Skip to main content

Advertisement

SpringerLink
Log in

Calculation and simulation of transient optimal voltage output point in wireless sensor networks

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

For production of transient voltage output in wireless sensor network, due to the instability of the control voltage, the traditional control method needs to introduce the feedback control, through the establishment of mathematical model for periodic monitoring of the output voltage, change control strategy once the problem is found, lead to slow control time computation, cannot obtain the optimal output voltage values. A new method for calculating transient optimal voltage output points in sensor network of two-phase rotating and stationary coordinates is proposed. Through the analysis of the mathematical model of the network transient, the repetitive controller is designed on the two axes. By calculating the mean value of maximum and minimum value of the voltage, the transient energy-saving voltage output of the sensor network is monitored in real time. The control process of output voltage quality of sensor network with nonlinear load is analyzed, and the feed forward control method of parameter on line control is put forward. Under the premise to meet the control accuracy, enhance the response speed and stability of voltage output control of sensor networks. Simulation results showed that the method presented in this paper improves the poor effect of traditional methods, solves the complex problems of network energy-saving voltage control, and control effect of energy-saving voltage output is great, and the optimal voltage output values is obtained.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Ren Z, Peng S, Chen H, Fang J, Chen Q (2013) Epidemic routing based on adaptive compression of vectors: efficient low-delay routing for opportunistic networks based on adaptive compression of vectors. Int J Commun Syst 28:560–573

    Article  Google Scholar 

  2. Hiertz GR, Denteneer D, Max S, Taori R, Cardona J, Berlemann L, Walke B (2010) IEEE 802.11s: the WLAN mesh standard. Wirel Commun IEEE 17(1):104–111

    Article  Google Scholar 

  3. Hao J (2015) Image processing and transmission scheme based on generalized Gaussian mixture with opportunistic networking for wireless sensor networks. HCIS 5:226–228

    Google Scholar 

  4. Manhas Jr EB, Brante G, Souza RD, Pellenz ME (2012) Energy-efficient cooperative image transmission over wireless sensor networks. In: Proceedings of 2012 IEEE wireless communications and networking conference (WCNC), pp 2014–2019

  5. Ngau CWH, Ang L-M, Seng KP (2012) Low memory visual saliency architecture for data reduction in wireless sensor networks. IET Wirel Sensor Syst 2(2):115–127

    Article  Google Scholar 

  6. Lecuire V, Duran-Faundez C, Krommenacker N (2008) Energy-efficient image transmission in sensor networks. Int J Sensor Netw 4(1–2):37–47

    Article  Google Scholar 

  7. Muhammad Imran Razzak SA, Hussain AA, Minhas MS (2010) Collaborative image compression in wireless sensor networks. Int J Comput Cogn 8(1):24–29

    Google Scholar 

  8. Lu Q, Luo W, Wang J, Chen B (2008) Low-complexity and energy efficient image compression scheme for wireless sensor networks. Comput Netw 52(13):2594–2603

    Article  MATH  Google Scholar 

  9. Carrano RC, Magalhaes LCS, Saade DCM, Albuquerque CVN (2011) IEEE 802.11s Multihop MAC: a tutorial. Commun Surv Tutor IEEE 13(1):52–67

    Article  Google Scholar 

  10. Liu X, Huang L, Li J, Wang Z, Zhao D, Zhu M (2012) Image transmission over ZigBee-based wireless sensor networks. Sensors Lett 10(1–2):205–212(8)

    Article  Google Scholar 

  11. Pham DM, Aziz SM (2013) Object extraction scheme and protocol for energy efficient image communication over wireless sensor networks. Comput Netw 57(15):2949–2960

    Article  Google Scholar 

  12. Aziz SM, Pham DM (2013) Energy efficient image transmission in wireless multimedia sensor networks. IEEE Commun Lett 17(6):1084–1087

    Article  Google Scholar 

  13. Viegas CMD, Sampaio S, Vasques F, Portugal P, Souto P (2012) 9th IEEE international workshop on factory communication systems (WFCS). Assessment of the interference caused by uncontrolled traffic sources upon real-time communication in IEEE 802.11-based mesh networks, pp 59–62

  14. Irgan K, Ünsalan C, Baydere S (2014) Low-cost prioritization of image blocks in wireless sensor networks for border surveillance. J Netw Comput Appl 38:54–64

    Article  MATH  Google Scholar 

  15. Nikolakopoulos G, Kandris D, Tzes A (2010) Adaptive compression of slowly varying images transmitted over wireless sensor networks. Sensors 10(8):7170–7191

    Article  Google Scholar 

  16. Liu L, Song Y, Zhang H, Ma H, Vasilakos AV (2015) Physarum optimization: a biology-inspired algorithm for the Steiner tree problem in networks. IEEE Trans Comput 64(3):818–831

    Article  MathSciNet  Google Scholar 

  17. Kaddachi ML, Soudani A, Lecuire V, Torki K, Makkaoui L, Moureaux JM (2012) Low power hardware-based image compression solution for wireless camera sensor networks. Comp. 10.1186/s13638-015-0458-3 Moureaux Low power hardware-based image compression solution for wireless camera sensor networks. Comp Stand Interfaces 34(1):14–23

  18. Zhang XM, Zhang Y, Yan F, Vasilakos AV (2015) Interference-based topology control algorithm for delay-constrained mobile ad hoc networks. IEEE Trans Mob Comput 14(4):742–754

    Article  Google Scholar 

  19. Duarte PBF, Fadlullah ZM, Vasilakos AV, Kato N (2012) On the partially overlapped channel assignment on wireless mesh network backbone: a game theoretic approach. IEEE J Sel Areas Commun 30(1):119–127

    Article  Google Scholar 

  20. Imran N, Seet BC, Fong ACM (2015) Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures. HCIS 4:513–515

    Google Scholar 

  21. Elamin A, Jeoti V, Belhouari S (2012) Wireless video sensor networks: advances in distributed video coding. In: Ang L, Seng KP (eds) Visual information processing in wireless sensor networks: technology, trends and applications. IGI Global, Hershey, pp 40–58

    Chapter  Google Scholar 

  22. Imran N, Seet BC, Fong ACM (2012) A comparative analysis of video codecs for multihop wireless video sensor networks. Multimed Syst 18(5):373–389

    Article  Google Scholar 

  23. Liang CJM (2011) Interference characterization and mitigation in large-scale wireless sensor networks. Dissertation, John Hopkins University,

  24. Magaiaa N, Hortab N, Nevesb R, Pereira PR, Correiaa M (2015) A multi-objective routing algorithm for wireless multimedia sensor networks. Appl Soft Comput 30:104–112

    Article  Google Scholar 

  25. Liu S, Cheng X, Fu W et al (2014) Numeric characteristics of generalized M-set with its asymptote. Appl Math Comput 243:767–774

    MathSciNet  MATH  Google Scholar 

  26. Liu S, Fu W, He L et al (2015) Distribution of primary additional errors in fractal encoding method. Multimed Tools Appl. doi:10.1007/s11042-014-2408-1

  27. Liu S, Zhang Z, Qi L et al (2015) A fractal image encoding method based on statistical loss used in agricultural image compression. Multimed Tools Appl. doi:10.1007/s11042-014-2446-8

Download references

Acknowledgments

The national natural science foundation of China (61472268); Suzhou science and technology support plan (SS201336)

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Suzhou Vocational University, 215104, Suzhou, Jiangsu, China

    Chengxi Gu, G. U. Caidong & Fan Ling

Authors
  1. Chengxi Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. U. Caidong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fan Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chengxi Gu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gu, C., Caidong, G.U. & Ling, F. Calculation and simulation of transient optimal voltage output point in wireless sensor networks. J Supercomput 72, 2767–2781 (2016). https://doi.org/10.1007/s11227-015-1605-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-015-1605-7

Keywords

Search

Navigation