Tracking of unusual events in wireless sensor networks based on artificial neural-networks algorithms

Abstract

Some of the algorithms developed within the artificial neural-networks 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 and data robustness. As a result of the dimensionality reduction obtained simply from the outputs of the neural-networks clustering algorithms, lower communication costs and energy savings can also be obtained. In this paper we will present two 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 purposefully faulty sensors show the data robustness of these architectures. The proposed neural-networks classifiers have distributed short and long-term memory of the sensory inputs and can function as security alert when unusual sensor inputs are detected.