Multi-dimensional range queries in sensor networks
By Xin Li, Young-jin Kim, Ramesh Govindan, W. Hong. ACM International Conference on Embedded Networked Sensor Systems, 2003
459 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Signal Processing
View Paper: https://www.semanticscholar.org/paper/0c3e07c5ef9a4abb64ac152e64efaca63b0fd788
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
- Scott Shenker
- Ramesh Govindan
- Ion Stoica
- Sylvia Ratnasamy
- David E. Culler
- Antonio Alfredo Ferreira Loureiro
- Eduardo Freire Nakamura
- Deborah Estrin
In Electrical and Electronic Engineering
- Srinivasan Seshan
- David E. Culler
- Scott Shenker
- Rodrigo Fonseca
- Antonio Alfredo Ferreira Loureiro
- Eduardo Freire Nakamura
- Jerry Zhao
- Alejandro Cesar Orgambide Frery
In Signal Processing
- Ramesh Govindan
- Scott Shenker
- Brad Karp
- Alejandro Cesar Orgambide Frery
- Stefano Chessa
- Antonio Alfredo Ferreira Loureiro
- Ion Stoica
- Eduardo Freire Nakamura
Citations in the corpus
- Mercury: supporting scalable multi-attribute range queries (2004, methodology): “The DIM data structure [16] supports multi-dimensional range queries by embedding them into a two-dimensional space and using a geographic routing algorithm.”
- Beacon vector routing: scalable point-to-point routing in wireless sensornets (2005, background): “These include applications such as PEG (a pursuer-evader game in which a large network tracks the movement of evader robots [4]), approaches such as reactive tasking (commands based on local sensing results), and data query methods such as multi-dimensional range queries [20], spatial range queries[”
- Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards (2007, background): “DIM [73] and KDDCS [4] are alternatives to GHT that recursively divide the network region into zones and map events (defined as attribute values) to binary codes that correspond to zones.”
- Information fusion for wireless sensor networks: Methods, models, and classifications (2007, methodology): “, SQL) to retrieve summarized data in database systems [Madden et al. 2002]. The use of data aggregation in WSNs and its impact on energy consumption is the subject for further research. Krishnamachari et al. [2002] provide theoretical results regarding the NP-completeness related to the formation o”
- Geographic routing made practical (2005, background): “More recently, geographic routing algorithms have been proposed for use as a routing primitive for static sensor networks, as building blocks for data storage and flexible query processing in sensor networks [20, 23], and even”