Distribution Bottlenecks in Classification Algorithms

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. Classi﬿cation algorithms can be used to make state classi﬿cations based on the available data. The distributed nature of Wireless Sensor Networks is a complication that needs to be considered when implementing classi﬿cation 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 classi﬿ers and decision trees. By analyzing theoretical boundaries and using simulations of various network topologies, we show that the naive Bayes classi﬿er is the most flexible algorithm for distribution. Decision trees can be distributed efficiently but are unpredictable. Feed Forward Neural Networks show severe limitations