Abstract

The general problem of data management in Wireless Sensor Networks (WSNs) is to provide efficient aggregation of different sensor’s data taking into account the problems of the limited energy of the nodes and their unpredictable failures. Generally, this is solved by reducing the communication among nodes. In order to have an efficient data aggregation performance, a preprocessing is needed which would reduce the amount of data being sent over the communication channels. As an outcome of this research, we propose two similar architectures for data aggregation of sound and video signals. These classification architectures have the same core consisted of a modified FuzzyART neural network and a modified SEQUITUR algorithm used previously only for analysis of symbolic sequences. The proposed architectures have been tested in a prototype implementation using Pocket PCs having microphones and cameras as sensors. Furthermore, in the pursuit for efficient data management methods in wireless sensor networks, this research has tackled the problem of pattern recognition over time in general. In our belief, the central parts of the proposed architectures can represent a core of a general learning system for any signal patterns that spread over time. This general learning system can be viewed as an extension to all Artificial Neural Networks proposed so far, which only dealt with static patterns, and also it can be viewed as an alternative to the Recurrent Neural Networks. 1

Citations