Synopsis data structures for massive data sets
External memory algorithms
Dynamic multidimensional histograms
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Continuous queries over data streams
ACM SIGMOD Record
Histogramming Data Streams with Fast Per-Item Processing
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
The Impact of Data Aggregation in Wireless Sensor Networks
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Supporting Aggregate Queries Over Ad-Hoc Wireless Sensor Networks
WMCSA '02 Proceedings of the Fourth IEEE Workshop on Mobile Computing Systems and Applications
Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Approximate Aggregation Techniques for Sensor Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Beyond average: toward sophisticated sensing with queries
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Information storage, reduction and dissemination in sensor networks: a survey
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
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The in--network aggregation paradigm in sensor networks provides a versatile approach for evaluating aggregate queries. Traditional approaches need a separate aggregate to be computed and communicated for each query and hence do not scale well with the number of queries. Since approximate query results are sufficient for many applications, we use an alternate approach based on summary data--structures. We consider two kinds of aggregate queries: value range queries that compute the number of sensors that report values in the given range, and location range queries that compute the sum of values reported by sensors in a given location range. We construct summary data--structures called linear sketches, over the sensor data using in--network aggregation and use them to answer aggregate queries in an approximate manner at the base--station. There is a trade--off between accuracy of the query results and lifetime of the sensor network that can be exploited to achieve increased lifetimes for a small loss in accuracy. Experimental results show that linear sketching achieves significant improvements in lifetime of sensor networks for only a small loss in accuracy of the queries. Further, our approach achieves more accurate query results than the other classical techniques using Discrete Fourier Transform and Discrete Wavelet Transform.