A Robust Architecture for Detecting Outliers in IoT Data using STCPOD Model

Authors(2) :-Priya Stella Mary, Dr. L. Arockiam

Internet of Things (IoT) is an ecosystem of interconnected physical devices that are accessible through the internet so that these devices can collect and exchange data. Outliers in IoT are generated either due to system malfunctions or because of unexpected transformation in the observed phenomenon. A novel outlier detection mechanism is crucial for IoT so as to achieve high detection rate and low false alarm rate by taking into consideration all the characteristics of IoT data while spotting outliers. In this paper a robust Architecture is proposed to efficiently detect outliers in IoT data using STCPOD (a novel STCPOD (Spatially and temporally correlated proximate Outlier Detection) model.

Authors and Affiliations

Priya Stella Mary
Department of Computer Science, St Joseph College(Autonomous). Trichy, Tiruchirappalli, Tamil Nadu, India
Dr. L. Arockiam
Department of Computer Science, St Joseph College(Autonomous). Trichy, Tiruchirappalli, Tamil Nadu, India

IoT, sensors, outliers, outlier detection

  1. Shen, Q. , Zhao, Z. , Niu, W. , Liu, Y. and Tang, H. , "Tolerance-Based Adaptive Online Outlier Detection for Internet of Things", In Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing, doi={10. 1109/GreenCom-CPSCom. 2010. 23}, 2010, pp. 560-565.
  2. Karkouch A, Mousannif H, Al Moatassime H and Noel T, "Data quality in internet of things: A state-of-the-art survey", Journal of Network and Computer Applications, Vol. 73, 2016, DOI: https://doi. org/10. 1016/j. jnca. 2016. 08. 002, pp. 57-81.
  3. Zhang, Y. , Meratnia, N. and Havinga, P. , "Why general outlier detection techniques do not suffice for wireless sensor networks", Intelligent Techniques for Warehousing and Mining Sensor Network Data, 2009, DOI: 10. 4018/978-1-60566-328-9. ch007, p. 136.
  4. Nisha, U. B. , Maheswari, N. U. , Venkatesh, R. and Abdullah, R. Y. , 2014, December. " Robust estimation of incorrect data using relative correlation clustering technique in wireless sensor networks", IEEE International Conference on Communication and Network Technologies (ICCNT), 2014, ISBN: 978-1-4799-6266-2, pp. 314-318.
  5. Wang, Min, and Zhongbo Wu. "Spatio-temporal correlation based outlier detection algorithm in sensor network. " In Second IEEE International Conference on Computer and Automation Engineering (ICCAE), Vol. 4, 2010, doi={10. 1109/ICCAE. 2010. 5451639}, pp. 424-427.
  6. Li, Fangfang, and Zhibo Feng. "An efficient real-time event detection approach based on temporal-spatial correlations in wireless sensor networks. " In IEEE International Conference on Computer Science and Network Technology (ICCSNT), Vol. 2, 2011, doi={10. 1109/ICCSNT. 2011. 6182185}, pp. 1245-1249.
  7. Niu, Kun, Fang Zhao, and Xiuquan Qiao. "An outlier detection algorithm in wireless sensor network based on clustering", In 15th IEEE International Conference on Communication Technology (ICCT), 2013, doi={10. 1109/ICCT. 2013. 6820415}, pp. 433-437.
  8. Abid, Aymen, Abdennaceur Kachouri, and Adel Mahfoudhi. "Anomaly detection through outlier and neighborhood data in Wireless Sensor Networks", In 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2016, doi={10. 1109/ATSIP. 2016. 7523045} pp. 26-30.
  9. Lalem, Farid, Ahcene Bounceur, Rahim Kacimi, Reinhardt Euler, and Massinissa Saoudi, "Faulty Data Detection in Wireless Sensor Networks Based on Copula Theory", In Proceedings of the ACM International Conference on Big Data and Advanced Wireless Technologies, 2016, ISBN: 978-1-4503-4779-2 doi={10. 1145/3010089. 3010114}, p. 29.
  10. Andrade, A. T. C. , C. Montez, R. Moraes, A. R. Pinto, Francisco Vasques, and G. L. da Silva. "Outlier detection using k-means clustering and lightweight methods for Wireless Sensor Networks", In 42nd Annual Conference of the IEEE on Industrial Electronics Society, 2016, doi={10. 1109/IECON. 2016. 7794093}, pp. 4683-4688.
  11. Li, Wenjie, Francesca Bassi, Davide Dardari, Michel Kieffer, and Gianni Pasolini. "Defective sensor identification for WSNs involving generic local outlier detection tests. " IEEE transactions on Signal and Information Processing over Networks, Vol. 2, No. 1, 2016, doi={10. 1109/TSIPN. 2016. 2516821}, ISSN={2373-776X}, pp. 29-48.
  12. Salehi, Mahsa, Christopher Leckie, James C. Bezdek, and Tharshan Vaithianathan. "Local outlier detection for data streams in sensor networks: Revisiting the utility problem invited paper", In IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2015, doi={10. 1109/ISSNIP. 2015. 7106978}, pp. 1-6.
  13. Martins, Hugo, Fábio Januário, Luís Palma, Alberto Cardoso, and Paulo Gil. "A machine learning technique in a multi-agent framework for online outliers detection in Wireless Sensor Networks" , In 41st Annual Conference of the IEEE on Industrial Electronics Society, 2015, doi={10. 1109/IECON. 2015. 7392180}, pp. 000688-000693.
  14. Zhang, Yang, Nirvana Meratnia, and Paul Havinga. "Outlier detection techniques for wireless sensor networks: A survey. " In IEEE Communications Surveys & Tutorials, Vol. 12, No. 2, 2010, doi={10. 1109/SURV. 2010. 021510. 00088}, ISSN={1553-877X}, pp. 159-170.
  15. Branch, Joel W. , Chris Giannella, Boleslaw Szymanski, Ran Wolff, and Hillol Kargupta ", In-network outlier detection in wireless sensor networks", In Springer Journal of Knowledge and information systems, Vol. 34, No. 1, 2013, https://doi. org/10. 1007/s10115-011-0474-5, pp. 23-54.
  16. Chatzigiannakis, Vasilis, Symeon Papavassiliou, Mary Grammatikou, and B. Maglaris. "Hierarchical anomaly detection in distributed large-scale sensor networks. " In 11th IEEE Symposium on Computers and Communications, 2006, doi={10. 1109/ISCC. 2006. 1691116}, ISSN={1530-1346} pp. 761-767.
  17. Ghorbel, Oussama, Mohamed Wassim Jmal, Walid Ayedi, Hichem Snoussi, and Mohamed Abid, "An overview of outlier detection technique developed for wireless sensor networks", In 10th International Multi-Conference on Systems, Signals and Devices (SSD), 2013, doi={10. 1109/SSD. 2013. 6564165}, pp. 1-6.

Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 659-664
Manuscript Number : CSEIT1726164
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Priya Stella Mary, Dr. L. Arockiam, "A Robust Architecture for Detecting Outliers in IoT Data using STCPOD Model", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.659-664, November-December-2017.
Journal URL : http://ijsrcseit.com/CSEIT1726164

Article Preview