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: 9635
Venue: VLDB
Year: 2007
Pagerank: 4.1945683e-05
Overall Rank: 12,481 | 13.18%
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	0.0032169493
91	M-tree: An Efficient Access Method for Similarity Search in Metric Spaces	1997	VLDB	0.0005181666
477	Model-Driven Data Acquisition in Sensor Networks	2004	VLDB	0.00022221803
750	Adaptive Precision Setting for Cached Approximate Values	2001	SIGMOD	0.00017220819
1,045	Adaptive Stream Resource Management Using Kalman Filters	2004	SIGMOD	0.00014472777
5,488	Energy-Efficient Monitoring of Extreme Values in Sensor Networks	2006	SIGMOD	5.4782627e-05

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