Authors: A. Kulakov and D. Davcev
Affilation: Ss. Cyril and Methodius University, Macedonia
Pages: 427 - 430
Keywords: sensor networks, neural networks, intellignet data aggregation, dimensionality reduction, data robustness, self-organization of input data, auto-classification of sensor readings
Most of the current in-network data processing algorithms are modified regression techniques like multidimensional data series analysis. In our opinion, some of the algorithms well developed within the artificial neural-networks tradition, for over 40 years, 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. 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 three 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 Smart-It motes, each equipped with several sensors of different types. Results from simulations of purposefully faulty sensors show the data robustness of these architectures.