FogMed: A Fog-Based Framework for Disease Prognosis Based Medical Sensor Data Streams

Recently, an increasing number of works start investigating the combination of fog computing and electronic health (ehealth) applications. However,there are still numerous unresolved issues worth to be explored. For instance,there is a lack of investigation on the disease prediction in fog environmentand only limited studies show, how the Quality of Service (QoS) levels of fog services and the data stream mining techniques influence each other to improve thedisease prediction performance (e.g., accuracy and time efficiency). To addressthese issues, we propose a fog-based framework for disease prediction basedon Medical sensor data streams, named FogMed. This framework aims toimprove the disease prediction accuracy by achieving two objectives: QoS guarantee of fog services and anomaly prediction of Medical data streams. We build avirtual FogMed environment and conduct comprehensive experiments on the public ECG dataset to validate the performance of FogMed. The experiment resultsshow that it performs better than the cloud computing model for processing taskswith different complexities in terms of time efficiency.