Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
Identifier based graph neuron: a light weight event classification scheme for WSN
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Optimization of embedded fuzzy rule-based systems in wireless sensor network nodes
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
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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.