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%).
Highlights
In recent times, smart healthcare becomes an emerging application of the internet of things (IoT).A smart healthcare system consists of wearable sensors used to monitor the specific health status of the users or patients [1,2]
We present the FaMOORA algorithm that works upon three major criteria, such as similarity score (Sim_Score), inter arrival time (IA_Time) and environmental status (En_Sta)
We propose a novel tri-foghealth system for the smart healthcare system
Summary
A smart healthcare system consists of wearable sensors used to monitor the specific health status of the users or patients [1,2]. Wearable technology has become a vital part of remote patient monitoring and for user health monitoring regularly. The introduction of wearable devices minimizes the frequent involvement of doctors in health monitoring. It assists in the early detection of diseases, drug research, smart hospital development, and safety provisioning [3,4]. A cognitive dynamics (CDS) concept is presented for smart healthcare and disease diagnosis, along with decision tree-based classification [40]. A structured Gaussian process is proposed for patient-specific physiological monitoring based on its health trajectory [41]. This work finds a single vital sign for determining the health level of the patients
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