Towards Enabling Uninterrupted Long-Term Operation of Solar Energy Harvesting Embedded Systems

  • Bernhard Buchli
  • Felix Sutton
  • Jan Beutel
  • Lothar Thiele
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8354)


In this work we describe a systematic approach to power subsystem capacity planning for solar energy harvesting embedded systems, such that uninterrupted, long-term (i.e., multiple years) operation at a predefined performance level may be achieved. We propose a power subsystem capacity planning algorithm based on a modified astronomical model to approximate the harvestable energy and compute the required battery capacity for a given load and harvesting setup. The energy availability model takes as input the deployment site’s latitude, the panel orientation and inclination angles, and an indication of expected meteorological and environmental conditions.We validate the model’s ability to predict the harvestable energy with power measurements of a solar panel. Through simulation with 10 years of solar traces from three different geographical locations and four harvesting setups, we demonstrate that our approach achieves 100% availability at up to 53% smaller batteries when compared to the state-of-the-art.


Wireless sensor networks energy harvesting modeling experimentation 


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  1. 1.
    Mini, R.A., Loureiro, A.A.: Energy in wireless sensor networks. In: Middleware for Network Eccentric and Mobile Applications, pp. 3–24. Springer (2009)Google Scholar
  2. 2.
    Buchli, B., Sutton, F., Beutel, J.: GPS-equipped wireless sensor network node for high-accuracy positioning applications. In: Picco, G.P., Heinzelman, W. (eds.) EWSN 2012. LNCS, vol. 7158, pp. 179–195. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Chalasani, S., Conrad, J.M.: A survey of energy harvesting sources for embedded systems. In: IEEE Southeastcon, pp. 442–447. IEEE (2008)Google Scholar
  4. 4.
    Sudevalayam, S., Kulkarni, P.: Energy harvesting sensor nodes: Survey and implications. IEEE Communications Surveys & Tutorials 13(3), 443–461 (2011)CrossRefGoogle Scholar
  5. 5.
    Hanssen, L., Gakkestad, J.: Solar Cell Size Requirement for Powering of Wireless Sensor Network Used in Northern Europe. In: Proceedings of the International Workshops on PowerMEMS, pp. 17–20 (2010)Google Scholar
  6. 6.
    Seah, W.K., et al.: Research in Energy Harvesting Wireless Sensor Networks and the Challenges Ahead (2012)Google Scholar
  7. 7.
    Taneja, J., et al.: Design, Modeling, and Capacity Planning for Micro-solar Power Sensor Networks. In: Proceedings of the 7th International Conference on Information Processing in Sensor Networks, IPSN 2008, pp. 407–418. IEEE Computer Society, Washington, DC (2008)Google Scholar
  8. 8.
    Le, T.N., et al.: Power Manager with PID controller in Energy Harvesting Wireless Sensor Networks. In: 2012 IEEE International Conference on Green Computing and Communications (GreenCom), pp. 668–670. IEEE (2012)Google Scholar
  9. 9.
    Vigorito, C.M., et al.: Adaptive control of duty cycling in energy-harvesting wireless sensor networks. In: 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2007, pp. 21–30. IEEE (2007)Google Scholar
  10. 10.
    Piorno, J.R., et al.: Prediction and management in energy harvested wireless sensor nodes. In: 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology, Wireless VITAE 2009, pp. 6–10. IEEE (2009)Google Scholar
  11. 11.
    Dave, J., et al.: Computation of Incident Solar Energy. IBM Journal of Research and Development 19(6), 539–549 (1975)CrossRefGoogle Scholar
  12. 12.
    Kansal, A., et al.: Power management in energy harvesting sensor networks. ACM Transactions on Embedded Computing Systems (TECS) 6(4), 32 (2007)CrossRefGoogle Scholar
  13. 13.
    Zhang, P., et al.: Hardware design experiences in ZebraNet. In: Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, SenSys 2004, pp. 227–238. ACM, New York (2004)Google Scholar
  14. 14.
    Raghunathan, V., et al.: Design considerations for solar energy harvesting wireless embedded systems. In: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks, p. 64. IEEE Press (2005)Google Scholar
  15. 15.
    Park, C., Chou, P.H.: Ambimax: Autonomous energy harvesting platform for multi-supply wireless sensor nodes. In: 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, SECON 2006, vol. 1, pp. 168–177. IEEE (2006)Google Scholar
  16. 16.
    Sitka, P., et al.: Fleck-a platform for real-world outdoor sensor networks. In: 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, ISSNIP 2007, pp. 709–714. IEEE (2007)Google Scholar
  17. 17.
    Glatz, P.M., et al.: Designing perpetual energy harvesting systems explained with rivermote: A wireless sensor network platform for river monitoring. Electronic Journal of Structural Engineering, Special Issue: Wireless Sensor Networks and Practical Applications, 55–65 (2010)Google Scholar
  18. 18.
    Lu, J., Whitehouse, K.: SunCast: fine-grained prediction of natural sunlight levels for improved daylight harvesting. In: Proceedings of the 11th International Conference on Information Processing in Sensor Networks, pp. 245–256. ACM (2012)Google Scholar
  19. 19.
    Sharma, N., et al.: Predicting solar generation from weather forecasts using machine learning. In: 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 528–533. IEEE (2011)Google Scholar
  20. 20.
    Bacher, P., et al.: Online short-term solar power forecasting. Solar Energy 83(10), 1772–1783 (2009)CrossRefGoogle Scholar
  21. 21.
    Kooti, H., et al.: Energy Budget Management for Energy Harvesting Embedded Systems. In: 2012 IEEE 18th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), pp. 320–329. IEEE (2012)Google Scholar
  22. 22.
    Jeong, J., Culler, D.: Predicting the Long-Term Behavior of a Micro-Solar Power System. ACM Transactions on Embedded Computing Systems (TECS) 11(2), 35 (2012)CrossRefGoogle Scholar
  23. 23.
    Bohren, C.F., Clothiaux, E.: Atmospheric Optics. Wiley-VCH (2006)Google Scholar
  24. 24.
    Heinemann, D., et al.: Forecasting of solar radiation. Solar energy resource management for electricity generation from local level to global scale. Nova Science Publishers, New York (2006)Google Scholar
  25. 25.
    Gubler, S., Gruber, S., Purves, R.: Uncertainties of parameterized surface downward clear-sky shortwave and all-sky longwave radiation. Atmospheric Chemistry and Physics 12(11), 5077–5098 (2012)CrossRefGoogle Scholar
  26. 26.
    Green, M.A., Emery, K., Hishikawa, Y., Warta, W., Dunlop, E.D.: Solar cell efficiency tables (version 39). Progress in Photovoltaics: Research and Applications 20(1), 12–20 (2011)CrossRefGoogle Scholar
  27. 27.
    Pister, K.: Smartdust: Autonomous sensing and communication in a cubic millimeterGoogle Scholar
  28. 28.
    Brunelli, D., et al.: An efficient solar energy harvester for wireless sensor nodes. In: Proceedings of the Conference on Design, Automation and Test in Europe, pp. 104–109. ACM (2008)Google Scholar
  29. 29.
    Corke, P., et al.: Long-duration solar-powered wireless sensor networks. In: Proceedings of the 4th Workshop on Embedded Networked Sensors, EmNets 2007, pp. 33–37. ACM, New York (2007)Google Scholar
  30. 30.
    Buchli, B., Aschwanden, D., Beutel, J.: Battery state-of-charge approximation for energy harvesting embedded systems. In: Demeester, P., Moerman, I., Terzis, A. (eds.) EWSN 2013. LNCS, vol. 7772, pp. 179–196. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  31. 31.
    Bergveld, H.J.: Battery management systems: design by modelling. PhD thesis, Enschede (June 2001)Google Scholar
  32. 32.
    Kansal, A., Potter, D., Srivastava, M.B.: Performance aware tasking for environmentally powered sensor networks. In: ACM SIGMETRICS Performance Evaluation Review, vol. 32, pp. 223–234. ACM (2004)Google Scholar
  33. 33.
    Kansal, A., et al.: Harvesting aware power management for sensor networks. In: Proceedings of the 43rd Annual Design Automation Conference, pp. 651–656. ACM (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bernhard Buchli
    • 1
  • Felix Sutton
    • 1
  • Jan Beutel
    • 1
  • Lothar Thiele
    • 1
  1. 1.Computer Engineering and Networks LaboratoryETH ZurichZurichSwitzerland

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