Skip to main content
Springer Nature Link
Log in

Improving Life Cycle of the Underwater Wireless Sensor Network

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

Aiming at the complexity of the underwater wireless sensor network and the characteristics of high temporal correlation of the data that continuously sensed by nodes, an optimization method of underwater data prediction based on exponential smoothing (ESDP) is proposed to improve the life cycle of the whole network. In this method, LEACH protocol is used to cluster the whole nodes and elect some cluster heads. Then a routing tree is constructed between the cluster heads to communicate with the water surface sink node. Finally, the optimized exponential smoothing method is used to establish the prediction model to predict the current data. Considering the CPU and RAM limitations of underwater nodes, the model adopts the difference transmission method, which reduces the calculation of the cluster heads, the communication overhead and the size of data packet. The simulation results show that ESDP can better balance the data prediction accuracy and energy consumption, and improve the life cycle of the whole network. Therefore, ESDP method can be better applied to underwater environments.

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.

Similar content being viewed by others

REFERENCES

  1. Raza, U., Camerra, A., Murphy, A.L., Palpanas, T., and Picco, G.P., Practical data prediction for real-world wireless sensor networks, IEEE Trans. Knowl. Data Eng., 2015, vol. 27, no. 8, pp. 2231–2244.

    Article  Google Scholar 

  2. Li Jixin, Zhang Yayun, Huang Fuchuan, et al., Data prediction algorithm based on Markov chain in wireless sensor network, Minicomput. Syst., 2016, vol. 37, no. 11, pp. 2498–2503.

    Google Scholar 

  3. Zhang Kai, Data Fusion and Processing Technology of Wireless Sensor Network Based on Privacy Protection, Nanjing Univ. Posts Telecommun., 2018.

  4. Chang Tieyuan, Liu Weina, Zhang Yan, and Li Huiya, LEACH protocol based on cluster head distance and energy optimization, J. Hebei Univ. (Nat. Sci. Ed.), 2019, vol. 39, no. 02, pp. 194–200.

  5. Fang Wei, Event Recognition and Evolution of Underwater Sensor Network Based on Data Prediction, Beijing: China Univ. Geosci., 2018.

    Google Scholar 

  6. Wei, G., Ling, Y., Guo, B., et al., Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter, Comput. Commun., 2011, vol. 34, no. 6, pp. 793–802.

    Article  Google Scholar 

  7. Wang Lingjiao, Peng Zhiqiang, Li Zhetao, Guo Hua, and Zhong Yiqun, Optimization of cluster head selection strategy for NPCHs-LEACH protocol based on weight, J. Sens. Technol., 2015, vol. 28, no. 12, pp. 1846–1852.

    Google Scholar 

  8. Liang Wenhui, Dong Qiang, He Ming, Chen Qiuli, and Ding Chenlu, AUVs based healing algorithm for underwater mobile wireless sensor networks, Comput. Eng. Appl., 2015, vol. 51, no. 19, pp. 98–102.

    Google Scholar 

  9. He Ming, Liang Wenhui, Chen Qiuli, Chen Xiliang, and Chen Jian, Topology optimization of underwater mobile wireless sensor network based on topology reconstruction, J. Commun., 2015, vol. 36, no. 06, pp. 82–91.

    Google Scholar 

  10. He Ming, Liang Wenhui, Chen Guohua, and Chen Qiuli, Underwater mobile wireless sensor network topology, Control Decis. Making, 2013, vol. 28, no. 12, pp. 1761–1770.

    Google Scholar 

  11. He Ming, Liang Wenhui, Chen Qiuli, Chen Xiliang, and Wang Lihui, Topology healing and optimization of underwater mobile wireless sensor networks, Control Decis. Making, 2015, vol. 30, no. 02, pp. 251–255.

    Google Scholar 

  12. Diao Pengfei and Wang Yanjiao, Coverage keeping clustering algorithm for underwater wireless sensor networks based on node sleep, J. Electron. Inf., 2018, vol. 40, no. 05, pp. 1101–1107.

    Google Scholar 

  13. Cao Cece, Ji Qianqian, Song Jiao, Zhang Shujing, and Chen Jiaxing, Security research in underwater wireless sensor networks, Inf. Secur. Res., 2018, vol. 4, no. 03, pp. 270–275.

    Google Scholar 

  14. Hung, C.C., Energy-aware set-covering approaches for approximate data collection in wireless sensor networks, IEEE Trans. Knowl. Data Eng., 2012, vol. 24, no. 11, pp. 1993–2007.

    Article  Google Scholar 

  15. Amara, U., Nadeem, J., Ashfaq, A., et al., DEADS: Depth and energy aware dominating set based algorithm for cooperative routing along with sink mobility in underwater WSNs, Sensors, 2015, vol. 15, no. 6, pp. 14458–14486.

    Article  Google Scholar 

  16. Han, G., Zhang, C., Shu, L., et al., Impacts of deployment strategies on localization performance in underwater acoustic sensor networks, IEEE Trans. Ind. Electron., 2015, vol. 62, no. 3, pp. 1725–1733.

    Article  Google Scholar 

  17. Lu Zhao, Research on Data Prediction Technology Based on Spatiotemporal Correlation in Wireless Sensor Networks, Hebei Univ. Eng., 2016.

  18. Zhou, Z., Fang, W., Niu, J., et al., Energy-efficient event determination in underwater WSNs leveraging practical data prediction, IEEE Trans. Ind. Inf., 2017, vol. 13, no. 3, pp. 1238–1248.

    Article  Google Scholar 

  19. Basagni, S., Boloni, L., Gjanci, P., et al., Maximizing the value of sensed information in underwater wireless sensor networks via an autonomous underwater vehicle, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications, 2014.

  20. Palpanas, T., Real-time data analytics in sensor networks, in Managing and Mining Sensor Data, Aggarwal, C., Ed., New York: Springer, 2012.

    MATH  Google Scholar 

  21. Li, N., Martinez, J.-F., Chaus, J.M.M., and Eckert, M., A survey on underwater acoustic sensor network routing protocols, Sensors, 2016, vol. 16, no. 3, pp. 1–28.

    Article  Google Scholar 

  22. Ayaz, M., Baig, I., Abdullah, A., and Faye, I., A survey on routing techniques in underwater wireless sensor networks, J. Network Comput. Appl., 2011, vol. 34, pp. 1908–1927.

    Article  Google Scholar 

  23. Derr, K. and Manic, M., Wireless sensor networks—node localization for various industry problems, IEEE Trans. Ind. Inf., 2015, vol. 11, no. 3, pp. 752–762.

    Article  Google Scholar 

  24. Seokhoon Yoon, Abul K. Azad, Hoon Oh, et al., AURP: An AUV-aided underwater routing protocol for underwater acoustic sensor networks, Sensors, 2012, vol. 12, pp. 1827–1845.

    Article  Google Scholar 

  25. Song Xiaoxiang, Guo Yan, Li Ning, et al., Prediction algorithm of time series missing data based on compressed perception, Comput. Sci., 2019, vol. 46, no. 7, pp. 217–223.

    Google Scholar 

Download references

Funding

This paper is supported by the project of Innovation Talent Support Plan of Liaoning Province (LR2018057); Science and Technology Activity Support Project for Candidates of “Talents Project” in Liaoning Province (Liaorenshe [2018] no. 45); the Natural Foundation of Liaoning Province (2019-ZD-0068); Project of Liaoning Provincial Department of Education (XXLJ2019010).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Shenyang University of Chemical Technology, 110142, Tiexi, Shenyang, P.R. China

    Jun Wang & Ni Wang

Authors
  1. Jun Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ni Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding authors

Correspondence to Jun Wang or Ni Wang.

Ethics declarations

The authors declare no conflict of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, J., Wang, N. Improving Life Cycle of the Underwater Wireless Sensor Network. Aut. Control Comp. Sci. 55, 277–286 (2021). https://doi.org/10.3103/S0146411621030068

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411621030068

Keywords: