Intelligent Fog-Enabled Smart Healthcare System for Wearable Physiological Parameter Detection

Abstract

Wearable technology plays a key role in smart healthcare applications. Detection and analysis of the physiological data from wearable devices is an essential process in smart healthcare. Physiological data analysis is performed in fog computing to abridge the excess latency introduced by cloud computing. However, the latency for the emergency health status and overloading in fog environment becomes key challenges for smart healthcare. This paper resolves these problems by presenting a novel tri-fog health architecture for physiological parameter detection. The overall system is built upon three layers as wearable layer, intelligent fog layer, and cloud layer. In the first layer, data from the wearable of patients are subjected to fault detection at personal data assistant (PDA). To eliminate fault data, we present the rapid kernel principal component analysis (RK-PCA) algorithm. Then, the faultless data is validated, whether it is duplicate or not, by the data on-looker node in the second layer. To remove data redundancy, we propose a new fuzzy assisted objective optimization by ratio analysis (FaMOORA) algorithm. To timely predict the user’s health status, we enable the two-level health hidden Markov model (2L-2HMM) that finds the user’s health status from temporal variations in data collected from wearable devices. Finally, the user’s health status is detected in the fog layer with the assist of a hybrid machine learning algorithm, namely SpikQ-Net, based on the three major categories of attributes such as behavioral, biomedical, and environment. Upon the user’s health status, the immediate action is taken by both cloud and fog layers. To ensure lower response time and timely service, we also present an optimal health off procedure with the aid of the multi-objective spotted hyena optimization (MoSHO) algorithm. The health off method allows offloading between overloaded and underloaded fog nodes. The proposed tri-fog health model is validated by a thorough simulation performed in the iFogSim tool. It shows better achievements in latency (reduced up to 3 ms), execution time (reduced up to 1.7 ms), detection accuracy (improved up to 97%), and system stability (improved up to 96%).

Optimal Resource Allocation in Fog Computing for Healthcare Applications

“…However, they did not consider the dynamic resource allocation and load balancing which raises the problem of overhead delays and wastage of resources. Kumari et al [6], Ijaz et al [8], Awaisi et al [9], Tuli et al [10] present fog architecture for patient-oriented real-time healthcare applications for data collection, processing and transmission. The authors have highlighted existing issues in healthcare system, such as faulty data and data duplication, data integration, user authentication, data security and privacy.…”

“…To fulfill the requirements of diverse and dynamic data arriving from end-users to resource constrained devices causes overhead delays and wastage of resources. The resource utilization in fog computing is a challenging issue that must be addressed [6][7][8][9][10][11]. Proper resource allocation, nodes sorting according to task requirements, and load balancing are critical issues, affecting the fast and timely response desirable for several real-time applications.…”

“…Proper resource allocation, nodes sorting according to task requirements, and load balancing are critical issues, affecting the fast and timely response desirable for several real-time applications. For instance, in IoHT healthcare critical applications, patients' information and appointment are of great importance to save their lives [4][5][6][7][8][9]. Hence, considering the criticality of healthcare applications, there is a need to develop an efficient resource utilization of fog resources for diverse applications.…”

An Energy-Efficient and Secure Data Inference Framework for Internet of Health Things: A Pilot Study

“…Therefore, this model is not appropriate for mHealth data. Ljaz et al [ 13 ] solved the latency and overloading problems in smart healthcare applications by developing a tri-fog health architecture, which includes three layers, consisting of a wearable layer, intelligent fog layer, and cloud layer. Pazienza et al [ 14 ] explored the machine learning technique to find the most suitable machine learning algorithm for predicting the clinical risk classes of patients.…”

Modeling of a Generic Edge Computing Application Design

“…Fog deployments have been implemented in many fields where processing a vast amount of data with strict time constraints renders cloud computing unfit [65]. One scenario may be the analysis of physiological data from wearable devices (the concept of smart health), where such devices obtain relevant health-related data from a patient, and in turn, pass them on to fog servers to undertake the processing and respond within a restricted time interval, whilst having cloud servers for backup purposes [66].…”