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Existing in-network query processing techniques are categorized as approximation and aggregation based approaches, where the former achieves lower network traffic at the expense of query response accuracy, whereas the later reduces query response inaccuracy by executing queries at the actual sensor nodes which necessitates the overhead of query specific sensor selection mechanism. In this paper, the authors propose a hybrid query processing framework that combines the advantages of both the approximation and aggregation based techniques and avoids their limitations. In their approach, they construct a hierarchical probabilistic data model representing the overall sensor data characteristics across the network, which is query independent and is later used for selecting sensor nodes to process user queries.
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