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Contourlet Transform, Particle Swarm Optimization, Differential Evolution, One Class Support Vector Machine, Outlier, Discrete Wavelet Transform, Internet of Things, Wireless Sensor Networks
The rapid development of Smart Cities and the Internet of Thinks (IoT) is largely dependent on data obtained through Wireless Sensor Networks (WSNs). The quality of data gathered from sensor nodes is influenced by abnormalities that happen due to different reasons including, malicious attacks, sensor malfunction or noise related to communication channel. Accordingly, outlier detection is an essential procedure to ensure the quality of data derived from WSNs. In the modern utilizations of WSNs, especially in online applications, the high detection rate for abnormal data is closely correlated with the time required to detect these data. This work presents an investigation of different outlier detection techniques and compares their performance in terms of accuracy, true positive rate, false positive rate, and the required detection time. The investigated algorithms include Particle Swarm Optimization (PSO), Deferential Evolution (DE), One Class Support Vector Machine (OCSVM), K-means clustering, combination of Contourlet Transform and OCSVM (CT-OCSVM), and combination of Discrete Wavelet Transform and OCSVM (DWT-OCSVM). Real datasets gathered from a WSN configured in a local lab are used for testing the techniques. Different types and values of outliers have been imposed in these datasets to accommodate the comparison requirements. The results show that there are some differences in the accuracy, detection rate, and false positive rate of the outlier detections, except K-means clustering which failed to detect outlier in some cases. The required detection time for both PSO and DE is very long as compared with the other techniques meanwhile, the CT-OCSVM and DWT-OCSVM required short time and also they can achieve high performance. On the other hand CT and DWT technique has the ability to compress its used dataset where in this paper, CT can extract much less number of coefficients as compared DWT. This makes CT-OCSVM more efficient to be utilized in detecting outliers in WSNS.
Hussein H. Shia, Mohammed Ali Tawfeeq, Sawsan M. Mahmoud, "High Rate Outlier Detection in Wireless Sensor Networks: A Comparative Study", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.4, pp. 13-22, 2019.DOI: 10.5815/ijmecs.2019.04.02
J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network survey,” Computer Networks, Vol. 52, NO. 12, pp. 2992-2805, 2008.
Y. Zhang, N. Meratnia, and P. Havinga, “outlier detection techniques for wireless sensor networks: A survey,” IEEE Communications Survey &Tutorials, Vol. 12, NO. 2, pp. 159-170, 2010.
I. Krontiris, Z. Benenson, T. Giannetsos, F. Freiling, and T. Dimitriou, “Cooperative intrusion detection in wireless sensor networks,” European Conference on Wireless Sensor Networks, Springer, pp. 263-278, 2009.
R. Jurdak, X.R. Wang, O. Obst, and P. Valencia, “Wireless sensor Network anomalies: Diagnosis and Detection Strategies,” Intelligence-Based Systems Engineering, Springer, pp. 309-325, 2011.
A.H. Farooqi, and F.A. Khan, “Intrusion Detection Systems for Wireless Sensor Networks: A Survey,” Communication and Networking, Springer, pp. 234– 241, 2009.
M. A. Rassam, A. Zainal, and M. A. Maarof, “Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues,” Sensors, vol. 13, No. 8, pp. 10 087–10122, 2013.
S. Siripanadorn, W. Hattagam, and N. Teaumroong, “Anomaly Detection in Wireless Sensor Networks using Self-Organizing Map and Wavelets,” International Journal of Communications, vol. 4, No. 3, pp. 74–83, 2010.
S. Takianngam, and W. Usaha, “Discrete Wavelet Transform and One-Class Support Vector Machine for Anomaly Detection in Wireless Sensor Network,” Intelligent Signal Processing and Communication Systems (ISPACS), IEEE, pp. 1-6, 2011.
Y. Zhang, N.A.S.Hamm, N. Meratnia, A. Stein, M. van de Voort, and P.J.M. Havinga, “Statistics-Based Outlier Detection for Wireless Sensor Networks,” International Journal of Geographical Information Science, vol. 26, No. 8, pp. 1373-1392, 2012.
L.Sahoo, A. R. Prusty, and S. K. Sarangi, “An Outlier Detection and Rectification Method in Cluster Based Wireless Sensor Network,” International Journal of Computer Science and Telecommunications, Vol. 3, No.4, pp. 63-69, 2012.
H.H. Soliman, Noha A. Hikal and Nehal A. Sakr, “A comparative performance evaluation of intrusion detection techniques for hierarchical wireless sensor networks,” Egyptian Informatics Journal, vol. 13, No. 3, pp. 225–238, 2012.
M.A. Rassam, A. Zainal, and M.A. Maarof, “an adaptive and efficient dimension reduction model for multivariate wireless sensor networks applications,” Applied Soft Computing., Vol. 13, pp. 1878-1996, 2013.
Y. Zhang, N. Meratnia, and P.J. Havinga, “Distributed online outlier detection in wireless sensor Networks using ellipsoidal support vector machine,” Ad Hoc Networks, Vol. 11, No. 3, pp. 1062-1074, 2012.
O. Salem, A. Guerassimov, A. Mehaoua, A. Marcus, and B. Furht, “Sensor fault and patient anomaly detection and classification in medical wireless sensor networks,” Communication (ICC). IEEE International. Conference, IEEE, pp. 4373–4378, 2013.
S. Mahmoud, A. Lotfi, and C. Langensiepen, “User activities outliers detection; integration of statistical and computational intelligence techniques,” Computational. Intelligence, Vol. 32. No. 1, pp. 49-71, 2014.
D. Sinwar, and V.S. Dhaka, “Outlier Detection from Multidimensional Space using Multilayer Perceptron, RBF Networks and Pattern Clustering Techniques,” Computer Engineering and Applications (ICACEA), 2015 International Conference on Advances in, IEEE, pp. 573- 579, 2015.
I. Ahmad, “Feature selection using particle swarm optimization in intrusion detection,” International Journal of Distributed Sensor Networks. Vol. 11, No. 10, pp. 806-954, 2015.
S. Kamal, R.A. Ramadan, and E.R. Fawzy, “Smart outlier detection of wireless sensor network,” Facta University, series: Electronics and Energitics, Vol. 29, No. 3, pp. 383–393, 2015.
A. T. C. Andrade, 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,” Industrial Electronics Society (IECON), IEEE, pp. 4683-4688, 2016.
V. Garcia-Font, C. Garrigues, and H.A. Rif-Pous, “A Comparative study of anomaly detection techniques for smart city wireless sensor networks,” sensors, Vol. 16, No. 6, 2016.
P. Gil, H.Martins, F.Januário, “Detection and accommodation of outliers in wireless sensor networks within a multi-agent framework,” Applied. Soft Computing, Vol. 42, pp. 204–214, 2016.
R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,”Micro Machine and Human Science, Proceeding of the sixth international Symposium on, IEEE, pp. 39-43, 1995.
K. Price and R. Storn, “Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, No.4, pp. 341–359, 1997.
D.M.J. Tax and R.P.W. Duin, “Support vector description,” Machine Learning, Vol. 54, No. 1, pp. 45-66, 2004.
M.N. Do and M. Vetterli, “The Contourlet Transform: An Efficient Directional Multiresolution Image Representation,” IEEE Transaction On Image Processing, Vol. 14, No. 12, pp. 2091-2106, 2005.
J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Transactions on Signal Processing, Special Issue on Wavelets and Signal Processing, vol. 41, No. 12, pp. 3445–3462, December 1993.
A. Cohen, I. Daubechies, and J.-C. Feauveau, “Biorthogonal bases of compactly supported Wavelets,” Communication On Pure and Applied Mathmatics., vol. 45, No. 5, pp. 485–560, 1992.
S.-M. Phoong, C. W. Kim, P. P. Vaidyanathan, and R. Ansari, “A new class of two-channel Biorthogonal filter banks and wavelet bases,” IEEE Transaction on Signal Processing, vol. 43, No. 3, pp. 649–665, 1995.