Distributed data processing in wireless sensor networks based on artificial neural-networks algorithms

Abstract

Most of the current in-network data processing algorithms are modified regression techniques like multidimensional data series analysis. In our opinion, several algorithms developed within the artificial neuralnetworks tradition can be easily adopted to wireless sensor network platforms and will meet the requirements for sensor networks like: simple parallel-distributed computation, distributed storage, data robustness and auto-classification of sensor readings. Lower communication costs and energy savings can be obtained as a consequence of the dimensionality reduction achieved by the neural-networks clustering algorithms, In this paper we will present three possible implementations of the ART and FuzzyART neuralnetworks algorithms, which are unsupervised learning methods for categorization of the sensory inputs. They are tested on a data obtained from a set of several motes, equipped with several sensors each. Results from simulations of deliberately made faulty sensors show the data robustness of these architectures.