An edge-stream computing infrastructure for real-time analysis of wearable sensors data

The fast development of IoT in general and wearable smart sensors in particular in the context of wellness and healthcare are demanding for definition of specific infrastructure supporting real time data analysis for anomaly detection, event identification, situation awareness just to mention few. The explosion in the development and adoption of these smart wearable sensors has contributed to the definition of the Internet of Medical Things (IoMT), which is revolutionizing the way healthcare is tackled worldwide. Data produced by wearable sensors continuously grow and could be spread among clinical centers, hospitals, research labs, yielding to a Big Data management problem. In this paper we propose a technological and architectural solution, based on Open Source big data technologies to perform real-time analysis of wearable sensor data streams. The proposed architecture is composed of four distinct layers: a sensing layer, a pre-processing layer (Raspberry Pi), a cluster processing layer (Kafka's broker and Flink's mini-cluster) and a persistence layer (Cassandra database). A performance evaluation of each layer has been carried out by considering CPU and memory usage for accomplishing a simple anomaly detection task using the REALDISP dataset.