Skip to main content

The Research of Reducing Energy Consumption Method on WSN Based on the Data Correlation

  • Conference paper
  • First Online:
Proceedings of the 6th International Asia Conference on Industrial Engineering and Management Innovation
  • 1237 Accesses

Abstract

Application of wireless sensor network is very broad. It usually works under mal-environment, so reducing energy consumption is very important in the aspect of network management because of limited energy supply. The wireless sensor network is usually divided into DWSN and CWSN. CWSN is evolved from DWSN. On the basis of the distributed sensor network structure, the nodes’ clustering is mainly according to the regional relationship, without considering the data relationship. But so many nodes in WSN make the time and spatial relationship existence between them. That is data correlation. According to DWSN structure, the nodes clustering are based on the data correlation in this paper. Some nodes hibernate by turns in the same cluster. This method can bring the redundant data uploading and the energy consumption down. The experiment proves that the energy consumption reduction is very obvious.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 189.00
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Intanagonwiwat C, Govindan R, Estrin D (2003) Directed diffusion for wireless sensor networking. IEEE/ACM Trans Netw 11(1):2–16

    Article  Google Scholar 

  2. Li JZ, Li JB (2003) Sensor networks and it concept, question and advance of data supervise. J Softw 14(10):1717–1726

    Google Scholar 

  3. Hartog AH (1983) A distributed temperature sensor based on liquid-core optical fibers. IEEE J Lightwave Technol 3(1):498–5096

    Article  ADS  Google Scholar 

  4. Hartog AH, Leach AP, Gold MP (1985) Distributed temperature sensing in solid-core fibers. Electron Lett 23(21):1061–1062

    Google Scholar 

  5. Perring A, Szewczyk R, wen V (2001) Secure protocols for sensor networks. In: Proceeding of the 7th annual ACM/IEEE international conference on mobile computing and networking (Mobi-Com), Rome, Italy, pp 189–199

    Google Scholar 

  6. Pi Y,Yang J (2007) Principle of synthetic aperture radar imaging. University of Electronic Science and Technology of China press, Chengdu, pp 110–120

    Google Scholar 

  7. Le C, Chan S (2004) Onboard FPGA-based SAR processing for future spaceborne systems. In: Proceedings of the IEEE radar conference, pp 15–20

    Google Scholar 

  8. Jain S, Shan RC (2006) Exploiting mobility for energy efficient data collection in wireless sensor networks. Mobile Netw Appl 11(3):27–39

    Google Scholar 

  9. Marta M, Cardei M (2008) Using sink mobility to increase wireless sensor networks lifetime. In: 2008 international symposium on a world of wireless, mobile and multimedia networks, pp 10–18

    Google Scholar 

  10. Buratti C,Verdone R (2008) A hybrid hierarchical architecture: from a wireless sensor network to the fixed infrastructure. In: 2008 European wireless conference (EW), pp 1–7

    Google Scholar 

  11. Chakrabarti A, Sabharwal A (2006) Communication power optimization in a sensor network with a path-constrained mobile observer. ACM Trans Sens Netw 2(3):297–324

    Article  Google Scholar 

  12. Xu J, Bi W, Zhu J, Zhao H (2011) Design & simulation of WSN equal-cluster-based multi-hop routing algorithm. J Syst Simul 23(5)

    Google Scholar 

  13. Ghidini G, Das SK (2011) An energy-efficient Markov chain-based randomized duty cycling scheme for wireless sensor networks. In: IEEE 31st international conference on distributed computing systems, pp 67–76

    Google Scholar 

  14. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422

    Article  Google Scholar 

  15. Zhu Y, Zhang J, Lifen, Peng W (2010) Multiple ant colony routing optimization based on cloud model for WSN with long-chain structure. In: International conference on wireless communications networking and mobile computing, pp 1–4

    Google Scholar 

Download references

Acknowledgments

Funds: The national natural science foundation, no. 11372197.

The scientific research project of Hebei province higher education, no. Z2012186.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Cui-jian Zhao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Atlantis Press and the author(s)

About this paper

Cite this paper

Sun, Sj., Zhao, Cj., Guo, P., Li, Xq. (2016). The Research of Reducing Energy Consumption Method on WSN Based on the Data Correlation. In: Qi, E. (eds) Proceedings of the 6th International Asia Conference on Industrial Engineering and Management Innovation. Atlantis Press, Paris. https://doi.org/10.2991/978-94-6239-145-1_1

Download citation

Publish with us

Policies and ethics