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Enhanced-Pro: A New Enhanced Solar Energy Harvested Prediction Model for Wireless Sensor Networks

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Abstract

Energy harvesting facilitates Wireless Sensor Networks (WSN) to work in perpetual mode. But, the amount and duration of green energy depend on the unpredictable behavior of ambient energy sources. Limited knowledge of future energy availability is the main constraint in designing routing and MAC (Medium Access Control) protocol. If the prediction of future energy availability can be done with fine accuracy, then energy management strategies can be designed accordingly. Predicting solar irradiance value is a challenging issue as it has seasonal and daily temperature variation. Enhanced-Pro, a novel energy prediction strategy is proposed here by considering past energy observations as well as current energy intake, and its effectiveness is also tested. It is a modification of Pro-Energy, which is a landmark solar energy prediction solution in terms of accuracy. In this work, real-life solar traces are used for prediction. The algorithm incorporates two factors tuning factor and fine adjustment index. These two factors help to enhance prediction accuracy. Enhanced-Pro is compared with Pro-Energy, I-Pro-Energy, and QL-SEP. The simulation result proves Enhanced-Pro delivers better performance in the scale of computational complexity and accuracy.

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References

  1. Liu, S., & Maddham, M. Power-management techniques for wireless sensor networks and similar low-power communication devices based on non- rechargeable batteries. Journal of Computer Networks and Communications, Vol. 2012 , Article ID 757291, p 10.

  2. Zhang, X., & Pless, (2007). Minimum power configuration for wirelesss communication in sensor networks. ACM Transactions on Sensor Networks, 3(2), 11.

    Article  Google Scholar 

  3. Ganesan, D., Ganeriwal, S., Shim, H., Tsiatsis, V., Srivastava, M. B. (2005). Estimating clock uncertainty for efficient duty-cycling in sensor networks. In proceedings of the third ACM conference on sensor networking systems, pp 130–141.

  4. Grimmer, M., Dutta, P., Bibyk, S., Arora, A., & Culler, D. (2005). Design of a wireless sensor network platform for detecting rare, random, and ephemeral events. In Proceedings of the 4 th international conference on information processing in sensor networks, pp 497–502.

  5. Chandrakasan, A., Heinzelman, W.R., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences vol.2, pp. 10

  6. Govindan, R., Intanagonwiwat, C., & Estrin, D. (2000). Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proceedings of the 6th annual international conference on mobile computing and networking, pp 56–67. ACM.

  7. Ganesan, D., Desnoyers, P., Li, M., Li, H., & Shenoy, P. (2005). PRESTO: A predictive storage architecture for sensor networks. In Proceedings of the 10th conference on hot topics in operating systems, pp 23–23. USENIX Association.

  8. Lai, Ten H., Kumar, Santosh, & Balogh, J’ozsef. (2008). On k-coverage in a mostly sleeping sensor network. Wireless Networks, 14(3), 277–294.

    Article  Google Scholar 

  9. Xing, G., Wang, X., Zhang, Y., Pless, R., Lu, C., & Gill, C. (2003). Integrated coverage and connectivity configuration in wireless sensor networks. In Proceedings of the first international conference on embedded networked sensor systems, pp 28–39. ACM.

  10. Chandra, A., Liu, H., & Srivastava, J. (2006). eSENSE: Energy efficient stochastic sensing framework for wireless sensor platforms. In Proceedings of the fifth international conference on information processing in sensor networks, pp 235–242.

  11. Rawat, P., Priyadarshi, R., & Nath, V. (2018). Energy dependent cluster formation in heterogeneous wireless sensor network. Microsystem Technologies, 25, 2313–2321.

    Google Scholar 

  12. Kulkarni, P., & Sudevalayam, S. (2011). Energy harvesting sensor nodes: Survey and implications. IEEE Communications Surveys and Tutorials, 13(3), 443–461.

    Article  Google Scholar 

  13. Yu, M., & Chao, P. C. (2018). A new multi-mode multi-input–multi-output (MIMO) converter in an efficient low-voltage energy harvesting system for a gas sensor. Microsystem Technologies, 24, 4477–4492.

    Article  Google Scholar 

  14. Boxwell, M. (2012). Solar electricity handbook: A simple, practical guide to solar energy. Coventry: Greenstream Publishing.

    Google Scholar 

  15. Stickler, G.(2016). Educational brief—solar radiation and the earth system. National Aeronautics and Space Administration. [Archived from the original on 25 April 2016. Retrieved 5 May 2016].

  16. Hsu, J., Kansal, A., Zahedi, S., & Srivastava, M. B. (2007). Power management in energy harvesting sensor networks. ACM Transaction Embeded Computer System, 6(4), 32.

    Article  Google Scholar 

  17. Bergonzini, C., Piorno, J., Rosing, T., & Atienza, D. (2009). Prediction and management in energy harvested wireless sensor nodes. In Proceedings of wireless VITAE, pp. 6–10, May 17–20.

  18. Petrioli, C., Cammarano, A., & Spenza, D. (2012) Pro-energy: A novel energy prediction model for solar and wind energy-harvesting wireless sensor networks. In Procedings of IEEE international conference mobile ad-hoc sensor systems, pp. 75–83.

  19. Saleem, U., Qureshi, H. K., Pitsillides, A., Saleem, M., & Lestas, M. (2017). Harvested energy prediction schemes for wireless sensor networks: Performance evaluation and enhancements. Wireless Communications and Mobile Computing. Volume 2017, Article ID 6928325, p. 14.

  20. Kosunalp, S. (2016). A new energy prediction algorithm for energy harvesting wireless sensor networks with Q-learning. IEEE Access, 4, 5755–5763.

    Article  Google Scholar 

  21. Website: NREL: Measurement and instrumentation data center. 2018, http://www.nrel.gov/midc/.

  22. Andreas, A., & Stoffel, T. (1981). NREL Solar Radiation Research Laboratory (SRRL): Baseline Measurement System (BMS); Golden, Colorado (Data); NREL Report No. DA-5500-56488. https://doi.org/10.5439/1052221

  23. Olson, K., & Andreas, A. (2012). Natural Energy Laboratory of Hawaii Authority (NELHA):Hawaii Ocean Science and Technology Park; Kailua-Kona, Hawaii (Data); NREL Report No. DA-5500-64450. https://doi.org/10.7799/1183461

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Authors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Kolkata, India

    Moumita Deb

  2. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Sarbani Roy

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  1. Moumita Deb
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  2. Sarbani Roy
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Correspondence to Sarbani Roy.

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Deb, M., Roy, S. Enhanced-Pro: A New Enhanced Solar Energy Harvested Prediction Model for Wireless Sensor Networks. Wireless Pers Commun 117, 1103–1121 (2021). https://doi.org/10.1007/s11277-020-07913-y

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  • DOI: https://doi.org/10.1007/s11277-020-07913-y

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