[go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A Novel Routing Algorithm with Bernoulli Sampling-based Link Quality Estimation in Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Link quality is important and can greatly affect the performance of wireless transmission algorithms and protocols. Currently, researchers have proposed a variety of approaches to implement link quality estimation. However, the estimated result of link quality is not accurate enough and the error is large, so they may lead to the failure of routing algorithm and protocol. In this paper, a novel method is proposed to achieve the more accurate estimation of link quality than before. This method employs Bernoulli sampling-based algorithm to complete the estimation of link quality. The problem is modeled as calculation of estimators based on Bernoulli sampling data. The authors further prove that the calculation results are accurate by probability theory. Furthermore, according to link quality estimation, the authors also provide a centralized routing algorithm and a distributed improvement algorithm in order to switch the data transmission on the better quality link. Finally, the extensive experiment results indicate that the proposed methods obtain high performance in terms of energy consumption and accuracy.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Wang, S., Kim, S. M., Liu, Y., Tan, G., & He, T. (2013). CorLayer: A transparent link correlation layer for energy efficient broadcast. In Proccedings of ACM MobiCom.

  2. Srinivasan, K., Jain, M., Choi, J. I., Azim, T., Kim, E. S., Levis, P., & Krishnamachari, B. (2010). The К-factor: Inferring protocol performance using inter-link reception correlation. In Proccedings MobiCom (pp. 317–328).

  3. Zhu, T., Zhong, Z., He, T., & Zhang, Z. L. (2010). Exploring link correlation for efficient flooding in wireless sensor networks. In Proceedings NSDI.

  4. Thangaramya, K., Kulothungan, K., Logambigai, R., et al. (2019). Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Computer Networks, 151(14), 211–223.

    Article  Google Scholar 

  5. Yue, G., Yang, K., Zhao, S., & Poor, H. V. (2018). Design of network coding for wireless broadcast and multicast with optimal decoders. IEEE Transactions on Wireless Communications, 17(10), 6944–6957.

    Article  Google Scholar 

  6. Wu, J., & Lou, W. (2006). Extended multipoint relays to determine connected dominating sets inMANETs. IEEE Transactions on Computers, 55(3), 334–347.

    Article  Google Scholar 

  7. Liu, C. (2016). Performance-guaranteed strongly connected dominating sets in heterogeneous wireless sensor networks. In Proccedings IEEE INFOCOM (pp. 1–9).

  8. Zhao, Z., Dong, W., & Guan, G. (2015). Modeling link correlation in low-power wireless networks. In Proccedings of IEEE INFOCOM.

  9. Zhu, T., Zhong, Z., & Zhang, Z.-L. (2013). Achieving efficient flooding by utilizing link correlation in wireless sensor networks. IEEE/ACM Translation on Networks, 21(1), 121–134.

    Article  Google Scholar 

  10. Alam, S. I., Sultana, S., Hu, Y. C., & Fahmy, S. (2013). SYREN: Synergistic link correlation-aware and network coding-based dissemination in wireless sensor networks. In Proccedings of MASCOTS.

  11. Zhao, Z., Dong, W., Bu, J., Gu, T., & Chen, C. (2014). Exploiting link correlation for core-based dissemination in wireless sensor networks. In Proccedings of IEEE SECON.

  12. Wang, S., Basalamah, A., Kim, S., Guo, S., Tode, Y., & He, T. (2014). Link correlation aware opportunistic routing in wireless networks. IEEE Transactions on Wireless Communications, 14(99), 1–1.

    Google Scholar 

  13. Cheng, S. Y., & Li, J. Z. (2009). Sample based (ε, δ)-approximate aggregation in sensor networks. In Proccedings IEEE 29th int’l conference distributed computing systems (ICDCS) (pp. 273–280).

  14. Li, J., & Cheng, S. (2012). (ε, δ)-approximate aggregation algorithmsin dynamic sensor networks. IEEE Transactions Parallel and Distributed Systems, 23(3), 385–396.

    Article  Google Scholar 

  15. Wu, J., Lou, W., & Dai, F. (2006). Extended multipoint relays to determineconnected dominating sets in MANETs. IEEE Transactions on Computers, 55(3), 334–347.

    Article  Google Scholar 

  16. Guo, S., Kim, S. M., Zhu, T., Gu, Y., & He, T. (2011). Correlated flooding in low-duty-cycle wireless sensor networks. In Proccedings ICNP (pp. 383-392).

  17. Zhong, Z., Zhu, T., Wang, D., & He, T. (2009). Tracking with unreliable node sequence. In Proccedings IEEE INFOCOM pp. (1215–1233).

  18. Ben-El-Kezadri, R., Pau, G., & Claveirole, T. (2011). TurboSync: Clock synchronization for shared media networks via principal component analysis with missing data. In INFOCOM (pp. 1170–1178).

  19. Tile, Y. (2006). Sampling algorithms. New York: Springer.

    Google Scholar 

  20. Lind, D. A., Marchal, W. G., & Wathen, S. A. (2011). Basic statistics for business and economics. Irwin: McGraw-Hill.

    Google Scholar 

  21. Bernstein, S., & Bernstein, R. (2004). Elements of statistics II: inferential statistics. New York: McGraw-Hill.

    Google Scholar 

  22. Cormen, T. H., Leiserson, C. E., Rivest, R. L., Stein, C. (2001). Section 24.3: Dijkstra's algorithm. Introduction to algorithms. MIT Press (pp: 595–601).

  23. Knuth, D. E. (1977). A generalization of Dijkstra’s algorithm. Information Processing Letters, 6(1), 1–5.

    Article  MathSciNet  Google Scholar 

  24. Thorup, M. (1999). Undirected single-source shortest paths with positive integer weights in linear time. Journal of the ACM, 46(3), 362–394.

    Article  MathSciNet  Google Scholar 

  25. Xiaoyong Yan, J., Cao, L., Sun, J., Zhou, S. W., & Song, A. (2020). Accurate analytical-based multi-hop localization with low energy consumption for irregular networks. IEEE Transactions on Vehicular Technology, 69(2), 2021–2033.

    Article  Google Scholar 

  26. Xiaoyong Yan, L., Sun, J. Z., & Song, A. (2018). DV-hop localisation algorithm based on optimal weighted least square in irregular areas. Electronics Letters, 54(21), 1243–1245.

    Article  Google Scholar 

  27. Xiaoyong Yan, L., Sun, Z., Sun, J. Z., & Song, A. (2019). Improved hop-based localisation algorithm for irregular networks. IET Communications, 13(5), 520–527.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Modern Posts, Nanjing University of Posts and Telecommunications Nanjing, Jiangsu, 210003, China

    Chao Meng

Authors
  1. Chao Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chao Meng.

Ethics declarations

Conflict of interest

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meng, C. A Novel Routing Algorithm with Bernoulli Sampling-based Link Quality Estimation in Wireless Sensor Networks. Wireless Pers Commun 126, 2753–2779 (2022). https://doi.org/10.1007/s11277-022-09840-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09840-6

Keywords