Tributaries and deltas: efficient and robust aggregation in sensor network streams
By Amit Manjhi, Suman Nath, Phillip B. Gibbons. 2005
286 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Dawn Song
- Adrian Perrig
- Michele Zorzi
- J. Widmer
- Michele Rossi
- Dina Katabi
- Phillip B. Gibbons
- Elena Fasolo
In Computer Networks and Communications
- Adrian Perrig
- Dawn Song
- Jean-Pierre Hubaux
- Haowen Chan
- Dina Katabi
- Hariharan Rahul
- Omid Salehi-Abari
- Phillip B. Gibbons
In Signal Processing
- Dawn Song
- Adrian Perrig
- Dina Katabi
- Phillip B. Gibbons
- Zachary R. Anderson
- Suman Nath
- Srinivasan Seshan
- Li Xiong
Citations in the corpus
- Over-the-air Function Computation in Sensor Networks (2016, background): “(a) Aggregated Wireless Sensor Networks : Most data aggregation work can be classified as: tree-based [17, 26, 16, 13], cluster-based [12, 27, 30, 18] or multipath [20, 19, 7].”
- Secure hierarchical in-network aggregation in sensor networks (2006, background): “The energy savings of performing in-network aggregation have been shown to be signi.cant and are crucial for energy-constrained sensor networks [9, 11, 20].”
- In-network aggregation techniques for wireless sensor networks: a survey (2007, methodology): “In doing so, the following rules have to be satisfied [ 9 ]: • Edge correctness: An edge originating from an”
- Synopsis diffusion for robust aggregation in sensor networks (2004, methodology): “Our initial results include a hybrid topology called Tributary-Delta [Manjhi et al. 2005], which runs both synopsis diffusion and tree-based schemes simultaneously in different regions of the network, depending on current loss conditions.”