MIST: Distributed Indexing and Querying in Sensor Networks using Statistical Models

Summary: MIST is a distributed in-network index over Markov and Hidden Markov sensor models to answer range, top-1, and 1-NN queries without centralizing all models. It uses average and extreme composite models with subtree-root bounds on observation likelihood and model distance, yielding scalable, lower-communication querying than centralized schemes. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 9636
Venue: VLDB
Year: 2007
Pagerank: 5.1725247e-05
Overall Rank: 12,490 | 13.20%
DOI: -

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 6 of 6 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
2	R-Trees: A Dynamic Index Structure For Spatial Searching	1984	SIGMOD	0.0020323916
55	M-tree: An Efficient Access Method for Similarity Search in Metric Spaces	1997	VLDB	0.0004086718
332	Model-Driven Data Acquisition in Sensor Networks	2004	VLDB	0.00020989244
639	Adaptive Precision Setting for Cached Approximate Values	2001	SIGMOD	0.00015564176
867	Adaptive Stream Resource Management Using Kalman Filters	2004	SIGMOD	0.00013558509
5,198	Energy-Efficient Monitoring of Extreme Values in Sensor Networks	2006	SIGMOD	6.3912028e-05

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