Optimal model detection in distributed sensor networks using genetic-fuzzy clustering

This paper presents an accurate method to determine the environment model for decentralized detection in sensor networks. We develop a clustering algorithm to classify sensor data and to achieve this model. Then we further enhance the performance of the method in case of noisy sensors, non-identical observations and unreliable communication links. The enhanced algorithm performs an unsupervised hierarchical grouping of the model, threshold detection and outlier removal. The proposed algorithm is verified through simulation.