Autonomous Framework for Sensor Network Quality Annotation: Maximum Probability Clustering Approach

In this paper an autonomous feature clustering framework has been proposed for performance and reliability evaluation of an environmental sensor network. Environmental time series were statistically preprocessed to extract multiple semantic features. A novel hybrid clustering framework was designed based on Principal Component Analysis (PCA), Guided Self-Organizing Map (G-SOM), and Fuzzy-CMeans (FCM) to cluster the historical multi-feature space into probabilistic state classes. Finally a dynamic performance annotation mechanism was developed based on Maximum (Bayesian) Probability Rule (MPR) to quantify the performance of an individual sensor node and network. Based on the results from this framework, a “data quality knowledge map” was visualized to demonstrate the effectiveness of this framework.