Abstract
Wireless sensor networks contain millions of nodes deployed in a spatially dispersed manner. These sensors are low battery powered devices having limited storage and computation power. The data collected by these sensors may be subjected to error due to environmental fluctuations, interference in wireless communication or wearing of sensors with time. These erroneous data deviate significantly from the rest of the data. To solve this issue, we present a new technique named Outlierness Factor based on Neighbourhood to detect and analyse the outliers in sensor network. Proposed detection approach is time efficient and scalable. Further, outlier data are classified as errors due to sensor malfunctioning or actual detected events such as fire detection, weather changes, earthquakes, landslide etc. The capabilities of the proposed approach have been evaluated on real dataset obtained from Intel Berkeley research lab and synthetic datasets. The results show the effectiveness of the proposed approach in contrast to the previously dealt approaches.
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Gupta, U., Bhattacharjee, V., & Bishnu, P. S. (2019). A new neighborhood-based outlier detection technique. In Proceedings of the third international conference on microelectronics, computing and communication systems (pp. 527–534). Springer.
Han, J., Pei, J., & Kamber, M. (2011). Data mining: Concepts and techniques. Amsterdam: Elsevier.
Ayadi, A., Ghorbel, O., Obeid, A. M., & Abid, M. (2017). Outlier detection approaches for wireless sensor networks: A survey. Computer Networks, 129, 319–333.
Alaiad, A., & Zhou, L. (2017). Patients’ adoption of WSN-based smart home healthcare systems: An integrated model of facilitators and barriers. IEEE Transactions on Professional Communication, 60(1), 4–23.
Mahamuni, C. V. (2016). A military surveillance system based on wireless sensor networks with extended coverage life. In 2016 International conference on global trends in signal processing, information computing and communication (ICGTSPICC) (pp. 375–381). IEEE.
Bhattacharjee, S., Roy, P., Ghosh, S., Misra, S., & Obaidat, M. S. (2012). Wireless sensor network-based fire detection, alarming, monitoring and prevention system for Bord-and-Pillar coal mines. Journal of Systems and Software, 85(3), 571–581.
Wang, Y., Liu, Z., Wang, D., Li, Y., & Yan, J. (2017). Anomaly detection and visual perception for landslide monitoring based on a heterogeneous sensor network. IEEE Sensors Journal, 17(13), 4248–4257.
Ludeña-Choez, J., Choquehuanca-Zevallos, J. J., & Mayhua-López, E. (2019). Sensor nodes fault detection for agricultural wireless sensor networks based on NMF. Computers and Electronics in Agriculture, 161, 214–224.
Zia, H., Harris, N. R., Merrett, G. V., Rivers, M., & Coles, N. (2013). The impact of agricultural activities on water quality: A case for collaborative catchment-scale management using integrated wireless sensor networks. Computers and Electronics in Agriculture, 96, 126–138.
Oliver, N., Calvard, T. S., & Potocnik, K. (2016). Sensemaking and control at the limit: The air France 447 disaster. In Academy of Management Proceedings (Vol. 2016, p. 12546). Academy of Management Briarcliff Manor, NY.
Gama, J., & Gaber, M. M. (2007). Learning from data streams: Processing techniques in sensor networks. Berlin: Springer.
Zhang, Y., Hamm, N. A., Meratnia, N., Stein, A., Van De Voort, M., & Havinga, P. J. (2012). Statistics-based outlier detection for wireless sensor networks. International Journal of Geographical Information Science, 26(8), 1373–1392.
Angiulli, F., & Pizzuti, C. (2005). Outlier mining in large high-dimensional data sets. IEEE Transactions on Knowledge and Data Engineering, 17(2), 203–215.
Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. In ACM sigmod record (Vol. 29, pp. 93–104). ACM.
Hawkins, D. M. (1980). Identification of outliers (Vol. 11). Berlin: Springer.
Abid, A., Masmoudi, A., Kachouri, A., & Mahfoudhi, A. (2017). Outlier detection in wireless sensor networks based on OPTICS method for events and errors identification. Wireless Personal Communications, 97(1), 1503–1515.
Wu, W., Cheng, X., Ding, M., Xing, K., Liu, F., & Deng, P. (2007). Localized outlying and boundary data detection in sensor networks. IEEE Transactions on Knowledge and Data Engineering, 19(8), 1145–1157.
Branch, J. W., Giannella, C., Szymanski, B., Wolff, R., & Kargupta, H. (2013). In-network outlier detection in wireless sensor networks. Knowledge and Information Systems, 34(1), 23–54.
Fawzy, A., Mokhtar, H. M., & Hegazy, O. (2013). Outliers detection and classification in wireless sensor networks. Egyptian Informatics Journal, 14(2), 157–164.
Zhang, Y., Meratnia, N., & Havinga, P. J. (2010). Outlier detection techniques for wireless sensor networks: A survey. IEEE Communications Surveys and Tutorials, 12(2), 159–170.
Chen, Y., & Li, S. (2019). A lightweight anomaly detection method based on SVDD for wireless sensor networks. Wireless Personal Communications, 105(4), 1235–1256.
Titouna, C., Aliouat, M., & Gueroui, M. (2015). Outlier detection approach using bayes classifiers in wireless sensor networks. Wireless Personal Communications, 85(3), 1009–1023.
Titouna, C., Naït-Abdesselam, F., & Khokhar, A. (2019). DODS: A distributed outlier detection scheme for wireless sensor networks. Computer Networks, 161, 93–101.
Chaudhary, S. (2019). Why “1.5” in IQR Method of Outlier Detection?. https://medium.com/mytake/why-1-5-in-iqr-method-of-outlier-detection-5d07fdc82097. Accessed June 2020.
Rajasegarar, S., Leckie, C., Palaniswami, M., & Bezdek, J. C. (2006). Distributed anomaly detection in wireless sensor networks. In 2006 10th IEEE Singapore international conference on communication systems (pp. 1–5). IEEE.
Madden, S. (2004). Intel lab data. http://db.csail.mit.edu/labdata/labdata.html. Accessed April 2019.
Chitradevi, N., Palanisamy, V., Baskaran, K., & Nisha, U. B. (2010). Outlier aware data aggregation in distributed wireless sensor network using robust principal component analysis. In 2010 Second international conference on computing, communication and networking technologies (pp. 1–9). IEEE.
Shih, K. P., Wang, S. S., Yang, P. H., & Chang, C. C. (2006). CollECT: Collaborative event detection and tracking in wireless heterogeneous sensor networks. In 11th IEEE symposium on computers and communications (ISCC’06) (pp. 935–940).
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The authors acknowledge the contribution of the anonymous reviewers whose comments greatly helped in preparing the paper in its present form.
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Gupta, U., Bhattacharjee, V. & Bishnu, P.S. Outlier Detection in Wireless Sensor Networks Based on Neighbourhood. Wireless Pers Commun 116, 443–454 (2021). https://doi.org/10.1007/s11277-020-07722-3
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DOI: https://doi.org/10.1007/s11277-020-07722-3