Intelligent Wireless Sensor Networks Using FuzzyART Neural-Networks

An adaptation of one popular model of neural-networks algorithm (ART model) in the field of wireless sensor networks is demonstrated in this paper. The important advantages of the ART class algorithms such as simple parallel distributed computation, distributed storage, data robustness and auto-classification of sensor readings are confirmed within the proposed architecture consisting of one clusterhead which collects only classified input data from the other units. This architecture provides a high dimensionality reduction and additional communication savings, since only identification numbers of the classified input data are passed to the clusterhead instead of the whole input samples. We have adapted and implemented the FuzzyART neural-network algorithm and used it for initial clustering of the sensor data as a sort of pattern recognition. This adaptation was made specifically for MicaZ sensor motes by solving mainly problems concerning the small memory capacity of the motes. At the final clusterhead -server, the data are stored in a database and the results of the data processing are continuously presented in a classification graph.