Paper Info
Title
Robust and computationally efficient diffusion-based classification in distributed networks
Abstract
Today's wireless sensor networks provide the possibility to monitor physical environments via small low-cost wireless devices. Given the large amount of sensed data, efficient and robust classification becomes a critical task in many applications. Typically, the devices must operate under stringent power and communication constraints and the transmission of observations to a fusion center (FC) is, in many cases, infeasible or undesired. A challenging research question in such cases is the design of data clustering and classification rules when each sensor collects a set of unlabelled observations that are drawn from a known number of classes. We propose two robust distributed hybrid classification algorithms, i.e., the Diffusion K-Medians and the Communicationally Efficient Distributed K-Medians. An extensive performance analysis in comparison to a benchmark algorithm is provided that investigates the error rates in dependence of different parameters of a distributed sensor network, and also considers communication cost. Our proposed algorithms, which are insensitive to outliers and various parameters, are applicable to on-line classification problems and scale well w.r.t. the number of classes.
Year
DOI
Venue
2015
10.1109/ICASSP.2015.7178608
IEEE International Conference on Acoustics, Speech and SP
Keywords
Field
DocType
K-medians, diffusion, distributed classification, robust, outlier
Data mining,Algorithm design,Pattern recognition,Computer science,Brooks–Iyengar algorithm,Robustness (computer science),Fusion center,Artificial intelligence,Cluster analysis,Statistical classification,Wireless sensor network,Benchmark (computing)
Conference
ISSN
Citations 
PageRank 
1520-6149
5
0.46
References 
Authors
10
3
Name
Order
Citations
PageRank
patricia binder1161.65
Michael Muma214419.51
Abdelhak M. Zoubir31036148.03