WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks
Cluster Computing
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ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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ASSET '00 Proceedings of the 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology (ASSET'00)
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ISPAN '99 Proceedings of the 1999 International Symposium on Parallel Architectures, Algorithms and Networks
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ACM SIGMOD Record
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Proceedings of the 2004 joint workshop on Foundations of mobile computing
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Artificial Intelligence Review
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PERCOMW '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops
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VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
PAQ: time series forecasting for approximate query answering in sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
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IEEE Journal on Selected Areas in Communications
A mobility-based framework for adaptive clustering in wireless ad hoc networks
IEEE Journal on Selected Areas in Communications
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In this paper, we address the problem of reducing the communication cost and hence the energy costs incurred in data-gathering applications of a sensor network. Environmental data depicts a huge amount of correlation in both the spatial and temporal domains. We exploit these temporal-spatial correlations to address the aforementioned problem. More specifically, we propose a framework that partitions the physical sensor network topology into a number of feature regions. Each sensor node builds a data model that represents the underlying structure of the data. A representative node in each feature region communicates only the model coefficients to the sink, which then uses them to answer queries. The temporal and spatial similarity has special meaning in outlier cleaning too. We use a modified z-score technique to precisely label the outliers and use the spatial similarity to confirm whether the outliers are due to a true change in the phenomenon under study or due to faulty sensor nodes.