A sampling-based approach to optimizing top-k queries in sensor networks

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A sampling-based approach to optimizing top-k queries in sensor networks (2006)

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by Adam Silberstein , Rebecca Braynard , Carla Ellis , Kamesh Munagala , Jun Yang

Venue:	In ICDE
Citations:	53 - 3 self

BibTeX

@INPROCEEDINGS{Silberstein06asampling-based,
    author = {Adam Silberstein and Rebecca Braynard and Carla Ellis and Kamesh Munagala and Jun Yang},
    title = {A sampling-based approach to optimizing top-k queries in sensor networks},
    booktitle = {In ICDE},
    year = {2006}
}

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Abstract

Wireless sensor networks generate a vast amount of data. This data, however, must be sparingly extracted to conserve energy, usually the most precious resource in battery-powered sensors. When approximation is acceptable, a model-driven approach to query processing is effective in saving energy by avoiding contacting nodes whose values can be predicted or are unlikely to be in the result set. However, to optimize queries such as topk, reasoning directly with models of joint probability distributions can be prohibitively expensive. Instead of using models explicitly, we propose to use samples of past sensor readings. Not only are such samples simple to maintain, but they are also computationally efficient to use in query optimization. With these samples, we can formulate the problem of optimizing approximate topk queries under an energy constraint as a linear program. We demonstrate the power and flexibility of our sampling-based approach by developing a series of top-k query planning algorithms with linear programming, which are capable of efficiently producing plans with better performance and novel features. We show that our approach is both theoretically sound and practically effective on simulated and real-world datasets. 1

Keyphrases

sampling-based approach top-k query sensor network real-world datasets approximate topk query novel feature linear program vast amount linear programming wireless sensor network battery-powered sensor top-k query planning algorithm model-driven approach joint probability distribution energy constraint result set precious resource past sensor reading

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