Self-energizing Wireless Sensor Network

  • Aditya SinghEmail author
  • Manisha J. Nene
Conference paper


The autonomous deployments using wireless sensor networks (WSNs) and their ability to self-organize play a vital role in data gathering in hostile environment or mission-critical applications. The contributions of this paper are threefold. First, the study in this paper proposes a preliminary model for peer-to-peer wireless power transfer (WPT) between sensor nodes, which is termed as self-energizing technique. Second, a fundamental design of a sensor node suitable for the self-energizing model is proposed, and third, using a clustering algorithm along with the flow mechanism to utilize the self-energizing technique is demonstrated. The study in this paper is a preliminary step toward proposing self-energizing technique between the peer sensor nodes of a deployed WSN. The paper concludes with the fact that the implications of self-energizing capabilities have the potential to enhance the fundamental deployment and design of such ad hoc networks.


Wireless Sensor network (WSN) Wireless power transfer (WPT) Clustering WSN recharging Self-Organization 


  1. 1.
    Rai, R., Rai, P.: Survey on energy-efficient routing protocols in wireless sensor networks using game theory. In: Sarma, H., Borah, S., Dutta, N. (eds.) Advances in Communication, Cloud, and Big Data. Lecture Notes in Networks and Systems, vol. 31. Springer, Singapore (2019)Google Scholar
  2. 2.
    Haque, M., Ahmad, T., Imran, M.: Review of hierarchical routing protocols for wireless sensor networks. In: Hu, Y.C., Tiwari, S., Mishra, K., Trivedi, M. (eds.) Intelligent Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol. 19, Springer, Singapore (2018)Google Scholar
  3. 3.
    Mazumdar, N., Roy, S., Nayak, S.: A survey on clustering approaches for wireless sensor networks. In: 2018 2nd International Conference on Data Science and Business Analytics IEEE (ICDSBA), Changsha, China, pp. 236–240 (2018)
  4. 4.
    Varghese, V., Nene, M.: Battlefield-of-Things and its implications in modern day battlefield, pp. 735–740. IEEE, ICCIC. (2017)
  5. 5.
    Misra, S., Kumar, R.: A literature survey on various clustering approaches in wireless sensor network. Int. Conf. Commun. Control. Intell. Syst. IEEE 18–22Google Scholar
  6. 6.
    Hu, J., Luo, J., Zheng, Y., Li, K.: Graphene-grid deployment in energy harvesting cooperative wireless sensor networks for green IoT. IEEE Trans. Ind. Inform. Scholar
  7. 7.
    Fan, Z., Jie, Z., Yujie, Q.: A multi-node rechargeable algorithm via wireless charging vehicle with optimal traveling path in wireless rechargeable sensor networks. In: 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN), Prague, 2018, pp. 531–536.
  8. 8.
    Wu, T., Yang, P., Dai, H., Li, P., Rao, X.: Near optimal bounded route association for drone-enabled rechargeable WSNs. Comput. Netw. 145:107–117 (2018). ISSN 1389-1286. Scholar
  9. 9.
    Hamrioui, S., Lorenz, P., Lloret, J.: Telecommun. Syst. 67(179) (2018). Springer US Scholar
  10. 10.
    Eledlebi, K., Ruta, D., Saffre, F., Al-Hammadi, Y., Isakovic, A.F.: A model for self-deployment of autonomous mobile sensor network in an unknown indoor environment. In: Zhou, Y., Kunz, T. (eds.) Ad Hoc Networks. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 223 Springer, Cham (2018)CrossRefGoogle Scholar
  11. 11.
    Cong, H., Li, Q., Zhou, C., Yang, Q.: Analysis on connectivity of energy harvesting wireless sensor networks based on simulation. In: 2018 International Conference on Computing, Networking and Communications (ICNC), Maui, HI, 2018, pp. 762–768.
  12. 12.
    Yu, W., Choi, J., Kim, Y., Lee, W., Kim, S.: Self-organizing localization with adaptive weights for wireless sensor networks. IEEE Sens. J. 18(20), 8484–8492 (2018).
  13. 13.
    Seo, S., Won, J., Sultana, S., Bertino, E.: Effective key management in dynamic wireless sensor networks. IEEE Trans. Inf. Forensics Secur. 10(2), 371–383 (2015). Scholar
  14. 14.
    Shehab, A., Elhoseny, M. Sahlol, A.T., Aziz, M.A.E.: Self-organizing single-hop wireless sensor network using a genetic algorithm: longer lifetimes and maximal throughputs. In: 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Srivilliputhur, 2017, pp. 1–6.
  15. 15.
    Desai, S.S., Nene, M.: DANES—Distributed Algorithm for Node Energy-Management for Self-organizing Wireless Sensor Networks, pp. 1296–1301 (2016)
  16. 16.
    Xie, L., et al.: Making sensor networks immortal: an energy-renewal approach with wireless power transfer. IEEE/ACM Trans. Net. 20(6), 1748–1761 (2012)CrossRefGoogle Scholar
  17. 17.
    Ajmal, T., Jazani, D., Allen, B.: Design of a compact RF energy harvester for wireless sensor networks. IET Conf. Wirel. Sens. Syst. (WSS), pp. 1–5. London, UK (2012)Google Scholar
  18. 18.
    Griffin, B., Detweiler, C.: Resonant wireless power transfer to ground sensors from a UAV. In: 2012 IEEE International Conference on Robotics and Automation (2012).
  19. 19.
    Wu, T., Yang, P., Dai, H., Li, P., Rao, X.: Near optimal bounded route association for drone-enabled rechargeable WSNs. Comput. Netw. 145, 107–117 (2018). ISSN 1389-1286 Scholar
  20. 20.
    Tesla, N.: The transmission of electrical energy without wires. Elect, World Engineer (1904)Google Scholar
  21. 21.
    Lu, X., Niyato, D., Wang, P.: Kim, D.I., Han, Z.: Wireless charger networking for mobile devices: fundamentals, standards, and applications. IEEE Wirel. Commun. 22(2), 126–135 (2015). Scholar
  22. 22.
    Mou, X., Sun, H.: Wireless power transfer: survey and roadmap. In: 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), Glasgow, 2015, pp. 1–5.
  23. 23.
    Ho, S.L., Wang, J., Fu, W.N., Sun, M.: A comparative study between novel witricity and traditional inductive magnetic coupling in wireless charging. IEEE Trans. Magn. 47(5), 1522–1525 (2011). Scholar
  24. 24.
    Dai, J., Ludois, D.C.: A survey of wireless power transfer and a critical comparison of inductive and capacitive coupling for small gap applications. IEEE Trans. Power Electron. 30(11), 6017–6029 (2015). Scholar
  25. 25.
    Zhang, Q., Fang, W., Liu, Q., Wu, J., Xia, P., Yang, L.: Distributed laser charging: a wireless power transfer approach. IEEE Internet Things J. 5(5), 3853–3864. Scholar
  26. 26.
    Liu, Q., Wu, J., Xia, P., Zhao, S., Chen, W., Yang, Y., Hanzo, L.: Charging unplugged: will distributed laser charging for mobile wireless power transfer work?” IEEE Veh. Technol. Mag. 11(4), 36–45 (2016)CrossRefGoogle Scholar
  27. 27.
    Nayak, P., Devulapalli, A.: A Fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sens. J. 16(1), 137–144 (2016). Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Defense Institute of Advanced TechnologyPuneIndia

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