A Data Fusion Scheme for Tenet Architecture
Tenet architecture is a two-tier sensor network architecture that provides a model to implement more complex algorithms due to incorporation of less resource-restricted nodes. Stargate-class nodes called masters form the upper tier while resource-restricted nodes named motes compose the lower tier. This paper introduces a data fusion scheme for a tenet architecture based on the correlation coefficients between data set extracted from the motes. Each master selects four sentinels to calculate the direction in which an event has been detected, and then uses this data as a base data to calculate the correlation coefficient for the incoming data. The aggregate output is a result of a weighted sum of the data collected from the N sensors. The weights are calculated based on the correlation coefficients. The aggregated output is compared with a linear means square (LMS) estimator based on variance. The proposed scheme achieves good performance.