Unsupervised outlier detection in sensor networks using aggregation tree

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4632 LNAI, Issue , 2007, Pages 158-169

  Zhang, Kejia ^a   Shi, Shengfei ^a   Gao, Hong ^a   Li, Jianzhong ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DATA TRANSFER; MESSAGE PASSING; TREES (MATHEMATICS); USER INTERFACES; DATA HANDLING; FORESTRY; QUERY PROCESSING; SENSOR NETWORKS;

AGGREGATION TREES; MULTI DIMENSIONAL DATA; APPROXIMATE RESULTS; CANDIDATE SETS; CONTINUOUS QUERIES; MULTIDIMENSIONAL DATA; OUTLIER DETECTION; SNAPSHOT QUERIES; UNSUPERVISED APPROACHES;

SENSOR NETWORKS; STATISTICS;

AGGLOMERATION; NETWORKS; TREES;

EID: 38049044112     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-73871-8_16     Document Type: Conference Paper

References (16)

1. Shnayder, V., Hempstead, M., Chen, B.R., Allen, G.W., Welsh, M.: Simulating the Power Consumption of Large-scale Sensor Network Applications. In: SenSys (2004)
2. Gupta, P., Kumar, P.R.: The Capacity of Wireless Networks. IEEETrans. Information Theory 46(2), 388-404 (2000)
3. Warneke, B., Last, M., Liebowitz, B., Pister, K.: Smart Dust: Communicating with A Cubic-millimeter Computer. IEEE Computer Magazine, pp. 44-51 (January 2001)
4. Gunopulos, D., Kollios, G., Tsotras, J., Domeniconi, C: Approximating Multi-Dimensional Aggregate Range Queries over Real Attributes. In: SIGMOD (2000)
5. Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: The Design of An Acquisitional Query Processor for Sensor Networks. In: SIGMOD (2003)
6. Ramaswamy, S., Rastogi, R., Shim, K.: Efficient Algorithms for Mining Outliers from Large Datasets. In: SIGMOD (2000)
7. Knorr, E., Ng, R.: Algorithms for Mining Distance-Based Outliers in Large Datasets. In: VLDB, 24-27 (1998)
8. Branch, J., Szymanski, B., Giannella, C., Wolff, R.: In-Network Outlier Detection in Wireless Sensor Networks. In: ICDCS (2006)
9. Subramaniam, S., Palpanas, T., Papadopoulos, D., Kalogeraki, V., Gunopulos, D.: Online Outlier Detection in Sensor Data Using Non-Parametric Models. In: VLDB (2006)
10. Palpanas, T., Papadopoulos, D., Kalogeraki, V., Gunopulos, D.: Distributed Deviation Detection in Sensor Networks. ACM SIGMOD 32(4), 77-82 (2003)
11. Zhuang, Y., Chen, L.: In-network Outlier Cleaning for Data Collection in Sensor Networks. In: CleanDB (2006)
12. Intel Berkeley Research Lab. http://db.lcs.mit.edu/labdata/labdata.html
13. Crossbow Technology Inc. http://www.xbow.com/
14. Ash, J.N., Moses, R.L.: Outlier Compensation in Sensor Network Self-localization via the EM Algorithm. In: ICASSP (2005)
15. Jun, M.C., Jeong, H., Kuo, C.J.: Distributed Spatio-temporal Outlier Detection in Sensor Networks. In: SPIE (2005)
16. Janakiram, D., Reddy, A.M., Kumar, A.P.: Outlier Detection in Wireless Sensor Networks using Bayesian Belief Networks. In: Comsware (2006)