Approximate Data Collection in Sensor Networks using Probabilistic Models
By David C. Chu, Amol Deshpande, Joseph M Hellerstein, W. Hong. IEEE International Conference on Data Engineering, 2006
541 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Signal Processing
View Paper: https://www.semanticscholar.org/paper/5bcc5ffe048bf8c449897a4ba17b4e902e7848d7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Marco Conti
- Mario Di Francesco
- Giuseppe Pio Anastasi
- Andrea Passarella
- Daniela Tulone
- Samuel Madden
- C. Jermaine
- Peter J. Haas
In Signal Processing
- Mario Di Francesco
- Andrea Passarella
- Giuseppe Pio Anastasi
- Marco Conti
- Samuel Madden
- C. Jermaine
- Daniela Tulone
- Peter J. Haas
Citations in the corpus
- PAQ: Time Series Forecasting for Approximate Query Answering in Sensor Networks (2006, methodology): “As in PAQ, [12] [5] [13] rely on a combination of local and global probabilistic models which are kept in synch to reduce communication between sensor nodes and the network sink.”
- MCDB: a monte carlo approach to managing uncertain data (2008, methodology): “It is possible to handle continuous distributions using the exact method, and relevant proposals exist [7, 10].”
- Energy conservation in wireless sensor networks: A survey (2009, methodology): “The Ken solution [27] well exemplifies this approach.”