Title :
Distributed learning in wireless sensor networks
Author :
Predd, Joel B. ; Kulkarni, Sanjeev R. ; Poor, H. Vincent
Author_Institution :
Princeton Univ., NJ, USA
fDate :
7/1/2006 12:00:00 AM
Abstract :
This paper discusses nonparametric distributed learning. After reviewing the classical learning model and highlighting the success of machine learning in centralized settings, the challenges that wireless sensor networks (WSN) pose for distributed learning are discussed, and research aimed at addressing these challenges is surveyed.
Keywords :
learning (artificial intelligence); reviews; telecommunication computing; wireless sensor networks; distributed inference; machine learning; nonparametric distributed learning; wireless sensor networks; Bandwidth; Delay estimation; Intelligent networks; Machine learning; Parametric statistics; Robustness; Sensor phenomena and characterization; Signal design; Statistical distributions; Wireless sensor networks;
Journal_Title :
Signal Processing Magazine, IEEE
DOI :
10.1109/MSP.2006.1657817
