A GS-MPSO-WKNN method for missing data imputation in wireless sensor networks monitoring manufacturing conditions

Wireless sensor networks have been utilized to monitor complex manufacturing processes but missing data from sensors cause problems for data-based applications. In this paper, a missing data estimation algorithm, GS-MPSO-WKNN (Gaussian mutation and simulated annealing-based memetic particle swarm optimization for weighted K nearest neighbours), based on a weighted K nearest neighbour (WKNN) and memetic computing is proposed. The GS-MPSO developed in our previous work is adopted in order to adjust the feature weights for the WKNN. A real world data set from a semiconductor manufacturing process is used to evaluate GS-MPSO-WKNN. Experimental results show that GS-MPSO-WKNN can reach a higher estimation accuracy, and GS-MPSO-WKNN is also robust to a high missing data ratio.

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