An Adaptive Modular Approach to the Mining of Sensor Network Data

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Provided by: International Journal of Computer Science & Engineering Technology (IJCSET)
Topic: Big Data
Format: PDF
In this paper, the authors propose a two-layer modular architecture to adaptively perform data mining tasks in large sensor networks. The architecture consists in a lower layer which performs data aggregation in a modular fashion and in an upper layer which employs an adaptive local learning technique to extract a prediction model from the aggregated information. The rationale of the approach is that a modular aggregation of sensor data can serve jointly two purposes: the organization of sensors in clusters, then reducing the communication effort, the dimensionality reduction of the data mining task, then improving the accuracy of the sensing task.
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