The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Distributed deviation detection in sensor networks
Abstract
Sensor networks have recently attracted much attention, because of their potential applications in a number of different settings. The sensors can be deployed in large numbers in wide geographical areas, and can be used to monitor physical phenomena, or to detect certain events.An interesting problem which has not been adequately addressed so far is that of distributed online deviation detection in streaming data. The identification of deviating values provides an efficient way to focus on the interesting events in the sensor network.In this work, we propose a technique for online deviation detection in streaming data. We discuss how these techniques can operate efficiently in the distributed environment of a sensor network, and discuss the tradeoffs that arise in this setting. Our techniques process as much of the data as possible in a decentralized fashion, so as to avoid unnecessary communication and computational effort.
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Citations
Cites
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335388 · Cited by 4,149
Efficient algorithms for mining outliers from large data sets
Ramaswamy, S., Rastogi, R., & Shim, K. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335437 · Cited by 1,171
Cited by
Sensor network security: a survey
Chen, X., Makki, K., Yen, K., & Pissinou, N. (2009) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2009.090205 · Cited by 599
Anomaly detection in wireless sensor networks: A survey
Xie, M., Han, S., Tian, B., & Parvin, S. (2011) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2011.03.004 · Cited by 355
Ding, Z., & Fei, M. (2013) · IFAC Proceedings Volumes · doi:10.3182/20130902-3-cn-3020.00044 · Cited by 321
Open challenges for data stream mining research
Krempl, G., Žliobaite, I., Brzeziński, D., Hüllermeier, E., Last, M., Lemaire, V., Noack, T., Shaker, A., Sievi, S., Spiliopoulou, M., et al. (2014) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2674026.2674028 · Cited by 295
Metadata
- Authors
- Palpanas, T., Papadopoulos, D., Kalogeraki, V., & Gunopulos, D.
- Venue
- ACM SIGMOD Record
- Published
- 2003-12
- Volume
- 32
- Issue
- 4
- Pages
- 77-82
- ISSN
- 0163-5808 (ISSN-L)
- OpenAlex
- W2092952332
- Cited by
- 174
- References
- 3
- FWCI
- 4.59
- Type
- journal article
- Language
- en
- Open access
- closed
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- Palpanas, T., Papadopoulos, D., Kalogeraki, V., & Gunopulos, D. (2003). Distributed deviation detection in sensor networks. ACM SIGMOD Record. https://doi.org/10.1145/959060.959074
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