Proactive Anomaly Detection Model for eHealth-Enabled Data in Next Generation Cellular Networks

Abstract

Internet of things (IoT) is an ever-growing technological paradigm that is expected to boost the development of a plethora of services and applications like eHealth services. The massive amount of data generated by eHealth applications will be handled by the cellular architecture. Due to the additional eHealth data, cellular networks may suffer from some anomalies which need intelligent and autonomic mechanisms to be avoided. Network operators must integrate to their architecture pro-active tools able to detect and signal these anomalous patterns and then mitigate the issue of overloaded base-stations. We address in this paper the issue of eHealth services by analyzing at first their impact on cellular networks. We propose also an on-line and efficient anomaly detection technique for eHealth data based on support vector regression (SVR). Moreover, we validate our model with a real dataset of cellular call detail records (CDR) combined with semi-synthetic eHealth dataset. A realistic testbed is provided on the context of a Marathon event where mobile users are running eHealth applications.