Skip to main content

Advertisement

SpringerLink
Log in

A novel data aggregation scheme based on self-organized map for WSN

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

Abstract

Wireless sensor network allows efficient data collection and transmission in IoT environment. Since it usually consists of a large number of sensor nodes, a significant amount of redundant data and outliers are generated which deteriorate the network performance. In this paper, a novel data aggregation scheme is proposed which is based on self-organized map neural network to reduce redundant data and eliminate outliers. In addition, cosine similarity is used to improve the clustering process of sensor nodes based on the density and similarity of the data, and interquartile analysis is adopted to remove outliers. It allows to significantly reduce the energy consumption and enhance the network performance. Extensive simulation with real dataset shows that the proposed scheme consistently outperforms the existing representative data aggregation schemes in term of data reduction rate, network lifetime, and energy efficiency.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Oliveira LM, Rodrigues JJ (2011) Wireless sensor networks: a survey on environmental monitoring. JCM 6(2):143–151

    Article  Google Scholar 

  2. Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52(12):2292–2330

    Article  Google Scholar 

  3. Ullah I, Youn HY (2018) Statistical multipath queue-wise preemption routing for zigbee-based WSN. Wirel Pers Commun. 100:1537–1551

    Article  Google Scholar 

  4. Abid B, Nguyen TT, Seba H (2015) New data aggregation approach for time-constrained wireless sensor networks. J Supercomput 71(5):1678–1693

    Article  Google Scholar 

  5. Huang C-F, Lin W-C (2016) Data collection for multiple mobile users in wireless sensor networks. J Supercomput 72(7):2651–2669

    Article  Google Scholar 

  6. Rawat P, Singh KD, Chaouchi H, Bonnin JM (2014) Wireless sensor networks: a survey on recent developments and potential synergies. J Supercomput 68(1):1–48

    Article  Google Scholar 

  7. Vuran MC, Akyildiz IF (2006) Spatial correlation-based collaborative medium access control in wireless sensor networks. IEEEACM Trans Netw 14(2):316–329

    Article  Google Scholar 

  8. Yoon S, Shahabi C (2007) The clustered aggregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks. ACM Trans Sens Netw TOSN 3(1):3

    Article  Google Scholar 

  9. Lee S, Chung T (2004) Data aggregation for wireless sensor networks using self-organizing map. In: International Conference on AI, Simulation, and Planning in High Autonomy Systems, Springer, Berlin, pp 508–517

  10. Khedo K, Doomun R, Aucharuz S (2010) Reada: redundancy elimination for accurate data aggregation in wireless sensor networks. Wirel Sens Netw 2(04):300

    Article  Google Scholar 

  11. Ozdemir S, Xiao Y (2011) Polynomial regression based secure data aggregation for wireless sensor networks. In: IEEE, pp 1–5

  12. Bahi JM, Makhoul A, Medlej M (2012) An optimized in-network aggregation scheme for data collection in periodic sensor networks. In: International Conference on Ad-Hoc Networks and Wireless, Springer, Berlin, pp 153–166

  13. Cui J (2016) Data aggregation in wireless sensor networks. Networking and Internet Architecture. INSA Lyon

    Google Scholar 

  14. Jadhav NH, Kashid DN, Kulkarni SR (2014) Subset selection in multiple linear regression in the presence of outlier and multicollinearity. Stat Methodol 19:44–59

    Article  MathSciNet  MATH  Google Scholar 

  15. Yuan F, Zhan Y, Wang Y (2014) Data density correlation degree clustering method for data aggregation in WSN. IEEE Sens J 14(4):1089–1098

    Article  Google Scholar 

  16. Toloueiashtian M, Motameni H (2018) A new clustering approach in wireless sensor networks using fuzzy system. J Supercomput. 74(2):717–737

    Article  Google Scholar 

  17. Rostami AS, Badkoobe M, Mohanna F, Hosseinabadi AAR, Sangaiah AK (2018) Survey on clustering in heterogeneous and homogeneous wireless sensor networks. J Supercomput 74(1):277–323

    Article  Google Scholar 

  18. Kuila P, Jana PK (2014) Approximation schemes for load balanced clustering in wireless sensor networks. J Supercomput 68(1):87–105

    Article  Google Scholar 

  19. Diwakaran S, Perumal B, Vimala Devi K (2018) A cluster prediction model-based data collection for energy efficient wireless sensor network. J Supercomput. https://doi.org/10.1007/s11227-018-2437-z

    Article  Google Scholar 

  20. Lee KY, Suh Y-K (2018) A pattern-based outlier region detection method for two-dimensional arrays. J Supercomput. https://doi.org/10.1007/s11227-018-2418-2

    Article  Google Scholar 

  21. Kuna HD, García-Martinez R, Villatoro FR (2014) Outlier detection in audit logs for application systems. Inf Syst 44:22–33

    Article  Google Scholar 

  22. Subhashini R, Kumar VJS (2010) Evaluating the performance of similarity measures used in document clustering and information retrieval. In: IEEE, pp 27–31

  23. Wan X, Wang W, Liu J, Tong T (2014) Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. 14(1):135. https://doi.org/10.1186/1471-2288-14-135

    Article  Google Scholar 

  24. Cosine similarity function - Wikipedia [Internet]. [cited 2018 Feb 23]. Available from: https://en.wikipedia.org/wiki/Cosine_similarity

  25. How to Calculate Outliers, by interquartile range [Internet]. wikiHow. https://www.wikihow.com/Calculate-Outliers

  26. Kumar DI, Kounte MR (2016) Comparative study of self-organizing map and deep self-organizing map using MATLAB. In: IEEE, pp 1020–1023

  27. Kohonen T (2013) Essentials of the self-organizing map. Neural Netw. 37:52–65

    Article  Google Scholar 

  28. Faigl J, Hollinger GA (2018) Autonomous data collection using a self-organizing map. IEEE Trans Neural Netw Learn Syst 29(5):1703–1715. https://doi.org/10.1109/TNNLS.2017.2678482

    Article  MathSciNet  Google Scholar 

  29. Aghajari E, Chandrashekhar GD (2017) Self-organizing map based extended fuzzy C-means (SEEFC) algorithm for image segmentation. Appl Soft Comput 54:347–363

    Article  Google Scholar 

  30. Isa D, Kallimani V, Lee LH (2009) Using the self organizing map for clustering of text documents. Expert Syst Appl 36(5):9584–9591

    Article  Google Scholar 

  31. Ganegedara H, Alahakoon D (2012) Redundancy reduction in self-organising map merging for scalable data clustering. In: IEEE, pp 1–8

  32. Gedik B, Liu L, Philip SY (2007) ASAP: an adaptive sampling approach to data collection in sensor networks. IEEE Trans Parallel Distrib Syst 18(12):1766–1783

    Article  Google Scholar 

  33. Sun L-Y, Cai W, Huang X-X (2010) Data aggregation scheme using neural networks in wireless sensor networks. In: IEEE, pp V1-725

  34. Bo W, Han-ying H, Wen F (2007) A pseudo LEACH algorithm for wireless sensor networks. In: IMECS, pp 1366–1370

  35. Liu C, Wu K, Pei J (2007) An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation. IEEE Trans Parallel Distrib Syst 18(7):1010–1023

    Article  Google Scholar 

  36. Sung W-T (2009) Employed BPN to multi-sensors data fusion for environment monitoring services. In: International Conference on Autonomic and Trust Computing, pp 149–63

  37. Villas LA, Boukerche A, Guidoni DL, De Oliveira HA, De Araujo RB, Loureiro AA (2013) An energy-aware spatio-temporal correlation mechanism to perform efficient data collection in wireless sensor networks. Comput Commun 36(9):1054–1066

    Article  Google Scholar 

  38. Li G, Wang Y (2013) Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks. EURASIP J Wirel Commun Netw. 2013(1):85

    Article  Google Scholar 

  39. Santini S, Romer K (2006) An adaptive strategy for quality-based data reduction in wireless sensor networks. In: Proceedings of the 3rd International Conference on Networked Sensing Systems, pp 29–36

  40. Yin Y, Liu F, Zhou X, Li Q (2015) An efficient data compression model based on spatial clustering and principal component analysis in wireless sensor networks. Sensors 15(8):19443–19465

    Article  Google Scholar 

  41. Lin H, Bai D, Gao D, Liu Y (2016) Maximum data collection rate routing protocol based on topology control for rechargeable wireless sensor networks. Sensors 16(8):1201

    Article  Google Scholar 

  42. Cluster with Self-Organizing Map Neural Network-MATLAB & Simulink –MathWorks. https://kr.mathworks.com/help/nnet/ug/cluster-with-self-organizing-map-neural-network.html

  43. Comparison OF LEACH EAMMH SEP TEEN Protocols (Contact for codes in WSN)–File Exchange–MATLAB Central [Internet]. http://kr.mathworks.com/matlabcentral/fileexchange/46199-comparison-of-leach-eammh-sep-teen-protocols–contact-for-codes-in-wsn-

  44. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/Air+quality

  45. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/3D+Road+Network+(North+Jutland%2C+Denmark)

  46. Hevin Rajesh D, Paramasivan B (2015) Data aggregation framework for clustered sensor networks using multilayer perceptron neural network. Int J Adv Res Comput Eng Technol (IJARCET) 4(4). https://ijarcet.org/wp-content/uploads/IJARCET-VOL-4-ISSUE-4-1156-1160.pdf

Download references

Acknowledgements

This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2016-0-00133, Research on Edge computing via collective intelligence of hyperconnection IoT nodes), Korea, under the National Program for Excellence in SW supervised by the IITP (Institute for Information & communications Technology Promotion) (2015-0-00914), Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2016R1A6A3A11931385, Research of key technologies based on software defined wireless sensor network for realtime public safety service, 2017R1A2B2009095, Research on SDN-based WSN Supporting Real-time Stream Data Processing and Multiconnectivity), the second Brain Korea 21 PLUS project, and Samsung Electronics.

Author information

Authors and Affiliations

  1. Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea

    Ihsan Ullah

  2. College of Software, Sungkyunkwan University, Suwon, Korea

    Hee Yong Youn

Authors
  1. Ihsan Ullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hee Yong Youn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Ihsan Ullah or Hee Yong Youn.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ullah, I., Youn, H.Y. A novel data aggregation scheme based on self-organized map for WSN. J Supercomput 75, 3975–3996 (2019). https://doi.org/10.1007/s11227-018-2642-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-018-2642-9

Keywords

Search

Navigation