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Research on suboptimal energy balance of non-uniform distributed nodes in WSN

  • Ruiying Wang
  • Guoping He
  • Xiaoming Wu
  • Fuqiang Wang
  • Yifan Hu
Article
  • 20 Downloads

Abstract

Wireless sensor networks (WSNs) are widely used in industrial production, environmental monitoring, and military applications. In the process of using, the node non-uniform distribution strategy can mitigate the energy hole and node suboptimal energy balance technology in wireless sensor networks. This paper discusses this strategy theoretically, proposes a node non-uniform distribution strategy, and it constructs a suboptimal energy balance algorithm, which based on the non-uniform distribution theory system. It has proved that in the circular network with non-uniform distribution of nodes, the uniform distributed method and the random non-uniform distributed method are tested and compared. The experimental results show that the non-uniform distributed method has high efficiency and good scalability, and it can be used to achieve the suboptimal energy balance. The simulation results also show that the nodes in the WSN are almost equal to the energy consumption.

Keywords

Uneven distributed nodes Suboptimal network Energy consumption equalization 

Notes

Acknowledgements

The work is funded by the National Natural Science Foundation of China (Grant: 61501282), Shandong Provincial Natural Science Foundation, China (No. ZR2018MF003).

References

  1. 1.
    Yildiz HU, Bicakci K, Tavli B, Gultekin H, Incebacak D (2016) Maximizing wireless sensor network lifetime by communication/computation energy optimization of non-repudiation security service: node level versus network level strategies. Ad Hoc Netw 37:301–323CrossRefGoogle Scholar
  2. 2.
    Sharma R, Lobiyal DK (2015) Energy holes avoiding techniques in sensor networks: a survey. Int J Eng Trends Technol 20(4):204–208CrossRefGoogle Scholar
  3. 3.
    Cayirpunar O, Kadioglu-Urtis E, Tavli B (2015) Optimal base station mobility patterns for wireless sensor network lifetime maximization. IEEE Sens J 15(11):6592–6603CrossRefGoogle Scholar
  4. 4.
    Shu Y, Yousefi H, Cheng P, Chen J, Gu YJ, He T, Shin KG (2016) Near-optimal velocity control for mobile charging in wireless rechargeable sensor networks. IEEE Trans Mob Comput 15(7):1699–1713CrossRefGoogle Scholar
  5. 5.
    Rani S, Malhotra J, Talwar R (2015) Energy efficient chain based cooperative routing protocol for WSN. Appl Soft Comput 35:386–397CrossRefGoogle Scholar
  6. 6.
    Cayirpunar O, Tavli B, Kadioglu-Urtis E, Uludag S (2017) Optimal mobility patterns of multiple base stations for wireless sensor network lifetime maximization. IEEE Sens J 17(21):7177–7188CrossRefGoogle Scholar
  7. 7.
    Jan B, Farman H, Javed H, Montrucchio B, Khan M, Ali S (2017) Energy efficient hierarchical clustering approaches in wireless sensor networks: a survey. Wirel Commun Mob Comput 2017(1):1–14CrossRefGoogle Scholar
  8. 8.
    Ku ML, Li W, Chen Y, Liu KR (2016) Advances in energy harvesting communications: past, present, and future challenges. IEEE Commun Surv Tutor 18(2):1384–1412CrossRefGoogle Scholar
  9. 9.
    Jia J, Chen J, Deng Y, Wang X, Aghvami AH (2017) Joint power charging and routing in wireless rechargeable sensor networks. Sensors 17(10):2290CrossRefGoogle Scholar
  10. 10.
    Mohamed RE, Ghanem WR, Khalil AT, Elhoseny M, Sajjad M, Mohamed MA (2018) energy efficient collaborative proactive routing protocol for wireless sensor network. Comput Netw 34(142):154–167CrossRefGoogle Scholar
  11. 11.
    Yildiz HU, Tavli B, Yanikomeroglu H (2016) Transmission power control for link-level handshaking in wireless sensor networks. IEEE Sens J 16(2):561–576CrossRefGoogle Scholar
  12. 12.
    Li J, Mohapatra P (2005) An analytical model for the energy hole in many-to-one sensor networks. In: Proceedings of IEEE Vehicular Technology Conference, Dallas, TX, 2005, pp 2721–2725Google Scholar
  13. 13.
    Lian J, Chen L, Naik K, Otzu T, Agnew G (2004) Modeling and enhancing the data capacity of wireless sensor networks. In: Phoha S, La Porta TF, Griffin C (eds) IEEE monograph on sensor network operations, IEEE Press, vol 13, issue 7, pp 376–379Google Scholar
  14. 14.
    Olariu S, Stojmenovic I (2006) Design guidelines for maximizing lifetime and avoiding energy holes in sensor networks with uniform distribution and uniform reporting. In: Proceedings of IEEE INFOCOM, Barcelona, Spain, 2006, pp 1–12Google Scholar
  15. 15.
    Lian J, Naik K, Agnew G (2006) Data capacity improvement of wireless sensor networks using non-uniform sensor distribution. Int J Distrib Sens Netw 2(2):121–145CrossRefGoogle Scholar
  16. 16.
    Shan-shan M, Jian-sheng Q (2014) Energy balanced non-uniform distribution node scheduling algorithm for wireless sensor networks. Appl Math Inf Sci 8(4):1997CrossRefGoogle Scholar
  17. 17.
    Gherbi C, Aliouat Z, Benmohammed M (2017) A survey on clustering routing protocols in wireless sensor networks. Sens Rev 37(1):12–25CrossRefGoogle Scholar
  18. 18.
    Liu H, Zhang Y, Liu H, Su X (2015) Inhomogeneous distribution strategy based on mobile sink nodes in wireless sensor networks. Wirel Pers Commun 83(1):411–426CrossRefGoogle Scholar
  19. 19.
    Keskin ME, Altınel IK, Aras N, Ersoy C (2016) Wireless sensor network design by lifetime maximisation: an empirical evaluation of integrating major design issues and sink mobility. Int J Sens Netw 20(3):131–146CrossRefGoogle Scholar
  20. 20.
    De D, Das SK (2015) SREE-tree: self-reorganizing energy-efficient tree topology management in sensor networks. In: 2015 Sustainable Internet and ICT for Sustainability, SustainIT 2015, Madrid, Spain, April 14–15, 2015, vol 34. IEEE, pp 1–8Google Scholar
  21. 21.
    Raval G, Bhavsar M, Patel N (2017) Enhancing data delivery with density controlled clustering in wireless sensor networks. Microsyst Technol 23(3):613–631CrossRefGoogle Scholar
  22. 22.
    Essa A, Al-Dubai AY, Romdhani I, Eshaftri MA (2017) A new dynamic weight-based energy efficient algorithm for sensor networks. In: Hu J, Leung V, Yang K, Zhang Y, Gao J, Yang S (eds) Smart grid inspired future technologies. Lecture notes of the institute for computer sciences, Social informatics and telecommunications engineering, vol 175. Springer, ChamGoogle Scholar
  23. 23.
    Sah DK, Amgoth T (2018) Parametric survey on cross-layer designs for wireless sensor networks. Comput Sci Rev 27:112–134MathSciNetCrossRefGoogle Scholar
  24. 24.
    Chiti F, Fantacci R, Mastandrea R, Rigazzi G, Sarmiento ÁS, López EMM (2015) A distributed clustering scheme with self nomination: proposal and application to critical monitoring. Wirel Netw 21(1):329–345CrossRefGoogle Scholar
  25. 25.
    Zhao Q, Nakamoto Y (2016) Topology management for reducing energy consumption and tolerating failures in wireless sensor networks. Int J Netw Comput 6(1):107–123CrossRefGoogle Scholar
  26. 26.
    Batra PK, Kant K (2018) An energy-aware clustering algorithm for wireless sensor networks: GA-based approach. Int J Auton Adapt Commun Syst 11(3):275–292CrossRefGoogle Scholar
  27. 27.
    Wang J, Jiang C, Han Z, Ren Y, Hanzo L (2016) Network association strategies for an energy harvesting aided super-WiFi network relying on measured solar activity. IEEE J Sel Areas Commun 34(12):3785–3797CrossRefGoogle Scholar
  28. 28.
    Płaczek B, Bernas M (2017) Self-organizing mobility control in wireless sensor and actor networks based on virtual electrostatic interactions. Wirel Pers Commun 96(4):5083–5103CrossRefGoogle Scholar
  29. 29.
    Dai L, Wang B, Yuan Y, Han S, Chih-Lin I, Wang Z (2015) Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends. IEEE Commun Mag 53(9):74–81CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ruiying Wang
    • 1
  • Guoping He
    • 1
    • 2
  • Xiaoming Wu
    • 2
  • Fuqiang Wang
    • 2
  • Yifan Hu
    • 3
    • 4
  1. 1.College of Mathematics and Systems ScienceShandong University of Science and TechnologyQingdaoChina
  2. 2.Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)JinanChina
  3. 3.Institute of Oceanographic InstrumentationQilu University of Technology (Shandong Academy of Sciences)JinanChina
  4. 4.Joint China-Ukrainian Scientific and Innovation Laboratory for HydroacousticsJinanChina

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