Predicting indoor temperature variation through sensor data pattern analysis

This paper presents an approach to address the problem of reliably cooling large indoor spaces. The proposed system is based on a wireless sensor network that implements a FP Growth algorithm used to predict the exceeded temperature situations. Both centralized and distributed implementations are studied. Experiments prove the efficiency of the system and depict the computational power and memory demands of it.

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