N. Chitradevi
ABSTRACT
Recent technology in wireless communication has enabled the development of low-cost sensor networks. Sensors at different locations can generate streaming data, which can be analyzed in real-time to identify events of interest. Wireless sensor networks (WSNs) usually have limited energy and transmission capacity, which cannot match the transmission of a large number of data collected by sensor nodes. So, it is necessary to perform in-network data aggregation in the WSN which is performed by aggregator node. Since, the nodes in WSN are vulnerable to malicious attackers and physical impairment; the data collected in WSNs may be unreliable. So, in this paper, we propose an efficient model based technique to detect the unreliable data. Data model is designed using the sound statistical multivariate technique called Principal Component Analysis (PCA). But as a drawback, it is not robust to outliers. Hence, if the input data is corrupted, an arbitrarily wrong representation is obtained. To overcome this problem, we propose two approaches namely Minimum Volume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) to design robust PCA which aids in design of a noise-free data model. The performance of proposed approach is evaluated and compared with previous approaches and found that our approach is effective and efficient.
- B. Przydatek, D. Song, and A. Perrig, "SIA: Secure information aggregation in sensor networks," in Proc. 1st Int. Conf. Embedded Networked Sensor Syst., 2003, pp. 255--265. Google Scholar
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- Chitradevi, N.; Palanisamy, V.; Baskaran, K.; Nisha, U. B.; "Outlier aware Data Aggregation in Distributed Wireless Sensor Network using Robust Principal Component Analysis" Computing Communication and Networking Technologies (ICCCNT), 2010 IEEE International Conference, pp: 1--9, 2010.Google Scholar
- E. Shi and A. Perrig "Designing secure sensor networks," Wireless Commun. vol. 11, no. 6, pp. 38--43, Dec 2004 Google ScholarDigital Library
- Fenxiong Chen; Fei Wen; Hongdong Jia;" Algorithm of Data Compression Based on Multiple Principal Component Analysis over the WSN" Wireless Communications Networking and Mobile Computing (WiCOM), IEEE Conference pp: 1--4, 2010.Google Scholar
- F. Y. Luo, H. S. Lu, and L. Zhang, "Statistical en-route filtering of injected false data in sensor networks," IEEE J. Sel. Areas Commun., vol. 23, no. 4, pp. 839--850, Apr. 2005. Google ScholarDigital Library
- G. Wang, W. Zhang, G. Cao, and T. La Porta, "On supporting distributed collaboration in sensor networks," in Proc. IEEE MilitaryCommun. Conf., Oct. 2003, vol. 2, pp. 752--757. Google ScholarDigital Library
- Harkat M. F., Mourot G. and Ragot J., "Sensor Failure Detection of Air Quality Monitoring Network", IFAC Symposium on Fault Detection, SAFERPROCESS, Hungary, June 14--16, 2000.Google ScholarCross Ref
- I. T. Jollife, Principal Component Analysis, 2nd ed. New York: Springer, 2002.Google Scholar
- J. E. Jackson and G. S. Mudholkar, "Control procedures for residuals associated with principal component analysis," Technometrics, pp. 341--349, 1979.Google ScholarCross Ref
- J. E. Jackson, A User's Guide to Principal Components. New York:Wiley, 2003.Google Scholar
- J. Newsome, E. Shi, D. Song, and A. Perrig, "The sybil attack in sensor networks: Analysis & defenses," in Proc. 3rd Int. Symp. Inf. Process. Sensor Netw., 2004, pp. 259--268. Google ScholarDigital Library
- Livani, M. A.; Abadi, M, "A PCA-based Distributed Approach for Intrusion Detection in Wireless Sensor Networks" Computer Networks and Distributed Systems (CNDS), International Symposium, IEEE, 2011, pp: 55--60, 2011.Google Scholar
- M. C. Vuran, B. Akan, and I. F. Akyildiz, "Spatio-temporal correlation: Theory and applications for wireless sensor networks," Compute Networks:Int. J. Comput. Telecommun. Netw., vol. 45, no. 3, 2004. Google ScholarDigital Library
- P.J. Rousseeuw, K. Van Driessen, A fast algorithm for minimum covariance determinant estimator, Technometrics 41 (1999) pp:212--223 Google ScholarDigital Library
- R. Dunia and S. J. Qin, "A subspace approach to multidimensional fault identification and reconstruction," Amer. Inst. Chem. Eng. J., pp. 1813--1831, 1998.Google ScholarCross Ref
- Rooshenas, A.; Rabiee, H. R.; Movaghar, A.; Naderi, M. Y.; "Reducing the data transmission in wireless sensor networks using the Principal Component Analysis" Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp: 133--138, 2011.Google Scholar
- Rousseeuw, P. J., and Leory, A. M. "Robust Regression and Outlier Detection", New York: Wiley, 1987. Google ScholarDigital Library
- Rousseeuw, P. J, "Multivariate estimation with hig breakdown point", In Matematical Statistics and Applications, pp: 283--297, 1985.Google ScholarCross Ref
- S. Pattem, B. Krishnmachari, and R. Govindan, "The Impact of spatial correlation on routing with compression in wireless sensor networks," in Proc. 3rd Int.Symp.Inf. Processi.SensorNetw., 2004, pp. 28--35. Google ScholarDigital Library
- S. Tanachaiwiwat and A. Helmy, "Correlation analysis for alleviating effects of inserted data in wireless sensor networks," in Proc. Mobileand Ubiquitous Syst.: Networking Services, 2005, pp. 97--108. Google ScholarDigital Library
- Silva, O.; Aquino, A. L. L.; Mini, R. A. F.; Figueiredo, C. M. S.; "Multivariate Reduction in Wireless Sensor Networks" Computers and Communications, IEEE Symposium pp: 726--729, 2009Google Scholar
- T. Palpamas, "Distributed deviation detection in sensor networks," in Proc.ACM SIGMOD, 2003, vol. 32. Google ScholarDigital Library
- V. Chatzigiannakis and S. Papavassiliou, "Diagnosing anomalies and identifYing faulty nodes in sensor networks," IEEE Sensors Journal, vol. 7, no. 5, pp. 637--645, May 2007.Google ScholarCross Ref
- V. Chatzigiannakis, S. Papavassiliou, Grammatikou, Maglaris, "Hierarchical Anomaly Detection in Distributed Large-scale Sensor Networks" Computers and Communications, IEEE Conference, 2006, pp:761--767. Google ScholarDigital Library
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