Contextual outlier detection for wireless sensor networks | Journal of Ambient Intelligence and Humanized Computing
Skip to main content
Springer Nature Link
Log in

Contextual outlier detection for wireless sensor networks

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

The quality of dataset measured and collected by wireless sensor networks (WSN) is often affected by noise and error that are inherent to resource-constrained sensor nodes. The affected data points deviating from the normal pattern are termed as outlier(s). However, detected outlier can be a result of the occurrence of an actual event. Outlier detection techniques developed for WSNs perform binary labelling of the data points and does not indicate the context stating whether the outlier is the result of an actual event or the noise/error. This paper proposes a contextual outlier detection framework specifically designed for WSNs named as in-network contextual outlier detection on edge (INCODE). The proposed framework also estimates the degree of outlierness associated with the detected outlier(s) to provide better insight into the measured data point. Algorithms used in INCODE are designed around the edge computing concept to minimize the communication and computational complexities to make it suitable for resource-constrained WSN. The results suggest an impressive 98% accuracy in identifying the context of the outlier(s). The low communication and computational complexity suggest INCODE’s suitability for resource constrained WSNs.

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
¥17,985 /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 includes VAT (Japan)

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

Similar content being viewed by others

Explore related subjects

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

Notes

  1. The terms ‘cause’ and ‘context’ are used interchangeably throughout the manuscript.

References

  • Ayadi A, Ghorbel O, Obeida AM, Abid M (2017) Outlier detection approaches for wireless sensor networks: A survey. Comput Netw 129:319–333

    Article  Google Scholar 

  • Abid A, Masmoudi A, Kachouri A, Mahfoudhi A (2017) Outlier detection in wireless sensor networks based on OPTICS method for events and errors identification. Wirel Pers Commun 97(1):1503–1515

    Article  Google Scholar 

  • Boracchi G, Michaelides M, Roveri M (2018) A cognitive monitoring system for detecting and isolating contaminants and faults in intelligent buildings. IEEE Trans Syst Man Cybern Syst. 48(3):433–447

    Article  Google Scholar 

  • Bosman HH et al (2017) Spatial anomaly detection in sensor networks using neighbourhood information. Infor Fusion 33:41–56

    Article  Google Scholar 

  • Bhuiyan Z et al (2017) Dependable structural health monitoring using wireless sensor networks. IEEE Trans Dependable Secur Comput 14(4):363–376

    Article  Google Scholar 

  • Bharti S, Pattanaik KK (2016) Gravitational outlier detection for wireless sensor networks. Int J Commun Syst 29(13):2015–2027

    Article  Google Scholar 

  • Branch J, Giannella C, Szymanski B, Wolff R, Kargupta H (2013) In-network outlier detection in wireless sensor networks. Knowl Infor Syst 34(1):23–54

    Article  Google Scholar 

  • Bisdikian C, Kaplan LM, Srivastava MB (2013) On the quality and value of information in sensor networks. ACM Trans Sens Netw 9(4):48

    Article  Google Scholar 

  • Burdakis S, Deligiannakis A (2012) Detecting outlier(s) in sensor networks using the geometric approach. In: IEEE 28th international conference on data engineering, April 1-5 2012, pp 1108–1119

  • Deligiannakis A et al. (2009) Another outlier bites the dust: computing meaningful aggregates in sensor networks. In: IEEE 25th international conference on data engineering, March 29-April 04 2009, pp 988–999

  • Fong S et al (2018) Predicting unusual energy consumption events from smart home sensor network by data stream mining with misclassified recall. J Ambient Intell Human Comput 9(4):1197–1221

    Article  Google Scholar 

  • Flouri K, Lozano B, Tsakalides P (2008) Distributed consensus algorithms for SVM training in wireless sensor networks. In: 16th European conference on signal processing, August 25-29 2008

  • Fawzy A, Mokhtar H, Hegazy O (2013) outlier(s) detection and classification in wireless sensor networks. Egypt Infor J 14:157–164

    Article  Google Scholar 

  • Giatrakos N et al (2018) Omnibus outlier detection in sensor networks using windowed locality sensitive hashing. Future Gener Computer Syst. https://doi.org/10.1016/j.future.2018.04.046

    Article  Google Scholar 

  • Gaura EI et al (2014) Edge mining the internet of things. IEEE Sens J 13(10):3816–3825

    Article  Google Scholar 

  • Gil P, Santos A, Cardoso A (2014) Dealing with outlier(s) in wireless sensor networks: an oil refinery application. IEEE Trans Control Syst Technol 22(4):1589–1596

    Article  Google Scholar 

  • Giatrakos N et al (2013) In-network approximate computation of outlier(s) with quality guarantees. Inf Syst 38(8):1285–1308

    Article  Google Scholar 

  • Giatrakos N et al. (2010) Taco: tnable approximate computation of outlier(s) in wireless sensor networks. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data, June 6-11 2010, pp 279-290

  • Gao Q et al (2006) Radio range adjustment for energy efficient wireless sensor networks. Ad-hoc Netw 4(1):75–82

    Article  Google Scholar 

  • Intel Lab Data (2014). http://db.csail.mit.edu/labdata/data.txt.gz. Accessed on 14 November 2014

  • Liu N, Shin D, Hu X (2018) Contextual outlier interpretation. arXiv:1711.10589. Accessed on 16 April 2018

  • LeBlanc HL, Zhang H, Sundaram S, Koutsoukos X (2012) Consensus of multi-agent networks in the presence of adversaries using only local information. Proceedings of the 1st international conference on High Confidence Networked Systems, April 17-18 2012, pp 1-10

  • Lotfi A et al (2012) Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour. J Ambient Intell Human Comput 3(3):205–218

    Article  Google Scholar 

  • Lee S, Lee H (2010) Analysis of network lifetime in cluster-based sensor networks. IEEE Commun Lett 14:900–902

    Article  Google Scholar 

  • Liang J, Parthasarathy S (2006) Robust contextual outlier detection: where context meets sparsity. https://arxiv.org/pdf/1607.08329.pdf. Accessed on 23 March 2018

  • Morley T, Oram A (2016) The elements of anomaly detection in the internet of things. O’Reilly publishing. https://www.oreilly.com/ideas/the-elements-ofanomaly-detection-in-the-internet-of-things. Accessed on 23 March 2018

  • Malazil HT, Zamanifar K, Dulman S (2011) FED: fuzzy event detection model for wireless sensor networks. Int J Wirel Mob Netw 3(6):29–45

    Article  Google Scholar 

  • Nesa N, Ghosh T, Banerjee I (2018) Non-parametric sequence-based learning approach for outlier detection in IoT. Future Gener Comput Syst 82:412–421

    Article  Google Scholar 

  • O’Reilly C, Gluhak A, Imran MA, Rajasegarar S (2014) Anomaly detection in wireless sensor networks in a non-stationary environment. IEEE Commun Surv Tutor 16(3):1413–1432

    Article  Google Scholar 

  • Paola A, Gaglio S (2015) Adaptive distributed outlier detection for WSNs. IEEE Trans Cybern 45(5):888–899

    Article  Google Scholar 

  • Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2014) Context aware computing for the internet of things : a survey. IEEE Commun Surv Tutor 16(1):414–454

    Article  Google Scholar 

  • Page L (2001) Method for node ranking in a linked database. US6285999 B1: 285-999

  • Rajasegarar S, Leckie C, Palaniswami M, Bezdek J (2006) Distributed anomaly detection in wireless sensor networks. In: 10th IEEE Singapore international conference on communication systems, October 30-November 1 2006

  • Rokach L, Maimon O (2005) Data mining and knowledge discovery handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4. ISBN 978-0-387-09822-7, e-ISBN 978-0-387-09823-4

    Book  MATH  Google Scholar 

  • Radivojac P, Korad U, Sivalingam KM, Obradovic Z (2003) Learning from class-imbalanced data in wireless sensor networks. In: Proceedings of the IEEE 58th vehicular technology conference, October 6-9 2003, pp 3030-3034

  • Shakya R, Singh Y, Verma N (2013) Generic correlation model for wireless sensor network applications. Wirel Sens Syst 3:266–276

    Article  Google Scholar 

  • Shahid N, Naqvi IH, Qaisar SB (2012) Real time energy efficient approach to outlier & event detection in wireless sensor networks. In: Proceedings of international conference on communication systems, November 21-23 2012, pp 162-166

  • Sheng B, Li Q, Mao W, Jin W (2007) Outlier detection in sensor networks. In: Proceedings of the 8th ACM international symposium on mobile ad Hoc networking and computing, 9-14 September 2007, pp 219–228

  • Shewhart WA (1939) Statistical Method from the viewpoint of quality control. The Graduate School, Department of Agriculture, Washington, D.C., 1939 edition

    Google Scholar 

  • Wong KBY, Zhang T, Aghajan H (2015) Extracting patterns of behaviour from a network of binary sensors. J Ambient Intell Human Comput. 6(1):83–105

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Zang Y, Hamm N, Meratnia N, Stein A, Voort M, Havinga P (2012) Statistics-based outlier detection for wireless sensor networks. Int J Geographical Inf Sci 26(8):1373–1392

    Article  Google Scholar 

Download references

Acknowledgements

We thank the editor and anonymous reviewers for their constructive comments and suggestions that helped improve the quality of this manuscript.

Author information

Authors and Affiliations

  1. Department of Information Technology, Indira Gandhi Delhi Technical University for Women, Delhi, India

    Sourabh Bharti

  2. Wireless Sensor Networks Laboratory, ABV-Indian Institute of Information Technology and Management, Gwalior, India

    K. K. Pattanaik

  3. Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Allahabad, India

    Anshul Pandey

Authors
  1. Sourabh Bharti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. K. Pattanaik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anshul Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sourabh Bharti.

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

Bharti, S., Pattanaik, K.K. & Pandey, A. Contextual outlier detection for wireless sensor networks. J Ambient Intell Human Comput 11, 1511–1530 (2020). https://doi.org/10.1007/s12652-019-01194-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-019-01194-5

Keywords

Search

Navigation