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
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Predictive filtering: a learning-based approach to data stream filtering
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
Proceedings of the 3rd international conference on Embedded networked sensor systems
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
Information Systems
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
Proceedings of the 2007 inaugural international conference on Distributed event-based systems
Toward adaptive query processing in wireless sensor networks
Signal Processing
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Over the past few years, many research works in Wireless Sensor Networks (WSN) have been focusing on node power saving. In order to achieve this goal, the amount of data sent over the node network is usually reduced. In this work, we propose an efficient strategy that aggregates and predicts data in WSN, aiming to reduce the data volume sent over the network and thus maximizing the network lifetime. Besides the widely used in-network aggregation strategy, this work presents the use of in-network prediction, based on query processing on the network data. Our prediction strategy works with a linear regression model, using data acquired from one or several sensor nodes. It is implemented in various sensor nodes distributed in a WSN. Experimental results show that our strategy is able to significantly reduce power consumption in WSN.