Empowering healthcare IoT systems with hierarchical edge-based deep learning

Abstract

Remote health monitoring is a powerful tool to provide preventive care and early intervention for populations-at-risk. Such monitoring systems are becoming available nowadays due to recent advancements in Internet-of-Things (IoT) paradigms, enabling ubiquitous monitoring. These systems require a high level of quality in attributes such as availability and accuracy due to patients critical conditions in the monitoring. Deep learning methods are very promising in such health applications to obtain a satisfactory performance, where a considerable amount of data is available. These methods are perfectly positioned in the cloud servers in a centralized cloud-based IoT system. However, the response time and availability of these systems highly depend on the quality of Internet connection. On the other hand, smart gateway devices are unable to implement deep learning methods (such as training models) due to their limited computational capacities. In our previous work, we proposed a hierarchical computing architecture (HiCH), where both edge and cloud computing resources were efficiently exploited, allocating heavy tasks of a conventional machine learning method to the cloud servers and outsourcing the hypothesis function to the edge. Due to this local decision making, the availability of the system was highly improved. In this paper, we investigate the feasibility of deploying the Convolutional Neural Network (CNN) based classification model as an example of deep learning methods in this architecture. Therefore, the system benefits from the features of the HiCH and the CNN, ensuring a high-level availability and accuracy. We demonstrate a real-time health monitoring for a case study on ECG classifications and evaluate the performance of the system in terms of response time and accuracy.

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

“…Moreover, extension of deep learning models to allow ensembling of results is a non trivial extension as it requires careful balance of accuracy improvement and latency increase to provide the most desired service quality. Furthermore, building on previous works like [2,19,46], HealthFog provides a novel architecture for healthcare computation integrating/harnessing diverse backend frameworks like FogBus [27] and Aneka [28] making it a scalable model.…”

Adaptive deep convolutional neural network‐based secure integration of fog to cloud supported Internet of Things for health monitoring system

“…Azimi et al 16 proposed the hierarchical edge-based deep learning approach to empower the healthcare based on IoT systems. Here the feasibility of the proposed approach was developed using a convolution neural network.…”

“…From Equation (15), the scale parameter and the shift parameter are denoted as > 0 and . Then the Fourier transform of the Levy distribution function is determined in Equation (16).…”

Streaming service provisioning in IoT‐based healthcare: An integrated edge‐cloud perspective

“…Novel paradigm is developed to seek whether any interference‐aware senor‐process scheduling could possibly use under IoT‐based ecosystem 32 . Edge computing is integrated with the deep learning methods for deployment in the hierarchical orientation 33,34 . Good quality air is essential for healthcare, thus pervasive monitoring of PM2.5 particles were investigated for IoT‐based use case 35 .…”