A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks

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. To optimize queries such as top-k, however, 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 top-k 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 topk query planning...

• Type: Conference
• Year: 2006
• Where: ICDE
• Authors: Adam Silberstein, Carla Schlatter Ellis, Jun Yang 0001, Kamesh Munagala, Rebecca Braynard
• Keywords: Database, ICDE 2006, Past Sensor Readings, Query Planning Algorithms, Wireless Sensor Networks