Skip to main content
Springer Nature Link
Log in

Efficient data aggregation with node clustering and extreme learning machine for WSN

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

Abstract

Wireless sensor network is effective for data aggregation and transmission in IoT environment. Here, the sensor data often contain a significant amount of noises or redundancy exists, and thus, the data are aggregated to extract meaningful information and reduce the transmission cost. In this paper, a novel data aggregation scheme is proposed based on clustering of the nodes and extreme learning machine (ELM) which efficiently reduces redundant and erroneous data. Mahalanobis distance-based radial basis function is applied to the projection stage of the ELM to reduce the instability of the training process. Kalman filter is also used to filter the data at each sensor node before transmitted to the cluster head. Computer simulation with real datasets shows that the proposed scheme consistently outperforms the existing schemes in terms of clustering accuracy of the data and energy efficiency of WSN.

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

Similar content being viewed by others

References

  1. Ullah I, Youn HY (2018) Statistical multipath queue-wise preemption routing for zigbee-based. Wirel Pers Commun 100(4):1537–1551

    Article  Google Scholar 

  2. Gravina R, Alinia P, Ghasemzadeh H, Fortino G (2017) Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges. Inf Fus 35:68–80

    Article  Google Scholar 

  3. 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 

  4. Ullah I, Youn HY (2019) A novel data aggregation scheme based on self-organized map for WSN. J Supercomput 75(7):3975–3996

    Article  Google Scholar 

  5. De Paola A, Gaglio S, Re GL, Milazzo F, Ortolani M (2015) Adaptive distributed outlier detection for WSNs. IEEE Trans Cybern 45(5):902–913

    Article  Google Scholar 

  6. 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 

  7. 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  Google Scholar 

  8. 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 

  9. Du T, Qu S, Liu F, Wang Q (2015) An energy efficiency semi-static routing algorithm for WSNs based on HAC clustering method. Inf Fus 21:18–29

    Article  Google Scholar 

  10. Abrardo A, Martalò M, Ferrari G (2017) Information fusion for efficient target detection in large-scale surveillance wireless sensor networks. Inf Fus 38:55–64

    Article  Google Scholar 

  11. Shobana M, Sabitha R, Karthik S Cluster-based systematic data aggregation model (CSDAM) for real-time data processing in large-scale WSN. Wirel Pers Commun 1–19

  12. Wang Z, Wu D, Gravina R, Fortino G, Jiang Y, Tang K (2017) Kernel fusion based extreme learning machine for cross-location activity recognition. Inf Fus 37:1–9

    Article  Google Scholar 

  13. Aggarwal CC (2015) Outlier analysis. Springer, Berlin, pp 237–263

    Google Scholar 

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

    Google Scholar 

  15. Bo W, Han-ying H, Wen F (2007) A pseudo LEACH algorithm for wireless sensor networks. In: International multi conference of engineers and computer scientists, IMECS, Hong Kong, China, pp 1366–1370

  16. 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 

  17. Shivashankarappa N, Adiga S, Avinash R, Kalman Janardhan H (2016) Filter based multiple sensor data fusion in systems with time delayed state. IEEE, Piscataway, pp 375–82

    Google Scholar 

  18. Sung W-T (2009) Employed BPN to multi-sensors data fusion for environment monitoring services. Auton Trust Comput 5586:149–163

    Article  Google Scholar 

  19. Murphy KP, Russell S (2002) Dynamic bayesian networks: representation, inference and learning. PhD Thesis, UC Berkeley, Computer Science Division

  20. Zhang Y, Ji Q (2006) Active and dynamic information fusion for multisensor systems with dynamic Bayesian networks. IEEE Trans Syst Man Cybern Part B Cybern 36(2):467–472

    Article  Google Scholar 

  21. De Paola A, La Cascia M, Re GL, Morana M, Ortolani M (2012) User detection through multi-sensor fusion in an ami scenario. IEEE, Piscataway, pp 2502–2509

    Google Scholar 

  22. van Kasteren T, Krose B (2007) Bayesian activity recognition in residence for elders. In: IE’07: proceedings of the third international intelligent environments conference, pp 209–212

  23. Roy N, Pallapa G, Das SK (2007) A middleware framework for ambiguous context mediation in smart healthcare application. IEEE, Piscataway, pp 72–72

    Google Scholar 

  24. Hossain MA, Atrey PK, El Saddik A (2009) Learning multisensor confidence using a reward-and-punishment mechanism. IEEE Trans Instrum Meas 58(5):1525–1534

    Article  Google Scholar 

  25. Yurur O, Labrador M, Moreno W (2014) Adaptive and energy efficient context representation framework in mobile sensing. IEEE Trans Mob Comput 13(8):1681–1693

    Article  Google Scholar 

  26. Rahmati A, Zhong L (2011) Context-based network estimation for energy-efficient ubiquitous wireless connectivity. IEEE Trans Mob Comput 10(1):54–66

    Article  Google Scholar 

  27. Kang S, Lee J, Jang H, Lee Y, Park S, Song J (2010) A scalable and energy-efficient context monitoring framework for mobile personal sensor networks. IEEE Trans Mob Comput 9(5):686–702

    Article  Google Scholar 

  28. Nath S (2012) ACE: exploiting correlation for energy-efficient and continuous context sensing. ACM, New York, pp 29–42

    Google Scholar 

  29. Jiang Y, Qiu H, McCartney M, Halfond WG, Bai F, Grimm D et al (2014) Carlog: a platform for flexible and efficient automotive sensing. ACM, New York, pp 221–235

    Google Scholar 

  30. 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 

  31. Zhao M, Ma M, Yang Y (2011) Efficient data gathering with mobile collectors and space-division multiple access technique in wireless sensor networks. IEEE Trans Comput 60(3):400–417

    Article  MathSciNet  Google Scholar 

  32. Jea D, Somasundara A, Srivastava M (2005) Multiple controlled mobile elements (data mules) for data collection in sensor networks. Springer, Berlin, pp 244–257

    Google Scholar 

  33. Rao J, Biswas S (2008) Joint routing and navigation protocols for data harvesting in sensor networks. IEEE, Piscataway, pp 143–152

    Google Scholar 

  34. Chakrabarti A, Sabharwal A, Aazhang B (2003) Using predictable observer mobility for power efficient design of sensor networks. Springer, Berlin, pp 129–145

    MATH  Google Scholar 

  35. Aslanyan H, Leone P, Rolim J (2010) Data propagation with guaranteed delivery for mobile networks. In: Experimental algorithms, vol 6049, pp 386–397

  36. Howard A, Matarić MJ, Sukhatme GS (2002) Mobile sensor network deployment using potential fields: a distributed, scalable solution to the area coverage problem. In: Distributed autonomous robotic systems, vol 5. Springer, Berlin, pp 299–308

  37. Santini S, Romer K (2006) An adaptive strategy for quality-based data reduction in wireless sensor networks, pp 29–36

  38. 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 

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

  40. Simon D (2006) Optimal state estimation. Wiley, New York

    Book  Google Scholar 

  41. Du K-L, Swamy MN (2013) Neural networks and statistical learning. Springer, Berlin

    MATH  Google Scholar 

  42. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  43. 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 

  44. 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 

  45. Comparison OF LEACH EAMMH SEP TEEN Protocols (Contact for codes in WSN)—File Exchange—MATLAB Central. https://kr.mathworks.com/matlabcentral/fileexchange/46199-comparison-of-leach-eammh-sep-teen-protocols--contact-for-codes-in-wsn-46. Accessed 15 Apr 2019

  46. Taormina R (2018) ELM_MatlabClass: fast OOP MATLAB® implementation of extreme learning machines for both regression and binary classification problems. https://github.com/rtaormina/ELM_MatlabClass. Accessed 5 Mar 2019

  47. UCI Machine Learning Repository: Data Sets. https://archive.ics.uci.edu/ml/datasets.php. Accessed 28 May 2019

  48. Data Aggregation Framework for Clustered Sensor Networks Using Multilayer Perceptron Neural Network. https://ijarcet.org/wp-content/uploads/IJARCET-VOL-4-ISSUE-4-1156-1160.pdf. Accessed 17 Feb 2019

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 (2017R1A2B2009095, Research on SDN-based WSN Supporting Real-time Stream Data Processing and Multiconnectivity, 2019R1I1A1A01058780, Efficient Management of SDN-based Wireless Sensor Network Using Machine Learning Technique), the second Brain Korea 21 PLUS project.

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 inPubMed Google Scholar

  2. Hee Yong Youn
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Hee Yong Youn.

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

Ullah, I., Youn, H.Y. Efficient data aggregation with node clustering and extreme learning machine for WSN. J Supercomput 76, 10009–10035 (2020). https://doi.org/10.1007/s11227-020-03236-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03236-8

Keywords