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

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 neural-networks 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.