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Cloud computing provides resources over the Internet and allows a plethora ofapplications to be deployed to provide services for different industries. Themajor bottleneck being faced currently in these cloud frameworks is theirlimited scalability and hence inability to cater to the requirements ofcentralized Internet of Things (IoT) based compute environments. The mainreason for this is that latency-sensitive applications like health monitoringand surveillance systems now require computation over large amounts of data(Big Data) transferred to centralized database and from database to cloud datacenters which leads to drop in performance of such systems. The new paradigmsof fog and edge computing provide innovative solutions by bringing resourcescloser to the user and provide low latency and energy-efficient solutions fordata processing compared to cloud domains. Still, the current fog models havemany limitations and focus from a limited perspective on either accuracy ofresults or reduced response time but not both. We proposed a novel frameworkcalled HealthFog for integrating ensemble deep learning in Edge computingdevices and deployed it for a real-life application of automatic Heart Diseaseanalysis. HealthFog delivers healthcare as a fog service using IoT devices andefficiently manages the data of heart patients, which comes as user requests.Fog-enabled cloud framework, FogBus is used to deploy and test the performanceof the proposed model in terms of power consumption, network bandwidth,latency, jitter, accuracy and execution time. HealthFog is configurable tovarious operation modes that provide the best Quality of Service or predictionaccuracy, as required, in diverse fog computation scenarios and for differentuser requirements.

future generation computer systems

HealthFog: An Ensemble Deep Learning based Smart Healthcare System for  Automatic Diagnosis of Heart Diseases in Integrated IoT and Fog Computing  Environments