A distributed and adaptive signal processing approach to reducing energy consumption in sensor networks
By Jim Chou, Dragan Petrovic, Kannan Ramchandran. IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428), 2003
313 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/05a65191aa15fd3dddd5ac2076699c78f1f90c95
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
- Martin Reisslein
- Guoliang Xue
- Satyajayant Misra
- Samuel Cheng
- Stefano Galli
- Zhifang Wang
- Zixiang Xiong
- Angelos D. Liveris
In Electrical and Electronic Engineering
- Martin Reisslein
- Guoliang Xue
- Satyajayant Misra
- Samuel Cheng
- Zixiang Xiong
- Angelos D. Liveris
- Michele Zorzi
- Stefano Galli
Citations in the corpus
- Compressive data gathering for large-scale wireless sensor networks (2009, background): “Distributed source coding techniques [9][11][18] intend to reduce complexity at sensor nodes and utilize correlation at the sink.”
- An Energy-Efficient Data Collection Framework for Wireless Sensor Networks by Exploiting Spatiotemporal Correlation (2007, background): “Transferring the model parameters, instead of the raw time series, can significantly decrease the energy consumption on communication.”
- Distributed source coding for sensor networks (2004, methodology): “Research on this application area [5], [12], [41], [43] has just begun.”
- Using mobile relays to prolong the lifetime of wireless sensor networks (2005, methodology): “Data aggregation and clustering methods such as [13, 14, 15] aggregate the sensing data to decrease traffic volume and thereby prolong network lifetime.”
- A survey of multimedia streaming in wireless sensor networks (2008, background): “the side information and the others send the coded information to reduce the rate to the Slepian-Wolf or Wyner-Ziv limit [32].”
- For the Grid and Through the Grid: The Role of Power Line Communications in the Smart Grid (2010, background): “correlation among the sensor data to reduce the information flows and to manage the rise in complexity of routing and processing data [62], [63], [64], [65], [66], [67].”
- In-network aggregation techniques for wireless sensor networks: a survey (2007, background): “In [75], the authors apply Slepian-Wolf coding in its simplest form by proving its effectiveness.”