Anomaly Detection in Medical Wireless Sensor Networks using Machine Learning Algorithms

Wireless sensor networks suffer from a wide range of faults and anomalies which hinder their smooth working. These faults are even more significant for medical wireless sensor networks, which simply cannot afford such inconsistencies. To combat this issue, various fault detection mechanisms have been developed. We tried enhancing the performance of one such mechanism, and our findings are presented in this paper. Using machine learning algorithms, we will show through our experiments on real medical datasets that our approach gives more accurate results than other existing fault detection mechanisms. This research will be critical in detecting sensor faults quickly, accurately and with a low false alarm ratio.