Bloom filter empowered smart storage/access in IoMT [edge-fog-cloud] hierarchy for health-care data ingestion

Today's era of smart technologies has shifted the paradigm of how things work in almost every domain. This change has also affected the utmost need of human beings, that is, sustainability in healthcare. With the help of technologies like the Internet of medical things (IoMT) devices and cloud-based applications, doctors can now monitor and diagnoses their patients remotely. In the data analytics process for health-care data, more precision and challenges are present. Data must be transferred from a lower layer to final storage (cloud-based) during data ingestion so that subsequent operations such as mining, accessing, and streaming can be performed on these data. This article addresses some of the important issues to maintain sustainability in healthcare like authentication, smart scheduling of devices, the removal of redundant data on the final layer, and improving access times for stored data. Bloom filter (a probabilistic data structure)-based solutions are proposed at different layers of IoMT (edge-fog-cloud). The comparative analysis of state-of-art techniques with the proposed has shown significant improvement over various metrics. The proposed framework has been evaluated experimentally on real health-care datasets.