Reliable vital sign collection in medical Wireless Sensor Networks

The aim of this paper is to propose a new approach for the detection and isolation of faulty measurements in medical wireless sensors networks. The proposed approach is based on the combination of statistical model and machine learning algorithm. We begin by collecting physiological data and then we cluster the data collected during the first few minutes using the Gaussian mixture decomposition. We use the resulted labeled data as the input for the Ant Colony algorithm to derive classification rules, which are used to detect abnormal values. Finally, we exploit the spatial correlation between monitored attributes to differentiate between faulty sensor readings and emergency situations. Our experimental results on real patient dataset show that our proposed approach achieves a high level of detection accuracy, which in turn proves the effectiveness of this approach in enhancing the reliability of medical wireless sensors networks.