Machine learning based optimal renewable energy allocation in sustained wireless sensor networks


The environmental energy harvesting is adjudged as a reliable solution to power the wireless nodes for infinite time and assuring uninterrupted operation of deployed network nodes. But uncertain energy availability initiates an important research issue of energy management in rechargeable sensor nodes. An integrated approach of energy assignment principles with adaptive duty cycling has been proposed to efficiently utilize the available energy and to maximize the node performance. The R interface based machine learning ensemble approach has been used for solar irradiance prediction to pre-estimate the node duty cycle. Dynamic programming based optimization problem has been used for real time adaption of pre-computed node duty cycle. The effectiveness of proposed work has been validated using MATLAB interface by extensive simulations on real time solar energy profiles in terms of magnitude and stability of sensors average duty cycle. The proposed algorithm achieves an average duty cycle of 65% to 69% with a limit of 70% maximum duty cycle irrespective of irregular radiation patterns throughout the day as well as for different forecasting horizons. The results shows minimum variation in estimated and real time energy profiles in stable weather conditions and optimize the duty cycle in irregular weather conditions. The results also shows minimum variation (\(>2\%\)) in estimated and real time energy profiles in stable weather conditions and optimize the duty cycle in irregular weather conditions.

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  1. 1.

    Kansal, A., Hsu, J., Zahedi, S., & Srivastava, M. B. (2007). Power management in energy harvesting sensor networks. ACM Transactions on Embedded Computing Systems (TECS), 6(4), 1–32.

    Article  Google Scholar 

  2. 2.

    Fu, A. C., Modiano, E., & Tsitsiklis, J. N. (2003). Optimal energy allocation and admission control for communications satellites. IEEE/ACM Transactions on Networking, 11(3), 488–500.

    Article  Google Scholar 

  3. 3.

    Raghunathan, V., Ganeriwal, S., & Srivastava, M. (2006). Emerging techniques for long lived wireless sensor networks. IEEE Communications Magazine, 44(4), 108–114.

    Article  Google Scholar 

  4. 4.

    Sharma, V., Mukherji, U., Joseph, V., & Gupta, S. (2010). Optimal energy management policies for energy harvesting sensor nodes. IEEE Transactions on Wireless Communications, 9(4), 1326–1336.

    Article  Google Scholar 

  5. 5.

    Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de Pison, F., & Antonanzas-Torres, F. (2016). Review of photovoltaic power forecasting. Solar Energy, 136, 78–111.

    Article  Google Scholar 

  6. 6.

    Chaturvedi, D. (2016). Solar power forecasting: A review. International Journal of Computer Applications, 145(6), 28–50.

    Article  Google Scholar 

  7. 7.

    Gagne, D. J., McGovern, A., Haupt, S. E., & Williams, J. K. (2017). Evaluation of statistical learning configurations for gridded solar irradiance forecasting. Solar Energy, 150, 383–393.

    Article  Google Scholar 

  8. 8.

    Yadav, A. K., & Chandel, S. (2014). Solar radiation prediction using artificial neural network techniques: A review. Renewable and Sustainable Energy Reviews, 33, 772–781.

    Article  Google Scholar 

  9. 9.

    Fidan, M., Hocaoğlu, F. O., & Gerek, Ö. N. (2014). Harmonic analysis based hourly solar radiation forecasting model. IET Renewable Power Generation, 9(3), 218–227.

    Article  Google Scholar 

  10. 10.

    Jiménez-Pérez, P. F., & Mora-López, L. (2016). Modeling and forecasting hourly global solar radiation using clustering and classification techniques. Solar Energy, 135, 682–691.

    Article  Google Scholar 

  11. 11.

    Lin, K.-P., & Pai, P.-F. (2016). Solar power output forecasting using evolutionary seasonal decomposition least-square support vector regression. Journal of Cleaner Production, 134, 456–462.

    Article  Google Scholar 

  12. 12.

    Sharma, A., & Kakkar, A. (2017). Development of modified pro-energy algorithm for future solar irradiance estimation using level and trend factors in time series analysis. Journal of Renewable and Sustainable Energy, 9(3), 033701–033716.

    Article  Google Scholar 

  13. 13.

    Sharma, A., & Kakkar, A. (2017). Forecasting daily global solar irradiance generation using machine learning. Renewable and Sustainable Energy Reviews, 82, 2254–2269.

    Article  Google Scholar 

  14. 14.

    Sheng, H., Xiao, J., Cheng, Y., Ni, Q., & Wang, S. (2018). Short-term solar power forecasting based on weighted gaussian process regression. IEEE Transactions on Industrial Electronics, 65(1), 300–308.

    Article  Google Scholar 

  15. 15.

    Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.

    Article  Google Scholar 

  16. 16.

    Xu, Y., Heidemann, J., Estrin, D. (2001). Geography-informed energy conservation for ad hoc routing. In Proceedings of the 7th annual international conference on Mobile computing and networking (pp. 70–84). ACM.

  17. 17.

    Perkins, C., Belding-Royer, E., Das, S. (2003). Ad hoc on-demand distance vector (aodv) routing. Technical report.

  18. 18.

    Johnson, D. B., Maltz, D. A., Broch, J., et al. (2001). Dsr: The dynamic source routing protocol for multi-hop wireless ad hoc networks. Ad Hoc Networking, 5, 139–172.

    Google Scholar 

  19. 19.

    Younis, M., Youssef, M., Arisha, K. (2002). Energy-aware routing in cluster-based sensor networks. In 10th IEEE international symposium on modeling, analysis and simulation of computer and telecommunications systems, 2002. MASCOTS 2002. Proceedings (pp. 129–136). IEEE.

  20. 20.

    Kalpakis, K., Dasgupta, K., & Namjoshi, P. (2003). Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks. Computer Networks, 42(6), 697–716.

    MATH  Article  Google Scholar 

  21. 21.

    Baek, S. J., De Veciana, G., & Su, X. (2004). Minimizing energy consumption in large-scale sensor networks through distributed data compression and hierarchical aggregation. IEEE Journal on selected Areas in Communications, 22(6), 1130–1140.

    Article  Google Scholar 

  22. 22.

    Nuggehalli, P., Srinivasan, V., & Rao, R. R. (2006). Energy efficient transmission scheduling for delay constrained wireless networks. IEEE Transactions on Wireless Communications, 5(3), 531–539.

    Article  Google Scholar 

  23. 23.

    Farkas, J., Hombs, B., Tranquilli, J., Mo, S., Sherman, M., Gu, J., Fette, B. (2010). Power aware scheduling and power control techniques for multiuser detection enabled wireless mobile ad-hoc network, In Military communications conference, 2010-MILCOM 2010 (pp. 110–115). IEEE.

  24. 24.

    D. D. energy-harvesting projects. Accessed Jan 2016.

  25. 25.

    Moser, C., Thiele, L., Brunelli, D., & Benini, L. (2010). Adaptive power management for environmentally powered systems. IEEE Transactions on Computers, 59(4), 478–491.

    MathSciNet  MATH  Article  Google Scholar 

  26. 26.

    Seyedi, A., & Sikdar, B. (2010). Energy efficient transmission strategies for body sensor networks with energy harvesting. IEEE Transactions on Communications, 58(7), 2116–2126.

    Article  Google Scholar 

  27. 27.

    Noh, D. K., & Kang, K. (2011). Balanced energy allocation scheme for a solar-powered sensor system and its effects on network-wide performance. Journal of Computer and System Sciences, 77(5), 917–932.

    MathSciNet  MATH  Article  Google Scholar 

  28. 28.

    Ozel, O., Tutuncuoglu, K., Yang, J., Ulukus, S., & Yener, A. (2011). Transmission with energy harvesting nodes in fading wireless channels: Optimal policies. IEEE Journal on Selected Areas in Communications, 29(8), 1732–1743.

    Article  Google Scholar 

  29. 29.

    Ho, C. K., & Zhang, R. (2012). Optimal energy allocation for wireless communications with energy harvesting constraints. IEEE Transactions on Signal Processing, 60(9), 4808–4818.

    MathSciNet  MATH  Article  Google Scholar 

  30. 30.

    Castiglione, P., Simeone, O., Erkip, E., & Zemen, T. (2012). Energy management policies for energy-neutral source-channel coding. IEEE Transactions on Communications, 60(9), 2668–2678.

    Article  Google Scholar 

  31. 31.

    Reddy, S., & Murthy, C. R. (2012). Dual-stage power management algorithms for energy harvesting sensors. IEEE Transactions on Wireless Communications, 11(4), 1434–1445.

    Article  Google Scholar 

  32. 32.

    Bhattacharjee, S., & Bandyopadhyay, S. (2013). Lifetime maximizing dynamic energy efficient routing protocol for multi hop wireless networks. Simulation Modelling Practice and Theory, 32, 15–29.

    Article  Google Scholar 

  33. 33.

    Luo, D., Zhu, X., Wu, X., Chen, G. (2011). Maximizing lifetime for the shortest path aggregation tree in wireless sensor networks. In INFOCOM, 2011 Proceedings IEEE (pp. 1566–1574). IEEE.

  34. 34.

    Tan, H. Ö., & Körpeolu, I. (2003). Power efficient data gathering and aggregation in wireless sensor networks. ACM Sigmod Record, 32(4), 66–71.

    Article  Google Scholar 

  35. 35.

    Ok, C.-S., Lee, S., Mitra, P., & Kumara, S. (2009). Distributed energy balanced routing for wireless sensor networks. Computers & Industrial Engineering, 57(1), 125–135.

    Article  Google Scholar 

  36. 36.

    Hosseinimehr, T., & Tabesh, A. (2016). Magnetic field energy harvesting from ac lines for powering wireless sensor nodes in smart grids. IEEE Transactions on Industrial Electronics, 63(8), 4947–4954.

    Google Scholar 

  37. 37.

    Sarma, H. K. D., Kar, A., Mall, R. (2010). Energy efficient and reliable routing for mobile wireless sensor networks. In 2010 international conference on distributed computing in sensor systems workshops (DCOSSW 2010) (pp. 1–6). IEEE.

  38. 38.

    Zhang, H., Huang, S., Jiang, C., Long, K., Leung, V. C., & Poor, H. V. (2017). Energy efficient user association and power allocation in millimeter-wave-based ultra dense networks with energy harvesting base stations. IEEE Journal on Selected Areas in Communications, 35(9), 1936–1947.

    Article  Google Scholar 

  39. 39.

    Abdul-Salaam, G., Abdullah, A. H., & Anisi, M. H. (2017). Energy-efficient data reporting for navigation in position-free hybrid wireless sensor networks. IEEE Sensors Journal, 17(7), 2289–2297.

    Article  Google Scholar 

  40. 40.

    Zhang, P., Nevat, I., Peters, G. W., Septier, F., & Osborne, M. A. (2018). Spatial field reconstruction and sensor selection in heterogeneous sensor networks with stochastic energy harvesting. IEEE Transactions on Signal Processing, 66(9), 2245–2257.

    MathSciNet  MATH  Article  Google Scholar 

  41. 41.

    Zhang, H., Du, J., Cheng, J., Long, K., & Leung, V. C. (2018). Incomplete csi based resource optimization in swipt enabled heterogeneous networks: A non-cooperative game theoretic approach. IEEE Transactions on Wireless Communications, 17(3), 1882–1892.

    Article  Google Scholar 

  42. 42.

    Bhardwaj, M., Chandrakasan, A. P. (2002). Bounding the lifetime of sensor networks via optimal role assignments. In INFOCOM 2002. Twenty-first annual joint conference of the IEEEE computer and communications societies. Proceedings (Vol. 3, pp. 1587–1596). IEEE.

  43. 43.

    Giridhar, A., Kumar, P. (2005). Maximizing the functional lifetime of sensor networks. In Fourth international symposium on information processing in sensor networks, IPSN 2005 (pp. 5–12). IEEE.

  44. 44.

    Kansal, A., Ramamoorthy, A., Srivastava, M. B., Pottie, G. J. (2005). On sensor network lifetime and data distortion. In International symposium on information theory, ISIT 2005. Proceedings (pp. 6–10). IEEE.

  45. 45.

    Jeong, J., & Culler, D. (2012). A practical theory of micro-solar power sensor networks. ACM Transactions on Sensor Networks (TOSN), 9(1), 9.

    Article  Google Scholar 

  46. 46.

    Del Testa, D., Michelusi, N., & Zorzi, M. (2016). Optimal transmission policies for two-user energy harvesting device networks with limited state-of-charge knowledge. IEEE Transactions on Wireless Communications, 15(2), 1393–1405.

    Article  Google Scholar 

  47. 47.

    Erdem, H., & Gungor, V. (2018). On the lifetime analysis of energy harvesting sensor nodes in smart grid environments. Ad Hoc Networks, 75, 98–105.

    Article  Google Scholar 

  48. 48.

    Zhao, Y., Govindan, R., & Estrin, D. (2002). Residual energy scans for monitoring wireless sensor networks. Los Angeles: Center for Embedded Network Sensing.

    Google Scholar 

  49. 49.

    Jiang, X., Polastre, J., Culler, D. (2005). Perpetual environmentally powered sensor networks. In Proceedings of the 4th international symposium on information processing in sensor networks (p. 65). IEEE Press.

  50. 50.

    Mora-Merchan, J., Larios, D., Barbancho, J., Molina, F. J., Sevillano, J. L., & León, C. (2013). mtossim: A simulator that estimates battery lifetime in wireless sensor networks. Simulation Modelling Practice and Theory, 31, 39–51.

    Article  Google Scholar 

  51. 51.

    Kansal, A., Potter, D., & Srivastava, M. B. (2004). Performance aware tasking for environmentally powered sensor networks. ACM SIGMETRICS Performance Evaluation Review, 32(1), 223–234.

    Article  Google Scholar 

  52. 52.

    Niyato, D., Hossain, E., & Fallahi, A. (2007). Sleep and wakeup strategies in solar-powered wireless sensor/mesh networks: Performance analysis and optimization. IEEE Transactions on Mobile Computing, 6(2), 221–236.

    Article  Google Scholar 

  53. 53.

    Vigorito, C. M., Ganesan, D., Barto, A. G. (2007). 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’07 (pp. 21–30). IEEE.

  54. 54.

    Gu, Y., Zhu, T., He, T. (2009). Esc: Energy synchronized communication in sustainable sensor networks. In 17th IEEE international conference on network protocols, ICNP 2009 (pp. 52–62). IEEE.

  55. 55.

    Merlin, C. J., & Heinzelman, W. B. (2010). Duty cycle control for low-power-listening mac protocols. IEEE Transactions on Mobile Computing, 9(11), 1508–1521.

    Article  Google Scholar 

  56. 56.

    Tadayon, N., Khoshroo, S., Askari, E., Wang, H., & Michel, H. (2013). Power management in smac-based energy-harvesting wireless sensor networks using queuing analysis. Journal of Network and Computer Applications, 36(3), 1008–1017.

    Article  Google Scholar 

  57. 57.

    Valera, A. C., Soh, W.-S., & Tan, H.-P. (2013). Energy-neutral scheduling and forwarding in environmentally-powered wireless sensor networks. Ad Hoc Networks, 11(3), 1202–1220.

    Article  Google Scholar 

  58. 58.

    Peng, S., & Low, C. (2014). Prediction free energy neutral power management for energy harvesting wireless sensor nodes. Ad Hoc Networks, 13, 351–367.

    Article  Google Scholar 

  59. 59.

    Valera, A. C., Soh, W.-S., & Tan, H.-P. (2017). Enabling sustainable bulk transfer in environmentally-powered wireless sensor networks. Ad Hoc Networks, 54, 85–98.

    Article  Google Scholar 

  60. 60.

    Quinlan, J. R. (1993). Combining instance-based and model-based learning. In Proceedings of the tenth international conference on machine learning (pp. 236–243).

  61. 61.

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

    Article  Google Scholar 

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Correspondence to Amandeep Sharma.

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Sharma, A., Kakkar, A. Machine learning based optimal renewable energy allocation in sustained wireless sensor networks. Wireless Netw 25, 3953–3981 (2019).

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  • Solar forecasting
  • Forecasting horizons
  • Energy assignment principles
  • Adaptive duty cycling
  • Energy neutral state
  • Storage efficiency