TACO: Tunable Approximate Computation of Outliers in Wireless Sensor Networks

Summary: In-network outlier detection for wireless sensor networks using locality-sensitive hashing with boosting and pruning. It offers tunable accuracy–bandwidth trade-offs and load-balanced, metric-flexible detection for data cleaning. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 4251
Venue: SIGMOD
Year: 2010
Pagerank: 4.1945683e-05
Overall Rank: 12,221 | 14.99%
DOI: -

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,628	Sim-Piece: Highly Accurate Piecewise Linear Approximation through Similar Segment Merging	2023	VLDB	6.0379315e-05

Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
205	Monitoring Streams – A New Class of Data Management Applications	2002	VLDB	0.00034731577
1,594	Adaptive Cleaning for RFID Data Streams	2006	VLDB	0.00011222484
2,629	Online Outlier Detection in Sensor Data Using Non-Parametric Models	2006	VLDB	8.4160309e-05
3,121	Compressing Historical Information in Sensor Networks	2004	SIGMOD	7.5271941e-05
7,522	Efficient and Tunable Similar Set Retrieval	2001	SIGMOD	4.7180617e-05

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