A novel R-PCA based multivariate fault-tolerant data aggregation algorithm in WSNs
Tianqi Yu, Xianbin Wang, Abdallah A. Shami · 2016
Wireless sensor networks have already been pervasively utilized due to the rapid deployment of information and communication technology (ICT) in many industrial applications, which generate massive amount of sensor data. This development has brought several technical challenges in sensor data processing, e.g., data fault and data redundancy. Principal component analysis (PCA) has been used recently to process the massive but correlated sensor data. However, the conventional PCA method is difficult to be adapted in following the dynamic conditions of wireless sensor networks. In this paper, recursive principal component analysis (R-PCA) method is exploited to progressively update the transformation basis for extracting principal components. Furthermore, a novel R-PCA based algorithm is proposed to address data fault and data redundancy problems. Different from conventional PCA-based algorithms, the proposed algorithm is cluster-based so that the network efficiency can be further improved. Simulations based on a practical dataset have been conducted to evaluate the performance of algorithms. Simulation results show that the proposed algorithm improves the fault detection accuracy by about 20% and reduces the data restoration error by about 28%.