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22476 Distribution Bottlenecks in Classification Algorithms
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Zwartjes, G.J. and Havinga, P.J.M. and Smit, G.J.M. and Hurink, J.L. (2012) Distribution Bottlenecks in Classification Algorithms. In: Second International Symposium on Frontiers in Ambient and Mobile Systems (FAMS-2012) , 27-29 Aug 2012, Niagara Falls, Canada. pp. 960-967. Procedia Computer Science 10. Elsevier. ISSN 1877-0509

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Official URL: http://dx.doi.org/10.1016/j.procs.2012.06.131

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Abstract

The abundance of data available on Wireless Sensor Networks makes online processing necessary. In industrial applications for example, the correct operation of equipment can be the point of interest while raw sampled data is of minor importance. Classification algorithms can be used to make state classifications based on the available data. The distributed nature of Wireless Sensor Networks is a complication that needs to be considered when implementing classification algorithms. In this work, we investigate the bottlenecks that limit the options for distributed execution of three widely used algorithms: Feed Forward Neural Networks, naive Bayes classifiers and decision trees. By analyzing theoretical boundaries and using simulations of various network topologies, we show that the naive Bayes classifier is the most flexible algorithm for distribution. Decision trees can be distributed efficiently but are unpredictable. Feed Forward Neural Networks show severe limitations.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-CAES: Computer Architecture for Embedded Systems, EWI-PS: Pervasive Systems, EWI-DMMP: Discrete Mathematics and Mathematical Programming
Research Program:CTIT-WiSe: Wireless and Sensor Systems
Research Project:Real Time Enterprise
ID Code:22476
Status:Published
Deposited On:08 November 2012
Refereed:Yes
International:Yes
More Information:statisticsmetis

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