Model-based approximate querying in sensor networks

@article{citeulike:1606210, abstract = {Abstract\ \ Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that “the sensornet is a database” is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this article, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network datasets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques.}, author = {Deshpande, Amol and Guestrin, Carlos and Madden, Samuel and Hellerstein, Joseph and Hong, Wei }, citeulike-article-id = {1606210}, doi = {10.1007/s00778-005-0159-3}, journal = {The VLDB Journal The International Journal on Very Large Data Bases}, month = {November}, number = {4}, pages = {417--443}, posted-at = {2007-08-29 22:17:23}, priority = {2}, title = {Model-based approximate querying in sensor networks}, url = {http://dx.doi.org/10.1007/s00778-005-0159-3}, volume = {14}, year = {2005} }

TY - JOUR ID - citeulike:1606210 L3 - citeulike-article-id:1606210 TI - Model-based approximate querying in sensor networks JF - The VLDB Journal The International Journal on Very Large Data Bases VL - 14 IS - 4 SP - 417 EP - 443 N2 - Abstract  Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that “the sensornet is a database” is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this article, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network datasets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques. AU - Deshpande, Amol AU - Guestrin, Carlos AU - Madden, Samuel AU - Hellerstein, Joseph AU - Hong, Wei PY - 2005/11/23/ UR - http://dx.doi.org/10.1007/s00778-005-0159-3 ER -