Skip to main content
SpringerLink
Log in

A new online anomaly learning and detection for large-scale service of Internet of Thing

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

The online anomaly detection has been propounded as the key idea of monitoring fault of large-scale sensor nodes in Internet of Things. Now, the exciting progresses of research have been made in online anomaly detection area. However, the highly dynamic distributing character of Internet of Things makes the anomaly detection scheme difficult to be used in online manner. This paper presents a new online anomaly learning and detection mechanism for large-scale service of Internet of Thing. Firstly, our model uses the reversible-jump MCMC learning to online learn anomaly-free of dynamics network and service data. Next, we perform a structural analysis of IoT-based service topology by network utility maximization theory. The results of experiment demonstrate the method accuracy in forecasting dynamics network and service structures from synthetic data.

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

Similar content being viewed by others

References

  1. Atzori L, Lera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805

    Article  MATH  Google Scholar 

  2. Miorandi D, Sicari S, De Pellegrini F, Chlamtac I (2012) The internet of things: vision, applications and research challenges. Ad Hoc Netw 10(7):1497–1516

    Article  Google Scholar 

  3. Xie M, Hu JK, Han S, Chen HH (2013) Scalable hypergrid k-NN-based online anomaly detection in wireless sensor networks. IEEE Trans Parallel Distrib Syst 24(8):1661–1670

    Article  Google Scholar 

  4. Xie M, Han S, Tian B, Parvin S (2011) Anomaly detection in wireless sensor networks: a survey. J Netw Comput Appl 34(4):1302–1325

    Article  Google Scholar 

  5. West SR, Guo Y, Wang XR (2011) Automated fault detection and diagnosis of HVAC subsystems using statistical machine learning. In: 12th international conference of the International Building Performance Simulation Association

  6. Warriach EU, Aiello M, Tei K (2012) A machine learning approach for identifying and classifying faults in wireless sensor networks. In: IEEE 15th international conference on computational science and engineering

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

    Article  Google Scholar 

  8. Yao Y, Sharma A, Golubchik L, Govindan R (2010) Online anomaly detection for sensor systems: a simple and efficient approach. Perform Eval 67(11):1059–1075

    Article  Google Scholar 

  9. Sagha H, Bayati H, Milln JR, Chavarriaga R (2013) Online anomaly detection and resilience in classifier ensembles. Pattern Recognit Lett 34(15):1916–1927

    Article  Google Scholar 

  10. Salem O, Liu YN, Mehaoua A (2013) A lightweight anomaly detection framework for medical wireless sensor networks. In: IEEE Wireless Communications and Conference (WCNC): service and applications

  11. Ma X, Yang D, Tang S, Luo Q, Zhang D, Li S (2004) Online mining in sensor networks. In: IFIP international conference on network and parallel computing, vol 3222, pp 544–550

  12. Subramaniam S, Palpanas T, Papadopoulos D, Kalogerakiand V, Gunopulos D (2006) Online outlier detection in sensor data using nonparametric models. In: Very Large Data Bases, VLDB

  13. Martincic F, Schwiebert L (2006) Distributed event detection in sensor networks. In: Proceedings of the international conference on systems and networks communication, pp 43–48

  14. Alam S, Noll J (2011) Virtualizing sensor for the enablement of semantic-aware internet of things ecosystem. Int J Des Anal Tools Circuits Syst 3(1):41–51

    Google Scholar 

  15. Azizi E (2013) Joint learning of modular structures from multiple data types. In: NIPS workshop of frontiers of network analysis: methods, models, and applications

  16. Azizi E, Airoldi E, Galagan J (2014) Learning modular structures from network data and node variables. In: International conference on machine learning

  17. Chiang M, Low SH, Calderbank AR, Doyle JC (2007) Layering as optimization decomposition: a mathematical theory of network architectures. Proc IEEE 95(1):255–312

    Article  Google Scholar 

  18. Wang J, Li L, Low SH, Doyle JC (2005) Cross-layer optimization in TCP/IP networks. IEEE/ACM Trans Netw 13(3):582–595

    Article  Google Scholar 

  19. Grant M, Boyd S (2008) CVX: Matlab software for disciplined convex programming. Prentice Hall, New York

    Google Scholar 

Download references

Acknowledgments

The author also would like to thank anonymous editor and reviewers who gave valuable suggestion that has helped to improve the quality of the manuscript. This research has been supported by the Project for 2015 National Key Technologies RD Program No. 2015BAH04F01.

Author information

Authors and Affiliations

  1. Laboratory of Precision Sensing and Control Center, Institute of Automation, Chinese Academy, Beijing, China

    JunPing Wang & Qiuming Kuang

  2. Beijing Key Laboratory of Cloud Computing Standard and Verification Research Institute, China Academy of Telecommunication Research of MIIT, Beijing, China

    ShiHui Duan

Authors
  1. JunPing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiuming Kuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. ShiHui Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to JunPing Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, J., Kuang, Q. & Duan, S. A new online anomaly learning and detection for large-scale service of Internet of Thing. Pers Ubiquit Comput 19, 1021–1031 (2015). https://doi.org/10.1007/s00779-015-0874-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-015-0874-8

Keywords

Search

Navigation