IoHT-enabled gliomas disease management using fog Computing computing for sustainable societies

Abstract The proliferation of sensor-based applications in healthcare has given rise to Internet of Health Things (IoHT) that improves patient safety, staff morale, and operational efficiency. Edge-fog computing has seen significant development in recent years and supports the association of various intelligent things with sensors for establishing smooth data transfer. However, it becomes challenging for edge-fog computing to tackle diverse IoHT settings such as efficient disease management, emergency response management, etc. The key limitation of existing architectures is the restricted scalability and inability to meet the demands of hierarchical computing environments for IoHT. This is because latency-sensitive applications often require large quantities of data to be measured and transferred to the data centers, which causes delay and reduced output. This research proposes a novel edge-fog computing framework for the convergence of machine learning ensemble with edge-fog computing. The proposed architecture delivers healthcare as a fog system that handles data from different sources to manage the diseases effectively. The proposed framework is used for the real-life implementation and automatic detection of gliomas diseases. Glioma is a kind of tumor, which ensues in the spinal cord and a portion of the brain. Glioma instigates in the glial cells that surround the nerve cells. The proposed edge-fog framework efficiently manages the real-time data related to gliomas. This framework is configured for specific operating modes including diverse edge-fog scenarios, different user requirements, quality of service, precision, and predictive accuracy. The proposed framework is evaluated using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The performance of the proposed model is evaluated in terms of power consumption, latency, accuracy, and execution time, respectively.