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A Robust Model of Multi-Sensor Data Fusion Applied in Wireless Sensor Networks for Fire Detection

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This paper presents a robust model of multi-sensor data fusion applied in wireless sensor network system for fire detection. These sensors are scattered in the forest to protect the environment against fires. The proposed model is composed by two levels of fusion to improve the decision based on heterogeneous information resources. In the first level, signals shipped from each sensors category are combined, using the Central Limit Theorem; primary decisions, detection and false alarm probabilities, are the results of this step. These decisions and probabilities will be exploited in the fusion centre by Chair-Varshney Rule to achieve a global decision. The results indicate that the proposed model is robust and efficient in terms of stability and decision making.
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Wireless Sensor Networks; Data Fusion; Detection Probability; False Alarm Probability; Central Limit Theorem; Chair-Varshney Rule; Global Decision

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