Streaming analysis in wireless sensor networks

Wireless Communications and Mobile Computing

Volumn 14, Issue 9, 2014, Pages 905-921

  Moshtaghi, Masud ^a   Bezdek, James C ^a   Havens, Timothy C ^b   Leckie, Christopher ^a   Karunasekera, Shanika ^a   Rajasegarar, Sutharshan ^a   Palaniswami, Marimuthu ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

^b MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

anomaly detection; environmental monitoring; temporal chains; temporal data; wireless sensor networks

Indexed keywords

DATA HANDLING; ENVIRONMENTAL ENGINEERING; SENSORS;

ANOMALY DETECTION; CYCLIC VARIATIONS; ENVIRONMENTAL MONITORING; GRACEFUL DEGRADATION; INCREMENTAL MODELS; ONLINE ANOMALY DETECTION; SEASONAL CHANGES; TEMPORAL DATA;

WIRELESS SENSOR NETWORKS;

EID: 84902376082     PISSN: 15308669     EISSN: 15308677     Source Type: Journal    
DOI: 10.1002/wcm.2248     Document Type: Article

References (36)

1. Chandola V, Banerjee A, Kumar V,. Anomaly detection: a survey. ACM Computing Surveys 2009; 41 (3): Article 15: 1-48.
2. Djenouri D, Khelladi L, Badache A,. A survey of security issues in mobile ad hoc and sensor networks. IEEE Communications Surveys 2005; 7 (4): 2-28.
3. Bhuse V, Gupta A,. Anomaly intrusion detection in wireless sensor networks. Journal of High Speed Networks 2006; 15: 33-51. (Pubitemid 43221071)
4. Subramaniam S, Palpanas T, Papadopoulos D, Kalogeraki V, Gunopulos D,. Online outlier detection in sensor data using nonparametric models. 32nd International Conference on Very Large DataBases, Seoul, Korea, 2006; 187-198.
5. Dereszynski EW, Dietterich TG,. Spatiotemporal models for data-anomaly detection in dynamic environmental monitoring campaigns. ACM Transactions on Sensor Networks 2011; 8 (1): 1-36.
6. Rajasegarar S, Leckie C, Palaniswami M,. Detecting data anomalies in wireless sensor networks. In Security in Ad-hoc and Sensor Networks, Beyah R, McNair J, Corbett C, (eds). World Scientific Publishing Inc, 2009; 231-260.
7. Chong C, Kumar S,. Sensor networks: evolution, opportunities, and challenges. Proceedings of IEEE 2003; 91: 1247-1256. (Pubitemid 40890834)
8. Hodge V, Austin J,. A survey of outlier detection methodologies. Artificial Intelligence Review 2004; 22 (2): 85-126.
9. Rajasegarar S, Leckie C, Palaniswami M,. Anomaly detection in wireless sensor networks. IEEE Wireless Communications 2008; 15 (4): 34-40.
10. Moshtaghi M, Rajasegarar S, Leckie C, Karunasekera S,. Anomaly detection by clustering ellipsoids in wireless sensor networks. Fifth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 09), Melbourne Australia, 2009; 331-336.
11. Onat I, Miri A,. An intrusion detection system for wireless sensor networks. Proc. IEEE International Conference on Wireless And Mobile Computing, Networking And Communications, Hawaii, USA, 2005; 253-259.
12. Sheng B, Li Q, Mao W, Jin W,. Outlier detection in sensor networks. Proceedings of the 8th ACM International Symposium on Mobile Ad-Hoc Networking and Computing, Montreal, Canada, 2007; 219-228.
13. Rajasegarar S, Bezdek JC, Leckie C, Palaniswami M,. Elliptical anomalies in wireless sensor networks. ACM Transactions on Sensor Networks 2009; 6 (1): 1550-1579.
14. Rajasegarar S, Leckie C, Palaniswami M, Bezdek JC,. Quarter sphere based distributed anomaly detection in wireless sensor networks. Proc. IEEE International Conference on Communication Systems, Glasgow, Scotland, 2007; 3864-3869. (Pubitemid 351146132)
15. Rajasegarar S, Shilton A, Leckie C, Kotagiri R, Palaniswami M,. Distributed training of multiclass conic-segmentation support vector machines on communication constrained networks. Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Brisbane, Australia, 2010; 211-216.
16. Rajasegarar S, Leckie C, Bezdek JC, Palaniswami M,. Centered hyperspherical and hyperellipsoidal one-class support vector machines for anomaly detection in sensor networks. IEEE Transactions on Information Forensics and Security 2010; 5 (3): 518-533.
17. Zhang Y, Meratnia N, Havinga P,. Adaptive and online one-class support vector machine-based outlier detection techniques for wireless sensor networks. International Conference on Advanced Information Networking and Applications Workshops, Bradford, UK, 2009; 990-995.
18. th WSEAS International Conference on Applied Computer Science, Iwate, Japan, 2010; 291-297.
19. Moshtaghi M, Havens TC, Bezdek JC, Park L, Leckie C, Rajasegarar S, Keller JM, Palaniswami M,. Clustering ellipses for anomaly detection. Pattern Recognition 2011; 44: 55-69.
20. Bezdek JC, Rajasegarar S, Moshtaghi M, Leckie C, Palaniswami M, Havens TC,. Anomaly detection in Environmental Monitoring Networks. Computational Intelligence Magazine 2011; 6 (2): 52-58.
21. Basseville M, Nikiforov IV,. Detection of Abrupt Change Theory and Application. Prentice Hall: Upper Saddle River, NJ, USA, 1993.
22. Lee HM, Mao CH,. Finding abnormal events in home sensor network environment using correlation graph. Proceedings of the 2009 IEEE International Conference on Systems, Man and Cybernetics, Texas, USA, 2009; 1852-1856.
23. Page ES,. Continuous inspection schemes. Biometrika 1954; 41 (1/2): 100-115.
24. Ross GJ, Tasoulis DK, Adams NM,. Online annotation and prediction for regime switching data streams. ACM Symposium on Applied Computing, Hawaii, USA, 2009; 1501-1505.
25. Gustafsson F,. The marginalized likelihood ratio test for detecting abrupt changes. IEEE Transactions on Automatic Control 1996; 41 (1): 66-78. (Pubitemid 126768439)
26. Branch J, Szymanski B, Giannella C, Wolff R, Kargupta H,. In-network outlier detection in wireless sensor networks. Proceedings of the 26th IEEE International Conference on Distributed Computing Systems (ICDCS 2006), Lisboa, Portugal, 2006.
27. Theodoridis S, Koutroumbas K,. Pattern Recognition, 5th ed. Academic Press: NY, 2010.
28. Frigui H, Krishnapuram R,. (1996). A comparison of fuzzy shell clustering methods for the detection of ellipses. IEEE Transactions on Fuzzy Systems 1996; 4: 193-199. (Pubitemid 126780548)
29. Duda RO, Hart PE, Stork DG,. Pattern Classification, 2nd ed. Wiley-Interscience: New York, NY, USA, 2000.
30. Ljung L,. System Identification: Theory for the User. Prentice Hall: Upper Saddle River, NJ, USA, 1999.
31. Henderson HV, Searle SR,. On deriving the inverse of a sum of matrices. SIAM Review 1981; 23 (1): 53-60.
32. Farebrother RW,. Linear Least Squares Computations. Marcel Dekker: New York, 1988.
33. IBRL-Web, 2006. http://db.lcs.mit.edu/labdata/labdata.html.
34. SensorScope Web, 2009. http://sensorscope.epfl.ch/index.php.
35. Brown RL, Durbin J, Evans JM,. Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society 1975; Series B (37/2): 149-192.
36. Han J, Kamber M,. Data Mining: Concepts and Techniques, 2nded. Elsevier: San Francisco, CA, USA, 2006.