Efficient Handling of Medical Data Classification in Cloud-Edge Network using Optimization Algorithm

Abstract

Nowadays, a cloud-edge computing framework with IoT offers different medicinal facilities by classifying a massive amount of patients’ health data through a Deep Neural Network. But, how to optimize task scheduling while carrying multiple tasks from multiple edge devices in realtime was still challenging. This article introduces a cooperative cloud-edge computing structure to effectively perform the fuzzy DNN classification into the edge system and handle the computationally complicated tasks of DNNs. First, the edge servers are constructed with fuzzy DNNs and cooperate with the cloud to create a cooperative cloud-edge computing paradigm. Then, an adaptive deployment method is developed using a Lion Optimization Algorithm, which supports the cloud to decide which task will be executed at the edge devices. Therefore, the study of fuzzy DNN using health data is performed for forecasting and diagnosing various diseases. Finally, the simulation outcomes reveal that the LOA achieved 37.8Jin energy use and 17.8ms latency while using 25 edge devices. Also, the fuzzy DNN achieved 85.8% accuracy for classifying the medical data and diagnosing them in the earlier stage. It concludes that LOA and fuzzy DNN are more efficient than classical optimization and classification for healthcare applications using the cloud-edge computing paradigm.