMillan, N., Marinakis, D., Wu, K.: Scheduling recurring tasks in energy harvesting sensors. In: IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS (2011)
Khodaian, A.M., Khalaj, B.H.: Delay-constrained utility maximisation in multihop random access networks. IET Communications 4(16), 1908–1918 (2010)
Palomar, D.P., Chiang, M.: A tutorial on decomposition methods for network utility maximization. IEEE Journal on Selected Areas in Communications 24(8), 1439–1451 (2006)
Moser, C., Thiele, L., Brunelli, D., Benini, L.: Adaptive Power Management for Environmentally Powered Systems. IEEE Transactions on Computers 59(4), 478–491 (2010)
Moser, C., et al.: Lazy scheduling for energy harvested sensor nodes. In: Conference on Distributed and Parallel Embedded Systems, DIPES 2006 (2006)
Hsu, J., Zahedi, S., Kansal, A., Srivastava, M., Raghunathan, V.: Adaptive Duty Cycling for Energy Harvesting Systems. In: Proceedings of the 2006 International Symposium on Low Power Electronics and Design (2006)
Vigorito, C.M., Ganesan, D., Barto, A.G.: Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks. In: IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (2007)
Hasenfratz, et al.: Analysis, Comparison, and Optimization of Routing Protocols for Energy Harvesting Wireless Sensor Networks. In: Sensor Networks, Ubiquitous, and Trustworthy Computing, SUTC (2010)
Lowry Range Solar Station, Colarado State Land Board, http://www.nrel.gov/midc/lrss
Bazaraa, M.S., Jarvis, J.J., Sherali, H.D.: Linear Programming and Network Flows, 2nd edn. John Wiley & Sons Inc. (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 IFIP International Federation for Information Processing
About this paper
Cite this paper
Prabhakar, T.V., Akshay Uttama Nambi, S.N., Venkatesha Prasad, R., Shilpa, S., Prakruthi, K., Niemegeers, I. (2012). A Distributed Smart Application for Solar Powered WSNs. In: Bestak, R., Kencl, L., Li, L.E., Widmer, J., Yin, H. (eds) NETWORKING 2012. NETWORKING 2012. Lecture Notes in Computer Science, vol 7290. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30054-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-30054-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30053-0
Online ISBN: 978-3-642-30054-7
eBook Packages: Computer ScienceComputer Science (R0)