Some of the algorithms developed within the artificial neural-networks tradition can be easily adopted to wireless sensor network platforms and in the same time they can meet the requirements for sensor networks like: simple parallel distributed computation, distributed storage, data robustness and auto-classification of sensor readings. Dimensionality reduction, obtained simply from the outputs of the neural-networks clustering algorithms, leads to lower communication costs and energy savings.Two different data aggregation architectures will be presented, with algorithms which use wavelets for initial data-processing of the sensory inputs and artificial neural-networks which use unsupervised learning methods for categorization of the sensory inputs. They are analyzed on a data obtained from a set of several motes, equipped with several sensors each. Results from deliberately simulated malfunctioning sensors show the data robustness of these architectures.
This paper places mobile learning in the space of the existing learning methods. The three main groups of challenges -technological, development and pedagogical -in the transition from e-learning to m-learning are defined. The influence they make over the main participants in the m-learning process -developers, educators and students is also examined.
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