A taxonomy of wireless micro-sensor network models
ACM SIGMOBILE Mobile Computing and Communications Review
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
The cougar approach to in-network query processing in sensor networks
ACM SIGMOD Record
The design of an acquisitional query processor for sensor networks
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Distributed regression: an efficient framework for modeling sensor network data
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DMSN '04 Proceeedings of the 1st international workshop on Data management for sensor networks: in conjunction with VLDB 2004
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
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MauveDB: supporting model-based user views in database systems
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
A note on efficient aggregate queries in sensor networks
Theoretical Computer Science
Processing approximate aggregate queries in wireless sensor networks
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PRESTO: a predictive storage architecture for sensor networks
HOTOS'05 Proceedings of the 10th conference on Hot Topics in Operating Systems - Volume 10
A system for semantic data fusion in sensor networks
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Toward adaptive query processing in wireless sensor networks
Signal Processing
An adaptive in-network aggregation operator for query processing in wireless sensor networks
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Proceedings of the 28th Annual ACM Symposium on Applied Computing
A framework for processing complex queries in wireless sensor networks
ACM SIGAPP Applied Computing Review
Compression in wireless sensor networks: A survey and comparative evaluation
ACM Transactions on Sensor Networks (TOSN)
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Researches in Wireless Sensor Networks (WSN) have been focused on saving power in sensor nodes. An efficient strategy to achieve this goal is to reduce the amount of data sent through the network, In this work, we propose an efficient strategy that predicts data in WSNs aiming at reducing the data traffic in WSNs and thus maximizing the network lifetime. The proposed prediction strategy, denoted ADAGA-P, is based on a linear regression model, using data acquired from one or several sensors. ADAGA-P is executed in an in-network fashion by several sensors geographically distributed in a WSN. Furthermore, in ADAGA-P the regression model is adjusted dynamically, if any sensor in the WSN senses an "outlier". Experimental results are presented using real data. These results show that the proposed strategy reduces energy consumption in WSNs.