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Synopsis Diffusion for Robust Aggregation in Sensor Networks

URL: https://doi.org/10.1145/1031495.1031525

Bibtex Entry:

@inproceedings{2004-Nath-sensys, author = “Nath, Suman and Gibbons, Phillip B. and Seshan, Srinivasan and Anderson, Zachary”, title = “Synopsis Diffusion for Robust Aggregation in Sensor Networks”, year = “2004”, isbn = “1581138792”, publisher = “Association for Computing Machinery”, address = “New York, NY, USA”, url = “https://doi.org/10.1145/1031495.1031525”, doi = “10.1145/1031495.1031525”, abstract = “Previous approaches for computing duplicate-sensitive aggregates in sensor networks (e.g., in TAG) have used a tree topology, in order to conserve energy and to avoid double-counting sensor readings. However, a tree topology is not robust against node and communication failures, which are common in sensor networks. In this paper, we present synopsis diffusion, a general framework for achieving signi.cantly more accurate and reliable answers by combining energy-efficient multi-path routing schemes with techniques that avoid double-counting. Synopsis diffusion avoids double-counting through the use of order- and duplicate-insensitive (ODI) synopses that compactly summarize intermediate results during in-network aggregation. We provide a surprisingly simple test that makes it easy to check the correctness of an ODI synopsis. We show that the properties of ODI synopses and synopsis di.usion create implicit acknowledgments of packet delivery. We show that this property can, in turn, enable the system to adapt message routing to dynamic message loss conditions, even in the presence of asymmetric links. Finally, we illustrate, using extensive simulations, the significant robustness, accuracy, and energy-efficiency improvements of synopsis diffusion over previous approaches.”, booktitle = “ACM Conference on Embedded Networked Sensor Systems (SenSys)”, pages = “250–262”, numpages = “13”, keywords = “sensor networks, synopsis diffusion, query processing”, location = “Baltimore, MD, USA”, month = “November”, category = “IrisNet”, series = “SenSys ‘04” }

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