A low complexity online controller using fuzzy logic in energy harvesting WSNs


In this paper, we present a fuzzy logic based scheme for a two hop energy harvesting (EH) wireless sensor network (WSN). Incorporating data and energy causality constraints, discrete transmission rates, finite energy and data buffers, a fuzzy model is developed which uses network throughput, battery level and channel gain as inputs. The fuzzy scheme is then compared with optimum, modified optimum, and Markov decision process (MDP) schemes in terms of computational complexity, throughput, battery level and data buffer capacity. The throughput results show that the fuzzy online scheme preforms closely to the compared schemes and avoids battery depletion even when the number of discrete transmission rates are increased.

This is a preview of subscription content, access via your institution.


  1. 1

    Ephremides A. Energy concerns in wireless networks. IEEE Wirel Commun, 2002, 9: 48–59

    Article  Google Scholar 

  2. 2

    Jiang X, Polastre J, Culler D. Perpetual environmentally powered sensor networks. In: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks, Boise, 2005. 463–468

    Google Scholar 

  3. 3

    Yeatman E M. Advances in power sources for wireless sensor nodes. In: Proceedings of International Workshop on Wearable Implantable BSN, 2004

    Google Scholar 

  4. 4

    Powercast Lifetime Power R Energy Harvesting Kit Deutschland. 2017. https://doi.org/www.mouser.de/new/powercast/powercastlifetimepower/

  5. 5

    Ulukus S, Yener A, Erkip E, et al. Energy harvesting wireless communications: a review of recent advances. IEEE J Sel Areas Commun, 2015, 33: 360–381

    Article  Google Scholar 

  6. 6

    Medepally B, Mehta N B. Voluntary energy harvesting relays and selection in cooperative wireless networks. IEEE Trans Wirel Commun, 2010, 9: 3543–3553

    Article  Google Scholar 

  7. 7

    Kashef M, Ephremides A. Optimal partial relaying for energy-harvesting wireless networks. IEEE/ACM Trans Netw, 2016, 24: 113–122

    Article  Google Scholar 

  8. 8

    Tutuncuoglu K, Yener A. Optimum transmission policies for battery limited energy harvesting nodes. IEEE Trans Wirel Commun, 2012, 11: 1180–1189

    Article  Google Scholar 

  9. 9

    Chen H, Li Y H, Rebelatto J L, et al. Harvest-then-cooperate: wireless-powered cooperative communications. IEEE Trans Signal Process, 2015, 63: 1700–1711

    MathSciNet  Article  Google Scholar 

  10. 10

    Nasir A A, Zhou X, Durrani S, et al. Wireless-powered relays in cooperative communications: time-switching relaying protocols and throughput analysis. IEEE Trans Commun, 2015, 63: 1607–1622

    Article  Google Scholar 

  11. 11

    Ishibashi K, Ochiai H, Tarokh V. Energy harvesting cooperative communications. In: Proceedings of the 23rd International Symposium on Personal, Indoor and Mobile Radio Communications, 2012. 1819–1823

    Google Scholar 

  12. 12

    Minasian A, Shahbazpanahi S, Adve R S. Energy harvesting cooperative communication systems. IEEE Trans Wirel Commun, 2014, 13: 6118–6131

    Article  Google Scholar 

  13. 13

    Aoudia F A, Gautier M, Berder O. RLMan: an energy manager based on reinforcement learning for energy harvesting wireless sensor networks. IEEE Trans Green Commun Netw, 2018, 2: 408–417

    Article  Google Scholar 

  14. 14

    Cong Y R, Zhou X Y. Event-trigger based robust-optimal control for energy harvesting transmitter. IEEE Trans Wirel Commun, 2017, 16: 744–756

    Article  Google Scholar 

  15. 15

    Liu W C, Zhou X Y, Durrani S, et al. Energy harvesting wireless sensor networks: delay analysis considering energy costs of sensing and transmission. IEEE Trans Wirel Commun, 2016, 15: 4635–4650

    Google Scholar 

  16. 16

    Li T, Fan P Y, Chen Z C, et al. Optimum transmission policies for energy harvesting sensor networks powered by a mobile control center. IEEE Trans Wirel Commun, 2016, 15: 6132–6145

    Article  Google Scholar 

  17. 17

    Kang X, Ho C K, Sun S. Full-duplex wireless-powered communication network with energy causality. IEEE Trans Wirel Commun, 2015, 14: 5539–5551

    Article  Google Scholar 

  18. 18

    Kravets P, Kyrkalo R. Fuzzy logic controller for embedded systems. In: Proceedings of International Conference on Perspective Technologies and Methods in MEMS Design, Ukraine, 2009

    Google Scholar 

  19. 19

    Jiang H F, Sun Y J, Sun R K, et al. Fuzzy-logic-based energy optimized routing for wireless sensor networks. Int J Distrib Sens Netw, 2013, 9: 216561

    Google Scholar 

  20. 20

    Aoudia F A, Gautier M, Berder O. Fuzzy power management for energy harvesting wireless sensor nodes. In: Proceedings of International Conference on Communications, Kuala Lumpur, 2016

    Google Scholar 

  21. 21

    Yousaf R, Ahmad R, Ahmed W, et al. Fuzzy power allocation for opportunistic relay in energy harvesting wireless sensor networks. IEEE Access, 2017, 5: 17165–17176

    Google Scholar 

  22. 22

    Li S, Murch R D. An investigation into baseband techniques for single-channel full-duplex wireless communication systems. IEEE Trans Wirel Commun, 2014, 13: 4794–4806

    Article  Google Scholar 

  23. 23

    Wang D X, Zhang R Q, Cheng X, et al. Relay selection in two-way full-duplex energy-harvesting relay networks. In: Proceedings of IEEE Global Communications Conference (GLOBECOM), Washington, 2016

    Google Scholar 

  24. 24

    Novák V, Perfilieva I, Movckovr J. Mathematical Principles of Fuzzy Logic. Dodrecht: Kluwer Academic, 1999

    Google Scholar 

  25. 25

    Siddique N, Adeli H. Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing. Hoboken: Wiley, 2013

    Google Scholar 

  26. 26

    Mamdani E H, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud, 1975, 7: 1–13

    Article  Google Scholar 

  27. 27

    Kallenberg L. Handbook of Markov Decision Processes: Methods and Applications. Berlin: Springer, 2002

    Google Scholar 

  28. 28

    Mashrgy M A, Bdiri T, Bouguila N. Robust simultaneous positive data clustering and unsupervised feature selection using generalized inverted Dirichlet mixture models. Knowl-Based Syst, 2014, 59: 182–195

    Article  Google Scholar 

  29. 29

    Littman M L, Dean T L, Kaelbling L P, et al. On the complexity of solving Markov decision problems. In: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, Montr´eal, 1995

    Google Scholar 

  30. 30

    Suraweera H A, Smith P J, Shafi M. Capacity limits and performance analysis of cognitive radio with imperfect channel knowledge. IEEE Trans Veh Technol, 2010, 59: 1811–1822

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Waqas Ahmed.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Azfar, T., Ahmed, W., Haseeb, A. et al. A low complexity online controller using fuzzy logic in energy harvesting WSNs. Sci. China Inf. Sci. 62, 42305 (2019). https://doi.org/10.1007/s11432-018-9751-5

Download citation


  • wireless sensor networks
  • energy harvesting
  • cooperative communications
  • fuzzy logic