Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

An improved method for sink node deployment in wireless sensor network to big data

  • S.I. : AI-based Web Information Processing
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Wireless sensor network (WSNs) technology and Internet technology penetrate and extend each other. It is a good way for physical changes of objects, state recognition and data collection, and becomes an important source of network data in big data. Compared with traditional wireless networks, WSNs have the characteristics of integrating sensing, processing, and transmission, limited hardware resources, limited power supply capacity, no center, self-organization, multi-hop routing, dynamic topology, large number of nodes, and dense distribution. In order to improve the energy utilization rate of a single node to a greater extent, reduce the energy consumption of the entire WSNs, and extend the life cycle of WSNs, high-efficiency networking is essential in the application of WSNs. Networking is one of the foundations to the large-scale WSNs. The network model and node location deployment are important technologies for WSNs networking. Based on the network characteristics of large-scale WSNs and the transmission capacity of big data, a new type of network model suitable is presented which combined the advantages of Star model and Mesh model. More importantly, the deployment environment of sensor nodes is a spatial network. The data collected and transmitted by large-scale WSNs is very large. The deployment of sensor nodes in space can ensure that the big data collected and transmitted are true and effective. This research proposes the space density first (SDF) algorithm, which improves the neighbor density first algorithm with the space node deployment and the density-optimized SDF algorithm. The SDF algorithm saves network energy and extends the life of the network. Experimental results show that large-scale WSNs built with a new networking model and SDF algorithm can collect and transmit big data stably and reliably, which saves network energy and improves the accuracy of big data.

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Mohsen M, Fariza N, Gani A, Ahmad K, Targio HIA, Aisha S, Ibrar Y (2017) Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access 5:5247–5261

    Article  Google Scholar 

  2. Zhang Q, Yang LT, Chen Z, Li P (2018) A survey on deep learning for big data. Inf Fus 42:146–157

    Article  Google Scholar 

  3. Sabrina B, Eddine BD, Azeddine B, Homero T-C (2018) Big data challenges and data aggregation strategies in wireless sensor networks. IEEE Access 6:20558–20571

    Article  Google Scholar 

  4. Femminella M, Pergolesi M, Reali G (2018) IoT, big data, and cloud computing value chain: pricing issues and solutions. Ann Telecommun 73(7):511–520

    Article  Google Scholar 

  5. Wolfert S, Ge L, Verdouw C, Bogaardt M-J (2017) Big Data in Smart Farming – A review. Agric Syst 153:69–80

    Article  Google Scholar 

  6. Zhou L, Pan S, Wang J, Vasilakos AV (2017) Machine learning on Big Data: opportunities and challenges. Neurocomputing 237:350–361

    Article  Google Scholar 

  7. Shen L, Ma J, Liu X, Wei F, Miao M (2017) A secure and efficient ID-based aggregate signature scheme for wireless sensor networks. IEEE Internet Things J 4(2):546–554

    Article  Google Scholar 

  8. Cai H, Boyi Xu, Jiang L, Vasilakos AV (2017) IoT-based big data storage systems in cloud computing: perspectives and challenges 4(1):75–87

    Google Scholar 

  9. Yetgin H, Cheung KTK, El-Hajjar M, Hanzo L (2017) A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Commun Surv Tutor 19(2):828–854

    Article  Google Scholar 

  10. Hana R, Karim T, Ryma A, Damien S, Sihem G (2018) A multi-hop graph-based approach for an energy-efficient routing protocol in wireless sensor networks. HCIS 8:1–21

    Google Scholar 

  11. Xia H, Zhang R-H, Jia Yu, Pan Z-K (2016) Energy-efficient routing algorithm based on unequal clustering and connected graph in wireless sensor networks 23:141–150

    Google Scholar 

  12. Sun G, Liu Y, Liang S, Chen Z, Wang A, Qianao Ju, Zhang Y (2017) A sidelobe and energy optimization array node selection algorithm for collaborative beamforming in wireless sensor networks. IEEE Access 6:2515–2530

    Article  Google Scholar 

  13. Hamouda YEM, Msallam MM (2019) Smart heterogeneous precision agriculture using wireless sensor network based on extended Kalman filter. Neural Comput Appl 31(9):5653–5669

    Article  Google Scholar 

  14. Ang K-M, Seng JKP, Zungeru AM (2018) Optimizing energy consumption for big data collection in large-scale wireless sensor networks with mobile collectors. IEEE Syst J 12(1):616–626

    Article  Google Scholar 

  15. Ndiaye M, Hancke GP, Abu-Mahfouz AM (2017) Software defined networking for improved wireless sensor network management: a survey. Sensors 17(5):1–32

    Article  Google Scholar 

  16. Muhammad A, Shafiullah K, Rashid A, Muhammad S, Dhananjay S (2017) Quality of service of routing protocols in wireless sensor networks: a review. IEEE Access 5:1846–1871

    Article  Google Scholar 

  17. Kuo Y-W, Li C-L, Jhang J-H, Lin S (2018) Design of a wireless sensor network based IoT platform for wide area and heterogeneous applications. IEEE Sens J 18(12):5187–5197

    Article  Google Scholar 

  18. Jin Wang Yu, Gao WL, Sangaiah AK, Kim H-J (2019) An improved routing schema with special clustering using PSO algorithm for heterogeneous wireless sensor network. Sensors 19(3):1–17

    Article  Google Scholar 

  19. Maurya S, Jain VK, Chowdhury DR (2019) Delay aware energy efficient reliable routing for data transmission in heterogeneous mobile sink wireless sensor network. J Netw Comput Appl 144:118–137

    Article  Google Scholar 

  20. Verma S, Sood N, Sharma AK (2019) A novelistic approach for energy efficient routing using single and multiple data sinks in heterogeneous wireless sensor network. Netw Appl 12:1110–1136

    Google Scholar 

  21. Verma S, Sood N, Sharma AK (2019) Genetic Algorithm-based Optimized Cluster Head selection for single and multiple data sinks in Heterogeneous Wireless Sensor Network. Appl Soft Comput J 85:1–21

    Article  Google Scholar 

  22. Kaushal K, Kaur T, Kaur J (2014) ZigBee based wireless sensor networks. Int J Comput Sci Inf Technol 5(6):7752–7755

    Google Scholar 

  23. Jiasong Mu (2017) An improved AODV routing for the zigbee heterogeneous networks in 5G environment. Ad Hoc Netw 58:13–24

    Article  Google Scholar 

  24. Huang Y, Pang A, Hsiu P, Zhuang W, Liu P (2012) Distributed throughput optimization for ZigBee cluster-tree networks. IEEE Trans Parallel Distrib Syst 23(3):513–520

    Article  Google Scholar 

  25. Yang F, Chen Y (2015) Heterogeneous networking technology research on wireless sensor networks in Internet of Things. J Hunan Univ Sci Technol 30(1):86–92

    Google Scholar 

  26. Castagnetti A, Pegatoquet A, Le TN, Auguin M (2014) A joint duty-cycle and transmission power management for energy harvesting WSN. IEEE Trans Ind Inf 10(2):928–936

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Innovative Province Construction Special Project of Hunan, China (Grant No. 2020NK2033), by the Scientific Research Fund of Hunan Provincial Education Department, China (Grant No. 20A249 and Grant No. 20A259), and in part by the Scientific Research and Technology Development Project of Hezhou, China (Grant No. HeGongJian1908016). (Corresponding authors: Xinghui Zhu; Kui Fang.)

Author information

Authors and Affiliations

  1. The College of Information and Intelligence, Hunan Agricultural University, Changsha, 410128, China

    Yineng Chen, Xinghui Zhu, Kui Fang, Xiaoxuan Chen, Cheng Li, Qingshan Ren & Zhuoyang Zou

  2. The College of Food and Bioengineering, Hezhou University, Hezhou, 542899, China

    Ting Guo

Authors
  1. Yineng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinghui Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kui Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoxuan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ting Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Cheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qingshan Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zhuoyang Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xinghui Zhu or Kui Fang.

Ethics declarations

Conflict of interest

All authors of this manuscript have no conflict of interest, financial or otherwise. And there are no other relationships or activities that could appear to have influenced the submitted work.

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

Chen, Y., Zhu, X., Fang, K. et al. An improved method for sink node deployment in wireless sensor network to big data. Neural Comput & Applic 34, 9499–9510 (2022). https://doi.org/10.1007/s00521-021-06443-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06443-3

Keywords

Search

Navigation