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A Learning-Theory Approach to Sensor Networks
October-December 2003 (vol. 2 no. 4)
pp. 44-49
ASCII Text
x 
 
Slobodan N. Simic, "A Learning-Theory Approach to Sensor Networks," IEEE Pervasive Computing, vol. 2, no. 4, pp. 44-49, October-December, 2003.
 
 
BibTex
x
 
@article{ 10.1109/MPRV.2003.1251168,
author = {Slobodan N. Simic},
title = {A Learning-Theory Approach to Sensor Networks},
journal ={IEEE Pervasive Computing},
volume = {2},
number = {4},
issn = {1536-1268},
year = {2003},
pages = {44-49},
doi = {http://doi.ieeecomputersociety.org/10.1109/MPRV.2003.1251168},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - IEEE Pervasive Computing
TI - A Learning-Theory Approach to Sensor Networks
IS - 4
SN - 1536-1268
SP44
EP49
EPD - 44-49
A1 - Slobodan N. Simic,
PY - 2003
KW - supervised learning
KW - random wireless sensor network
KW - distributed algorithms
VL - 2
JA - IEEE Pervasive Computing
ER -
 
Slobodan N. Simic, University of California, Berkeley

Supervised learning techniques have been applied in many diverse scenarios. They also might provide an effective approach to sensor network applications. A well-known learning-theory algorithm effectively applies to environmental monitoring, tracking of moving objects and plumes, and localization. This research, although preliminary, offers a beneficial perspective for the sensor network community to consider.

Index Terms:
supervised learning, random wireless sensor network, distributed algorithms
Citation:
Slobodan N. Simic, "A Learning-Theory Approach to Sensor Networks," IEEE Pervasive Computing, vol. 2, no. 4, pp. 44-49, Oct.-Dec. 2003, doi:10.1109/MPRV.2003.1251168
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