Improved approaches for density-based outlier detection in wireless sensor networks


Density-based algorithms are important data clustering techniques used to find arbitrary shaped clusters and outliers. Recently, outlier detectors through density-based clustering are applied to supervise data streams including wireless sensor networks (WSN’s). In this article, we compare two density-based methods, DBSCAN and OPTICS, using proposed configuration and specific classifier to identify outlier and normal clusters. For simulation, in MATLAB, we use real data of WSN’s from Intel Berkeley lab in that we introduce white Gaussian noise for different signal-to-noise ratio per data vector. We evaluate the two algorithms under different input parameters using several performance metrics as detection rate, false alarm rate. Results indicate that the DBSCAN scheme is more accurate and comprehensive compared with existing approaches for WSN’s. At the same time, OPTICS remains an interesting solution for a hierarchical study of datasets with an identification of anomalies.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9


  1. 1.

    Sitharama Iyengar S, Brooks RR (2016) Distributed sensor networks: sensor. CRC Press, Boca Raton

    Book  Google Scholar 

  2. 2.

    Khediri SE, Nasr N, Kachouri A, Wei A (2013) Synchronization in wireless sensors networks using balanced clusters. In: 6th joint IFIP wireless and mobile networking conference (WMNC). IEEE, pp 1–4

  3. 3.

    Khediri SE, Nasri N, Khan RU, Kachouri A (2021) An improved energy efficient clustering protocol for increasing the life time of wireless sensor networks. Wirel Pers Commun 116(1):539–558

    Article  Google Scholar 

  4. 4.

    Khediri E et al (2020) Improved node localization using k-means clustering for wireless sensor networks. Comput Sci Rev 37:100284

    MathSciNet  Article  Google Scholar 

  5. 5.

    Mikail SA, Wang J, Zhang S (2020) Distributed clustering and operational state scheduling in wireless rechargeable sensor networks. Int J Sens Netw 34(1):26–37

    Article  Google Scholar 

  6. 6.

    Zhang Y, Meratnia N, Havinga P (2010) Outlier detection techniques for wireless sensor networks: a survey. IEEE Commun Surv Tutor 12(2):159–170

    Article  Google Scholar 

  7. 7.

    Gupta M, Gao J, Aggarwal CC, Han J (2013) Outlier detection for temporal data: a survey. IEEE Trans Knowl Data Eng 26(9):2250–2267

    Article  Google Scholar 

  8. 8.

    Shaikh RAJ, Naidu H, Kokate PA (2020) Next-generation WSN for environmental monitoring employing big data analytics, machine learning and artificial intelligence. In: Evolutionary computing and mobile sustainable networks. Springer, pp 181–196

  9. 9.

    Safaei M et al (2020) A systematic literature review on outlier detection in wireless sensor networks. Symmetry 12(3):328

    MathSciNet  Article  Google Scholar 

  10. 10.

    Alrashidi M et al (2020) Energy-efficiency clustering and data collection for wireless sensor networks in industry 4.0. J Ambient Intell Humaniz Comput 1–8

  11. 11.

    Gaddam A, Wilkin T, Angelova M, Gaddam J (2020) Detecting sensor faults, anomalies and outliers in the internet of things: a survey on the challenges and solutions. Electronics 9(3):511

    Article  Google Scholar 

  12. 12.

    Subramaniam S et al (2006) Online outlier detection in sensor data using non-parametric models. In: Proceedings of the 32nd international conference on very large data bases. VLDB Endowment, pp 187–198

  13. 13.

    Bihar P (2016) Density based outlier detection (DBOD) in data mining: a novel approach. In: Recent advances in mathematics, statistics and computer science, p 403

  14. 14.

    Duan L (2012) Density-based clustering and anomaly detection. Business Intelligence-Solution for Business Development 79–96

  15. 15.

    Ester M, Kriegel H-P, Sander J, Xiaowei X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96:226–231

    Google Scholar 

  16. 16.

    Ankerst M, Breunig MM, Kriegel H-P, Sander J (1999) Optics: ordering points to identify the clustering structure. In: ACM Sigmod record, volume 28. ACM, pp 49–60

  17. 17.

    Daszykowski M, Walczak B, Massart DL (2002) Looking for natural patterns in analytical data. 2. Tracing local density with optics. J Chem Inf Comput Sci 42(3):500–507

    Article  Google Scholar 

  18. 18.

    Hinneburg A, Keim DA (1998) An efficient approach to clustering in large multimedia databases with noise. In: KDD, vol 98, pp 58–65

  19. 19.

    Chitradevi N et al (2013) Efficient density based techniques for anomalous data detection in wireless sensor networks. J Appl Sci Eng 16(2):211–223

    Google Scholar 

  20. 20.

    Kumaran RS (2011) Ordering points to identify the clustering structure (optics) with ant colony optimization for wireless sensor networks. Eur J Sci Res 59(4):571–582

    Google Scholar 

  21. 21.

    Shamshirband S et al (2014) D-FICCA: a density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks. Measurement 55:212–226

    Article  Google Scholar 

  22. 22.

    Zheng Z, Jeong H-Y, Huang T, Shu J (2017) Kde based outlier detection on distributed data streams in multimedia network. Multimed Tools Appl 76(17):18027–18045

    Article  Google Scholar 

  23. 23.

    Yan Y, Cao L, Kulhman C, Rundensteiner E (2017) Distributed local outlier detection in big data. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1225–1234

  24. 24.

    Elmogy A, Rizk H, Sarhan AM (2021) Ofcod On the fly clustering based outlier detection framework. Data 6(1):1

    Article  Google Scholar 

  25. 25.

    Nanda et al (2021) A novel approach to detect emergency using machine learning. In: Progress in advanced computing and intelligent engineering. Springer, pp 185–192

  26. 26.

    Wang X, Wang X, Wilkes M (2020) New developments in unsupervised outlier detection. Springer, Berlin

    Google Scholar 

  27. 27.

    Kamal S, Ramadan RA, Fawzy EL-R (2016) Smart outlier detection of wireless sensor network. Facta Universitatis Ser Electron Energ 29(3):383–393

    Article  Google Scholar 

  28. 28.

    Guo S et al (2014) Detecting faulty nodes with data errors for wireless sensor networks. ACM Trans Sens Netw TOSN 10(3):40

    Google Scholar 

  29. 29.

    Livani MA, Alikhany M, Tabari MY et al (2013) Outlier detection in wireless sensor networks using distributed principal component analysis. J AI Data Min 1(1):1–11

    Google Scholar 

  30. 30.

    Zhang Y et al (2012) Statistics-based outlier detection for wireless sensor networks. Int J Geogr Inf Sci 26(8):1373–1392

    Article  Google Scholar 

  31. 31.

    Tran TN, Drab K, Daszykowski M (2013) Revised DBSCAN algorithm to cluster data with dense adjacent clusters. Chemom Intell Lab Syst 120:92–96

    Article  Google Scholar 

  32. 32.

    Powers DMW (2007) Evaluation: from precision, recall and f-factor to roc, informedness. Technical report, markedness correlation. Technical report SIE-07-001, School of Informatics and Engineering, Flinders University, Australia, Australia

  33. 33.

    Sluban B (2014) Ensemble-based noise and outlier detection. PhD thesis, Joezef Stefan International Postgraduate School Ljubljana, Slovenia

  34. 34.

    Zhou X, Valle AD (2020) Range based confusion matrix for imbalanced time series classification. In: 2020 6th conference on data science and machine learning applications (CDMA). IEEE, pp 1–6

  35. 35.

    Samuel M (2004) Intel lab data

  36. 36.

    Luo X, Chang X (2015) A novel data fusion scheme using grey model and extreme learning machine in wireless sensor networks. Int J Control Autom Syst 13(3):539–546

    Article  Google Scholar 

  37. 37.

    Appice A, Ciampi A, Malerba D (2015) Summarizing numeric spatial data streams by trend cluster discovery. Data Min Knowl Discov 29(1):84–136

    MathSciNet  Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Salim El Khediri.

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

Verify currency and authenticity via CrossMark

Cite this article

Abid, A., Khediri, S.E. & Kachouri, A. Improved approaches for density-based outlier detection in wireless sensor networks. Computing (2021).

Download citation


  • Data analysis
  • Density clustering
  • Outlier detection
  • WSN’s

Mathematics Subject Classification

  • 68
  • 68M18