Distributed deviation detection in sensor networks
Authors:
Open Access
- 1 December 2003
- journal article
- Published by Association for Computing Machinery (ACM) in ACM SIGMOD Record
- Vol. 32 (4) , 77-82
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.Keywords
This publication has 12 references indexed in Scilit:
- Efficient biased sampling for approximate clustering and outlier detection in large data setsIEEE Transactions on Knowledge and Data Engineering, 2003
- Correlating synchronous and asynchronous data streams2003
- Maintaining variance and k-medians over data stream windows2003
- ESRT2003
- On computing correlated aggregates over continual data streams2001
- Directed diffusion2000
- Approximating multi-dimensional aggregate range queries over real attributes2000
- Efficient algorithms for mining outliers from large data sets2000
- LOF2000
- Statistics for Spatial Data1993