Decision Fusion Supported by Correlated Auxiliary Data in Wireless Sensor Networks

Leakage monitoring is different from sudden incident monitoring because most of the leakage cases involve a slow process that lasts for a long time. During this case monitoring, sensors suffer long exposure to erosion and may lead to errors in the measurement. An approach is proposed to make use of a soft-decision fusion approach according to the Neyman-Pearson criterion to accumulate auxiliary data from multiple sensors. The proposed method optimizes the soft-function and adjusts its range of sensors, which provide auxiliary data to improve the fusion center confidence for making a global decision. The new method encompasses the collection of useful data and weights and combines them according to the corresponding confidence level to make a global decision. In the simulation case of Rayleigh-distributed observations of leakage monitoring, it is proved that the proposed method has a good performance.